As a veteran architect familiar with the WTC Building designs, Richard Gage had realized on that September morning as he had watched on television the awful events happen that those two super-structure WTC Towers, 1 and 2, had been designed and built to withstand total collapse caused by a large colliding jet aircraft, and that the aluminum fuselages and engines of those two jet aircraft were mere putty in the grasp of those thick steel girded buildings, and that the Towers completely destroyed the aircraft instead of the exact opposite happening. To the many American people without scientific understanding, who saw on television those two jets crash into the Twin Towers, their proper perception of what actually happened was dramatically skewed by the collective voices of the television media pundits following the government script vehemently exclaiming, “those jet airplanes have caused those Towers to collapse!” The duping of most American minds on that fateful day had been carefully plotted and planned several years earlier, and executed with strategic conspiratorial precision. What actually happened during the subsequent bombing of the Pentagon was also extremely skewed in the minds of the American public by a planned federal orchestration of something very different from the federal version of a hijacked Boeing 767 airliner filled with passengers flown into the Pentagon wall by a Muslim hijacker who, according to a certified flight instructor, couldn’t even properly fly a small Cessna aircraft.

The American public remains an extremely diverse and heterogeneous amalgamation of people in terms of literacy, competency to understand current events, and general education. Compared to the population of the fledgling American republic in 1788 (1.5 million new Americans), which had an electorate literacy rate of 90 percent without the benefit of a public education system, the current American electorate literacy rate is staggeringly less than 85 percent. Richard Gage understood the social and political forces that were working against him as he tried diligently to create an Internet means for educating that 40 percent of the electorate, the adults who are working daily on an eighth-to-ninth grade level of reading comprehension competency, about the real scientific facts underscoring the happenings of 9/11. For who among the adults of the 325 million U.S. population would want to believe and admit that their federal government intentionally and deliberately conspired to murder over 3,000 innocent people on 9/11 in order get the Patriot Act (actually written secretly in 1993) passed by Congress and signed by the U.S. President, and a state of war declared against two foreign nation-states, Afghanistan and Iraq, in the name of an elusive fictional adversary, Islamic terror? It’s like trying to convince the family of a man, a husband and a father of four children, that he is a mass murderer. That is a very difficult thing to do unless there are forensic facts to back-up the allegations.

Yet, Gage came to realize that the proverbial smoking-gun of federal duplicity was not what most of the American people had seen, and heard, on television on 9/11, but, rather, what they hadn’t seen, or known about, which happened at around 5:15 on the afternoon of 9/11, when the 47-story Solomon Building, WTC Building 7, collapsed totally and completely into its tracks in a manner identical to remote demolition, just like what had happened to the Twin Towers only hours earlier; but Building 7 had not been affected to any extent by the collapse of the Twin Towers. Amazingly, almost 95 percent of the American electorate had not realized that WTC Building 7 was the third massive building to collapse on 9/11 until Gage and his organization had publicized that relevantly salient fact on the Internet and until prolific writers such as Dr. David R. Griffin had written about it in a series of his books about 9/11. Of course, the federal government had hoped that the collapse of Building 7 would have gone relatively unnoticed; but, truly, when a massive 47-story high-rise building collapsed in downtown New York City for no apparent reason in a manner identical to controlled demolition, questions were surely asked by the public that the National Institute for Standards and Technology (NIST), the federal agency assigned as the apologist for the collapse of Building 7, was unable answer with candor and veracity.

Richard Gage discovered that the issues the federal government’s 9/11 Commission had addressed and answered falsely in their tome “The Report of the 9/11 Commission” about the crashes of the jet aircraft into the WTC Twin Towers, their subsequent collapse, and the Pentagon bombing were 100 percent more than those they had addressed and answered correctly. In other words, the production of the 9/11 Commission was a veritable hoax. The collapse of WTC Building 7 wasn’t even mentioned within the report’s pages, as though the Commission was deliberately attempting to create the perception that it wasn’t in any way connected to the destruction of the Twin Towers.

After 2006, Gage and his AE911Truth associates, as a well-organized team, continued to press the federal government for an explanation of the collapse of WTC Building 7 until the powers-that-be in DC ordered NIST to produce some “plausible” reason to supposedly eliminate the ensuing controversy. So, suddenly the headlines appeared in mainstream newspapers, on television, and the Internet that two controlled fires on two floors of the 47-story building had caused the entire structure to collapse in a manner identical to controlled demolition. No explanation was provided, however, by NIST to explain the residue presence of nano-thermite in the air around the collapsed building, the same military-grade explosive that was found in abundance lingering for several days in the massive amount of debris and dust in the air as far as ten-blocks away from the collapsed remains of the WTC Twin Towers, called Ground-Zero. NIST was also silent about the reason for the mandated evacuation of Building 7 an hour before men with hard-hats were shooing people away from the perimeter of the building on the afternoon of 9/11, saying that the building was going to be “pulled,” a word used to mean “remotely detonated.” The most telling statement from NIST after its official flat denial that “explosives” had been used to bring-down the massive Solomon Building was that “an inspection for the remains of explosives” had not been made in the building’s collapsed debris by NIST scientists.” Several physicists and engineers had independently done extensive testing for explosive residue in the remains of Building 7 and had discovered great amounts of nano-thermite residue in the debris and dust circulating around those remains. Samples of those remains had come into the hands of AE911Truth in 2006 and other more delicate testing had been done by the organization’s scientists to confirm the original findings. Having realized that they, the NIST spokespeople, had been sorely exposed by the work of AE911Truth, NIST and the federal apologists suddenly became very silent about the conclusive findings published by Gage and2 his associates on the Internet.

Several prominent physicists associated with the original 9/11Truth movement, which was formally initiated in 2002, endeavored to expound the scientific findings on television through interviews they had with such media pundits as Bill O’Reilly. Such statements by highly respected physical scientists contradicting the official government version of 9/11 began in 2002, when these numerous professional men and women of science responded to the government’s version of 9/11 with deriding disbelief and utter skepticism. Though I have been conservatively minded most of my life and had some respect for O’Reilly as a voice of conservatism, I was appalled and lost all respect for him when I saw and heard on television the rude and impudent man blindly defending the neoconservative 9/11 position of the federal government by demeaning and ridiculing professional physicists who were trying to explain in simple scientific terms why a great deal of nano-thermite residue was found at, and around, the WTC ground-zero. Bombastic O’Reilly wouldn’t let them get a word in edge-wise as the scientists he interviewed attempted to explain the infamous “secondary explosions (an integral part of controlled demolition)” heard by over 50 firemen and other first-responders before the collapses of the WTC Twin Towers. They also tried to explain the great pools of molten steel discovered by first-responders beneath collapsed girders, beams, and assorted debris of the collapsed towers, but O’Reilly and company didn’t give them an opportunity.

One particularly astute physicist later went on record to assert that “the laws of physics were politically suspended and revoked on 9/11″ if the government version of 9/11 is true.” This was Dr. Stephen E. Jones, a BYU professor of physics. Dr. Jones was so impassioned and provoked by what he saw and heard on television on 9/11 that he immediately left his job in Provo, Utah and traveled to NYC, where he conducted his own experiments confirming conclusively that a great residue of nano-thermite was present in the dust and debris emanating from the collapsed towers. Because Dr. Jones later went on record to claim that the jet aircraft fires in the WTC Twin Towers were not what caused them to collapse, but, rather, controlled demolition using nano-thermite explosive, he lost his professorial physics position at Brigham Young University. In 2006, Dr. Jones was first introduced to Richard Gage and AE911Truth and became one of the organization’s supporting members. It seems that every professional physical scientist, engineer, and architect who meets and associates with Richard becomes enthralled with his candor and veracity, and an instant subscriber to the AE911Truth agenda.

What happened on that awful day, September 11, 2001, boils-down to a very simple conclusion as a matter of relevant material and scientific facts. You see, physical science is a collection of noble universal truths that can’t be altered in any way by political conjecture, conspiracy, or delusion. Just like forensics and DNA have turned crime-solving from a process of mere deduction and elimination to a hard-and-fast science, both physical and biochemical, every aspect of what really occurred on 9/11 may be explained scientifically. If the collision of those jet aircraft into the WTC Twin Towers, and their ensuing fires, burning at just under 800 degrees, could “not” have scientifically caused the super-steel WTC Twin Towers to collapse, and if loud secondary explosions were clearly heard emanating from Towers 1 and 2 before their collapse, and if great pools of molten steel were found beneath the rubble of the collapsed towers, it is scientifically obvious that something, besides kerosene jet fuel, with the chemical capacity of creating temperatures above 2,000 degrees Fahrenheit, was used as an explosive to cause those huge steel-lattice beams and girders, designed and engineered to withstand the direct impact of a Boeing 747, to melt and collapse. And since an abundant residue of nano-thermite was found in the dust and debris at, and around, the area within ten blocks of ground-zero, a scientific forensic conclusion can be drawn that the Twin Towers were made to collapse through the contrived use of the military-grade nano-thermite planted in the two Towers over a period-of-time.

Furthermore, by simple deduction, if the WTC Twin Towers were made to collapse through a federally contrived controlled demolition, killing over 3,000 human beings, then the Pentagon bombing was also contrived by those same conspirators. For, surely, ten tons of steel-titanium jet engine components of a Boeing 767 commercial airliner cannot be vaporized in a crash of the huge jet, traveling at 450 mph into that Pentagon wall. Most assuredly, a great deal of jet aircraft wreckage would be left in the wake such a crash; but no such crash remains were found on the undisturbed Pentagon lawns bordering the bombed wall. In fact, the first CNN news crew that reported from the Pentagon site broadcasted the news that “it appears that a Boeing 767 did not crash into the Pentagon wall as reported, because, as you can now see, there are no aircraft remains here at the Pentagon.” That report lasted all of 48 seconds before it was suddenly cut by the network and
never again replayed. I wonder why? Perhaps the federal conspirators hadn’t planned this unexpected exposure. Great appreciation is certainly in order for the audio/video DVRs in the homes of average Americans who recorded the startling 48 second CNN broadcast, and for those concerned and alarmed Americans who preserved those broadcasted facts by loading that video segment into YouTube format on the Internet. I have elaborated on the real scientific facts surrounding the 9/11 Pentagon bombing in the Ezine Article entitled “The Burlingame End-Game Murders.”

In the mathematical science of physics, algorithms have been created by physicists and mathematicians to explain what did and did not occur on 9/11 according to existing scientific fact, and one can say with total assurance, after examining the results of those algorithms, that it was impossible for the WTC Twin Towers and WTC Building 7 to have collapsed by any other means than controlled demolition. I don’t know if it is exactly true, but I’ve been told by others closely associated with Richard Gage and AE911Truth that Gage and others closely associated with AE911Truth have received death threats anonymously by telephone and in letters written anonymously, warning them to stop their 9/11 investigations; but the old expression, “the truth will prevail” is one of the truisms of forensic mathematics, and Richard, like a tenacious and bodacious bulldog will keep on pursuing the truth despite the risks. That was why the 9/11 crime scene was so deliberately tainted by the federal government in the weeks following the debacle by the removal of all of the primary evidentiary remains from the collapsed Twin Towers. Instead of allowing the NYPD and New York State crime scene investigators to do their jobs of determining scientifically how and why the Twin Towers collapsed, the feds swarmed over the crime scene closing it off to city and state investigators. Then many lies were told to the American public, via the media, by federal spokespeople denying the sworn statements of the first-responders about the secondary explosions they heard, the deep pools of molten steel they encountered, and the other evidences of controlled demolition. Much of this vital information has since been carefully accumulated, correlated, and filed on the AE911Truth website for public perusal, and nearly all of the curious people around the USA who visit that website come away convinced that 9/11 was a complex federal conspiracy. All it takes, as Richard Gage has repeatedly said, is a willingness to look at the prevailing scientific and forensic facts and the common sense to accept the conclusive findings.

What does Richard Gage and his associates hope to accomplish through the investigatory efforts of AE911Truth? They are faithfully hoping that the States will soon collectively initiate their own criminal murder investigation into 9/11 and the murders of over 3,000 innocent human beings, much like a constitutional convention of the States is allowed and sanctioned by the U.S. Constitution to propose and submit new constitutional amendments to the States for ratification. Seeking another federal investigation of 9/11, tantamount to the austere mockery of justice that occurred with the politically orchestrated 9/11 Commission, would be like an investigation of who ate cock-robin in the henhouse conducted by a tribunal of hungry guilty federal foxes. Instead of politicians running the show during such an investigation, chief law enforcement officers such the Texas Rangers and other imminent State criminal investigators should be in charge of determining the suspects and the relevant and material evidence, and which of it should be presented to a grand jury, presided over by a selected State supreme court chief justice, for indictment, trial, and sentencing of those federal officers who were involved in the 9/11 conspiracy. Oh, there would be an assortment of federal hurtles and impediments to the formation and operation of such an State investigation, perhaps even a federal court denial of such a State action; but, according to the 10th Amendment of the Bill of Rights, the States have much more power than the federal government, and it is about time that they, again, assert that constitutional power in order to the set the record straight and convict and punish the guilty.

The theories adopted in guiding this paper therefore are observed in how motivation can be employed in arousing learners’ interest in science subjects. The theories include motivation as propounded by prominent behaviorist Abraham Maslow, learning theory by prominent constructivist paradigm including Piaget and Vygotsky and the theory of social cognition by its prominent proponent Albert Bandura.

As Maslow (1954) says, “If we are interested in what actually motivates us and not what has or will, or might motivate us, then a satisfied need is not a motivator.” According to him and to other various theories, motivation may be rooted in the basic need to minimize physical pain and maximize pleasure, or it may include specific needs such as eating and resting, or a desired object, hobby, goal, state of being, ideal, or it may be attributed to less-apparent reasons such as altruism, morality, or avoiding mortality. Motivation is of particular interest to Educational psychologists because of the crucial role it plays in student learning. However, the specific kind of motivation that is studied in the specialized setting of education differs qualitatively from the more general forms of motivation studied by psychologists in other fields. Motivation in education can have several effects on how students learn and how they behave towards subject matter as for science subjects in our case. It can direct behavior toward particular goals; Lead to increased effort and energy; Increase initiation of, and persistence in, activities; Enhance cognitive processing; Determine what consequences are reinforcing and; Lead to improved performance. Because students are not always internally motivated, they sometimes need situated motivation, which is found in environmental conditions that the teacher creates.

There are two kinds of motivation: firstly, intrinsic motivation which occurs when people are internally motivated to do something because it either brings them pleasure, they think it is important, or they feel that what they are learning is significant, and secondly extrinsic motivation which comes into play when a student is compelled to do something or act a certain way because of factors external to him or her like money or good grades (Wikipedia, 2008). Young people can be motivated to perform science subjects as pleasure when they are supplied with quality, enough materials and sufficient facilitating situation through competition, science clubs, and any other situations where awards and prizes are provided for best achievers. Externally successfully scientists and best students in science subjects can be invited in science celebrations and exhibitions to demonstrate their achievements.

There are cognitive views of motivation by constructivists which stress that human behavior is influenced by the way people think about themselves and their environment. The direction that behavior takes can be explained by four influences which include; the inherent need to construct an organized and logically consistent knowledge base; one’s expectations for successfully completing a task; the factors that one believes account for success and failure; and one’s beliefs about the nature of cognitive ability (Biehler/Snowman, 1997). The impact of cognitive development view is based on Jean Piaget’s principles of equilibration, assimilation, accommodation, and schema formation. Piaget proposes that children possess an inherent desire to maintain a sense of organization and balance in their conception of the world (equilibration). A sense of equilibration may be experienced if a child assimilates a new experience by relating it to an existing scheme, or the child may accommodate by modifying an existing scheme if the new experience is too different. In our case then love of science can be build to young people since their childhood through directing and provision of simpler experiments and observations on various matters and organisms.

In addition, individuals will repeatedly use new schemes because of an inherent desire to master their environment. This explains why young children can, with no loss of enthusiasm, sing the same song, tell the same story, and play the same game over and over and why they repeatedly open and shut doors to rooms and cupboards with no seeming purpose. It also explains why older children take great delight in collecting and organizing almost everything they can get their hands on and why adolescents who have begun to attain formal operational thinking will argue incessantly about all the unfairness in the world and how it can be eliminated (Stipek, 1993). This allows the room for these habits to be turned into science learning and observation interests.

Social cognition theory proposes reciprocal determination as a primary factor in both learning and motivation. In this view, the environment, an individual’s behavior, and the individual’s characteristics (e.g., knowledge, emotions, and cognitive development) both influence and are influenced by each other two components. Bandura (1986, 1997) highlights self-efficacy (the belief that a particular action say for science [as our case goals], is possible and that the individual can accomplish it) and self-regulation (the establishment of goals, the development of a plan to attain those goals, the commitment to implement that plan, the actual implementation of the plan, and subsequent actions of reflection and modification or redirection.

EDUCATION POLICY IMPLEMENTATION
The first strategy is to deal with the policy effective implementation. Tanzania education policy (Education and Training Policy – ETP) highlights on: Access that encompass participation, gender and equity issues; Quality in internal efficiency, relevance and external effectiveness; and Management includes governance, decentralization and resource management. It is one of the best policies in Sub-Saharan Africa (SSA) as pointed by World Bank (2005); with well established strategic plans but had not yet been able to be implemented effectively.

Woods (2007) pointed out that the education system of Tanzania has made commendable progress in the period since 2000, especially in the introduction of free primary education, in steps taken to broaden access to secondary, and in the introduction of competence based curricula at primary and secondary levels. However, there are still challenges to improve system performance in terms of inclusion, repetition and completion at primary level, and to expand opportunity at secondary from the previously very low base. Pre-service and in-service training have lacked the necessary coherence with each other and with the demands of changes in the system, especially of curriculum and pedagogy in enhancing science and technology. Particular attention needs to be paid to equity and strengthening of financial management and mainstreaming of ongoing project and programs. These need to be pursued vigorously and implemented fully. A prioritized strategy for capacity building is required for these and all other major dimensions (World Bank, 2005). In this case there is no problem with the policy; the problem is in the implementation.

MOTIVATING TEACHING PERSONNEL
In enabling the Ministry to meet the goals the question of teachers concern should be addressed as the second strategy as the foremost activities to motivate teaching resource. Teaching resource elsewhere plays the big role in ensuring maximum success in education arena. Recognizing the unique motivational styles can also help to identify the types of educational products and problems that will satisfy respective needs (Tough, 1979). So, teachers’ in-service training, teaching environment nourishment, reasonable payments and retain/recognition are important factors.

Learners are motivated by teachers so teachers should be motivated in order to transmit it to learners. Apart from sufficient pre-service and in-service training, capacity building and refresher courses provision; the availability of required teaching and learning materials in one hand build teachers’ morale and motivate them. Struggle in finding teaching-learning for themselves, shortage of books and other supportive materials de-motivate teachers and encourage insufficient teaching and rote learning. Ibid (1979) remarked that someone can get easily distracted from the task at hand and become more motivated to do something else perhaps not on task.

Teachers need laboratory with recommended equipments to prepare and demonstrate practical and laboratory technician an assistant. In the past when schools were few, a science teacher needed to have a laboratory to work in and there were also a laboratory technician to work together (Guardian, 2009). Laboratory is compulsory for science subjects; there is no way, without their availability. But these days in some schools even science teachers do not have laboratories to conduct experiments and there is no laboratory technician to help the teacher.

Teaching environment improvements include housing water and sanitation. Research has shown that many teachers do not have houses, and those who do live in houses that are often in serious need of repair and most schools are in very poor physical environment. The challenges of school improvement in rural areas are associated with the presence of teachers, but many rural schools in Tanzania like other countries “serve disadvantaged populations, have great difficulty attracting and retaining qualified teachers and have management systems poorly adapted to their small size”(ADEA, 2006)

Pay reform to adequate salary in the other hand settle psychological and physical unrest of teachers and motivate them concentrate in their work accordingly. Teachers’ low payment is a burning issue and recently caused periodic strikes. In most of developing countries including Tanzania, teachers’ wages were considerably below the level necessary to ensure their adequate motivation (Fry, 2003). The government should revise teachers’ pay reform and come up with solution otherwise academic fraud might emerge or persist. When teachers sell grades or require students to pay for private tutoring, most observers recognize it as corruption. But it is tolerated because everyone understands that it is necessary to survive (Fontana, 2008). Their practices may be interpreted by some as a reasonable adaptive response to a difficult situation. In some instances it is even tolerated by government, which sees it as the only way to maintain the number of teachers and the quality of teaching.

There is a need to train and retain enough teachers. Learning is a process of interaction between teachers and students as they both participate in the learning process, but with more weight given to teachers to show the way, for recommended number of learners in the class. Learning achievements can mainly “be determined in classroom by motivated teachers who plan for teaching, put into practice what they have learned” (ADEA, 2006). But teachers’ motivation is critically ignored factor in all levels of policy choices including crowded classes (Ndawi, 1997). Motivation of teachers helps to retain them at their work places and it includes “materials and psychological needs” as pay on its own does not increase motivation among teachers; however pecuniary motives are likely to be dominant among teachers in less developed countries. In SSA, teachers’ motivation is low and it has been detrimental to the quality of education” (Fry, 2003).

LEARNERS MOTIVATION
In motivating learners, as the third strategy, emphasis should be applied in approaches such as demonstration, case study and problem based learning. Their introduction or if have been introduced, could aim at increasing the students’ interests in learning science subjects. Also a useful method of concept mapping would be given for assessment, particularly for the development of the students’ self-directed learning skills and lifelong learning skills.

Demonstration as one of the approaches is very useful in arousing interest. According to Lagowski (1990) students retain 10% of what they read, 26% of what they hear, 30% of what they see, 50% of what they see and hear, 70% of what they say, and 90% of something they say as they do something. So if teachers show as many demonstrations as they can to the students as well as letting the students do demonstrations by themselves, students will learn more actively and effectively. Students also need more positive and realistic demonstrations of the scope and limitations of science and scientists.

Science historical stories are one of the methods which can be used elsewhere even in remote areas and is costless. According to Huo (2006) the development of science and technology can not be separated from the contributions of past scientists. The science stories will inspire students to overcome the difficulties and to gain success. So giving the relevant story will spark the students’ inner-motivation. Only with inner-motivation will the students show their initiative and creative abilities in their learning and working processes. For instance ‘Newton becomes a professor at the age of 25 years in Glasgow University and lately he formulated the law of gravitational force’.

Multimedia technology approach can be applied in areas where it allows. Although it is expensive and it requires power availability for schools that can afford is also recommended. With the development of computer technology multimedia methods are been increasingly used in teaching practice. A multimedia course can combine sound and pictures with knowledge. This reinforces the fact that students retain 50% of what they see and hear, as the use of multimedia technology gives students more information than just writing on the blackboard, and increase the chance of active learning (ibid). But on the other hand it can also makes a more boring lecture for the students, if too much useless information is given or if, when using the projector, the light in the classroom is too dim. To avoid these disadvantages the teacher can combine it with other strategies and gives students more opportunity to think and ask questions.

Case study is another interesting teaching-learning approach and also costless. Science is very relevant to our real life. It would be worthwhile to find some real cases before the teacher gives a lecture. When students find that what they will learn is useful to the society, they will be active learners (Lagowski, 1990). Case studies are capable of being delivered with a range of styles, they can be designed to complement (not replace) other teaching approaches, and focus on re-visiting topics rather than attempting to cover an entire syllabus. In addition, the contexts and delivery styles can be selected in order to be stimulating. It is crucial, therefore, to highlight the importance of science and its relevance to students’ lives.

Problem-based learning (PBL) is a pedagogical approach based on recent advances in cognitive science research on human learning (Barrows, 1985). PBL has been widely used in undergraduate settings in Western countries but there is very little published on the application of PBL in science education in developing countries like Tanzania. A PBL class is organized around collaborative problem solving activities that provide a context for learning and discovery. The responsibility for learning is with the student; not with the facilitator. There are five well-defined stages in the PBL process: introduction, inquiry, self-directed study, revisiting the hypotheses, and self-evaluation (Ram 1999). This approach can be introduced in higher learning institutions although it is expensive, its return to education is more important.

Research shows that students do not like examinations and if their mark is low it may reduce their confidence to continue learning. It also can not reflect all the problems and may not show the abilities that the students have gained (Huo, 2006). It is preferable to find other methods to supplement examinations. Concept mapping is an alternative method: it can show the teacher how much the students knew and how much they didn’t know; and the students can assess their own learning. I don’t suggest examinations to be eliminated completely but they can be reduced in number in levels of education. Elimination of National Standard IV Exam in primary school level and National Form II Exam in O-level is the exact instance. Concept mapping was developed by

With more and more information coming onto the internet everyday, it became harder to go through or find the exact sort of data that was required by an individual. One could find a lot of junk data -as it is being termed- online. This came down to the advent of data science.

Data science is the application of algorithms, machine learning and various such methods and options for bringing out patterns by going through a lot of data. If you’re wondering what sort of patterns could a bunch of numbers have, well the kind that makes everything easier. For example, let’s say one would like to predict the kind of weather for tomorrow or maybe teach a computer how to play chess, one could feed all of this data and find out the probabilities of similar weather or train a computer to react by producing a certain counter move or moves. It gives one the ability to predict with a very high accuracy or learn and adapt from a newfound instance. Number-crunching, as it is sometimes called, is turning to be a necessity in an era where everything is backed by solid data and numbers that add authenticity.

Data scientists are essential analysts when it comes to research and content curation and extraction to make all of this data reader worthy and visual worthy. They become the backbone of providing valuable information in the junkyard of data we have to filter through everyday. As data representation becomes the new trend and the new demand, it may turn out to be a driving force in multiple platforms. From newspapers to machine learning, it’s everywhere already. All it takes to become one is inquisition and the mental set to be ready to work for it. As far as degrees and formal education go, it’s all good to have a PhD but not an absolute requirement. So start your data science training today and benefit with your newfound research capabilities as it becomes a secondary task at the back of the head.

The demand for data scientists is increasing day by day. Data science is a new technology and though not enough material is available on the internet to study it. Reputable institutes are teaching data science to their students. But students from other institutes can also study data science course.

In spite of all the focus Cyber security has had, it has been a challenging journey so far. The global spend on IT Security is expected to hit $120 Billion by 2017 [4], and that is one area where the IT budget for most companies either stayed flat or slightly increased even in the recent financial crises [5]. But that has not substantially reduced the number of vulnerabilities in software or attacks by criminal groups.

The US Government has been preparing for a “Cyber Pearl Harbour” [18] style all-out attack that might paralyze essential services, and even cause physical destruction of property and lives. It is expected to be orchestrated from the criminal underbelly of countries like China, Russia or North Korea.

The economic impact of Cyber crime is $100B annual in the United states alone [4].

There is a need to fundamentally rethink our approach to securing our IT systems. Our approach to security is siloed and focuses on point solutions so far for specific threats like anti viruses, spam filters, intrusion detections and firewalls [6]. But we are at a stage where Cyber systems are much more than just tin-and-wire and software. They involve systemic issues with a social, economic and political component. The interconnectedness of systems, intertwined with a people element makes IT systems un-isolable from the human element. Complex Cyber systems today almost have a life of their own; Cyber systems are complex adaptive systems that we have tried to understand and tackle using more traditional theories.

2. Complex Systems – an Introduction

Before getting into the motivations of treating a Cyber system as a Complex system, here is a brief of what a Complex system is. Note that the term “system” could be any combination of people, process or technology that fulfils a certain purpose. The wrist watch you are wearing, the sub-oceanic reefs, or the economy of a country – are all examples of a “system”.

In very simple terms, a Complex system is any system in which the parts of the system and their interactions together represent a specific behaviour, such that an analysis of all its constituent parts cannot explain the behaviour. In such systems the cause and effect can not necessarily be related and the relationships are non-linear – a small change could have a disproportionate impact. In other words, as Aristotle said “the whole is greater than the sum of its parts”. One of the most popular examples used in this context is of an urban traffic system and emergence of traffic jams; analysis of individual cars and car drivers cannot help explain the patterns and emergence of traffic jams.

While a Complex Adaptive system (CAS) also has characteristics of self-learning, emergence and evolution among the participants of the complex system. The participants or agents in a CAS show heterogeneous behaviour. Their behaviour and interactions with other agents continuously evolving. The key characteristics for a system to be characterised as Complex Adaptive are:

The behaviour or output cannot be predicted simply by analysing the parts and inputs of the system
The behaviour of the system is emergent and changes with time. The same input and environmental conditions do not always guarantee the same output.
The participants or agents of a system (human agents in this case) are self-learning and change their behaviour based on the outcome of the previous experience
Complex processes are often confused with “complicated” processes. A complex process is something that has an unpredictable output, however simple the steps might seem. A complicated process is something with lots of intricate steps and difficult to achieve pre-conditions but with a predictable outcome. An often used example is: making tea is Complex (at least for me… I can never get a cup that tastes the same as the previous one), building a car is Complicated. David Snowden’s Cynefin framework gives a more formal description of the terms [7].

Complexity as a field of study isn’t new, its roots could be traced back to the work on Metaphysics by Aristotle [8]. Complexity theory is largely inspired by biological systems and has been used in social science, epidemiology and natural science study for some time now. It has been used in the study of economic systems and free markets alike and gaining acceptance for financial risk analysis as well (Refer my paper on Complexity in Financial risk analysis here [19]). It is not something that has been very popular in the Cyber security so far, but there is growing acceptance of complexity thinking in applied sciences and computing.

3. Motivation for using Complexity in Cyber Security

IT systems today are all designed and built by us (as in the human community of IT workers in an organisation plus suppliers) and we collectively have all the knowledge there is to have regarding these systems. Why then do we see new attacks on IT systems every day that we had never expected, attacking vulnerabilities that we never knew existed? One of the reasons is the fact that any IT system is designed by thousands of individuals across the whole technology stack from the business application down to the underlying network components and hardware it sits on. That introduces a strong human element in the design of Cyber systems and opportunities become ubiquitous for the introduction of flaws that could become vulnerabilities [9].

Most organisations have multiple layers of defence for their critical systems (layers of firewalls, IDS, hardened O/S, strong authentication etc), but attacks still happen. More often than not, computer break-ins are a collision of circumstances rather than a standalone vulnerability being exploited for a cyber-attack to succeed. In other words, it’s the “whole” of the circumstances and actions of the attackers that cause the damage.

3.1 Reductionism vs Holisim approach

Reductionism and Holism are two contradictory philosophical approaches for the analysis and design of any object or system. The Reductionists argue that any system can be reduced to its parts and analysed by “reducing” it to the constituent elements; while the Holists argue that the whole is greater than the sum so a system cannot be analysed merely by understanding its parts [10].

Reductionists argue that all systems and machines can be understood by looking at its constituent parts. Most of the modern sciences and analysis methods are based on the reductionist approach, and to be fair they have served us quite well so far. By understanding what each part does you really can analyse what a wrist watch would do, by designing each part separately you really can make a car behave the way you want to, or by analysing the position of the celestial objects we can accurately predict the next Solar eclipse. Reductionism has a strong focus on causality – there is a cause to an affect.

But that is the extent to which the reductionist view point can help explain the behaviour of a system. When it comes to emergent systems like the human behaviour, Socio-economic systems, Biological systems or Socio-cyber systems, the reductionist approach has its limitations. Simple examples like the human body, the response of a mob to a political stimulus, the reaction of the financial market to the news of a merger, or even a traffic jam – cannot be predicted even when studied in detail the behaviour of the constituent members of all these ‘systems’.

We have traditionally looked at Cyber security with a Reductionist lens with specific point solutions for individual problems and tried to anticipate the attacks a cyber-criminal might do against known vulnerabilities. It’s time we start looking at Cyber security with an alternate Holism approach as well.

3.2 Computer Break-ins are like pathogen infections

Computer break-ins are more like viral or bacterial infections than a home or car break-in [9]. A burglar breaking into a house can’t really use that as a launch pad to break into the neighbours. Neither can the vulnerability in one lock system for a car be exploited for a million others across the globe simultaneously. They are more akin to microbial infections to the human body, they can propagate the infection as humans do; they are likely to impact large portions of the population of a species as long as they are “connected” to each other and in case of severe infections the systems are generally ‘isolated’; as are people put in ‘quarantine’ to reduce further spread [9]. Even the lexicon of Cyber systems uses biological metaphors – Virus, Worms, infections etc. It has many parallels in epidemiology, but the design principles often employed in Cyber systems are not aligned to the natural selection principles. Cyber systems rely a lot on uniformity of processes and technology components as against diversity of genes in organisms of a species that make the species more resilient to epidemic attacks [11].

The Flu pandemic of 1918 killed ~50M people, more than the Great War itself. Almost all of humanity was infected, but why did it impact the 20-40yr olds more than others? Perhaps a difference in the body structure, causing different reaction to an attack?

Complexity theory has gained great traction and proven quite useful in epidemiology, understanding the patterns of spread of infections and ways of controlling them. Researchers are now turning towards using their learnings from natural sciences to Cyber systems.

4. Approach to Mitigating security threats

Traditionally there have been two different and complimentary approaches to mitigate security threats to Cyber systems that are in use today in most practical systems [11]:

4.1 Formal validation and testing

This approach primarily relies on the testing team of any IT system to discover any faults in the system that could expose a vulnerability and can be exploited by attackers. This could be functional testing to validate the system gives the correct answer as it is expected, penetration testing to validate its resilience to specific attacks, and availability/ resilience testing. The scope of this testing is generally the system itself, not the frontline defences that are deployed around it.

This is a useful approach for fairly simple self-contained systems where the possible user journeys are fairly straightforward. For most other interconnected systems, formal validation alone is not sufficient as it’s never possible to ‘test it all’.

Test automation is a popular approach to reduce the human dependency of the validation processes, but as Turing’s Halting problem of Undecideability[*] proves – it’s impossible to build a machine that tests another one in all cases. Testing is only anecdotal evidence that the system works in the scenarios it has been tested for, and automation helps get that anecdotal evidence quicker.

4.2 Encapsulation and boundaries of defence

For systems that cannot be fully validated through formal testing processes, we deploy additional layers of defences in the form of Firewalls or network segregation or encapsulate them into virtual machines with limited visibility of the rest of the network etc. Other common techniques of additional defence mechanism are Intrusion Prevention systems, Anti-virus etc.

This approach is ubiquitous in most organisations as a defence from the unknown attacks as it’s virtually impossible to formally ensure that a piece of software is free from any vulnerability and will remain so.

Approaches using Complexity sciences could prove quite useful complementary to the more traditional ways. The versatility of computer systems make them unpredictable, or capable of emergent behaviour that cannot be predicted without “running it” [11]. Also running it in isolation in a test environment is not the same as running a system in the real environment that it is supposed to be in, as it’s the collision of multiple events that causes the apparent emergent behaviour (recalling holism!).

4.3 Diversity over Uniformity

Robustness to disturbances is a key emergent behaviour in biological systems. Imagine a species with all organisms in it having the exact same genetic structure, same body configuration, similar antibodies and immune system – the outbreak of a viral infection would have wiped out complete community. But that does not happen because we are all formed differently and all of us have different resistance to infections.

Similarly some mission critical Cyber systems especially in the Aerospace and Medical industry implement “diversity implementations” of the same functionality and centralised ‘voting’ function decides the response to the requester if the results from the diverse implementations do not match.

It’s fairly common to have redundant copies of mission critical systems in organisations, but they are homogenous implementations rather than diverse – making them equally susceptible to all the faults and vulnerabilities as the primary ones. If the implementation of the redundant systems is made different from the primary – a different O/S, different application container or database versions – the two variants would have different level of resilience to certain attacks. Even a change in the sequence of memory stack access could vary the response to a buffer overflow attack on the variants [12] – highlighting the central ‘voting’ system that there is something wrong somewhere. As long as the input data and the business function of the implementation are the same, any deviations in the response of the implementations is a sign of potential attack. If a true service-based architecture is implemented, every ‘service’ could have multiple (but a small number of) heterogeneous implementations and the overall business function could randomly select which implementation of a service it uses for every new user request. A fairly large number of different execution paths could be achieved using this approach, increasing the resilience of the system [13].

Multi variant Execution Environments (MVEE) have been developed, where applications with slight difference in implementation are executed in lockstep and their response to a request are monitored [12]. These have proven quite useful in intrusion detection trying to change the behaviour of the code, or even identifying existing flaws where the variants respond differently to a request.

On similar lines, using the N-version programming concept [14]; an N-version antivirus was developed at the University of Michigan that had heterogeneous implementations looking at any new files for corresponding virus signatures. The result was a more resilient anti-virus system, less prone to attacks on itself and 35% better detection coverage across the estate [15].

4.4 Agent Based Modelling (ABM)

One of the key areas of study in Complexity science is Agent Based Modelling, a simulation modelling technique.

Agent Based Modelling is a simulation modelling technique used to understand and analyse the behaviour of Complex systems, specifically Complex adaptive systems. The individuals or groups interacting with each other in the Complex system are represented by artificial ‘agents’ and act by predefined set of rules. The Agents could evolve their behaviour and adapt as per the circumstances. Contrary to Deductive reasoning[†] that has been most popularly used to explain the behaviour of social and economic systems, Simulation does not try to generalise the system and agents’ behaviour.

ABMs have been quite popular to study things like crowd management behaviour in case of a fire evacuation, spread of epidemics, to explain market behaviour and recently financial risk analysis. It is a bottom-up modelling technique wherein the behaviour of each agent is programmed separately, and can be different from all other agents. The evolutionary and self-learning behaviour of agents could be implemented using various techniques, Genetic Algorithm implementation being one of the popular ones [16].

Cyber systems are interconnections between software modules, wiring of logical circuits, microchips, the Internet and a number of users (system users or end users). These interactions and actors can be implemented in a simulation model in order to do what-if analysis, predict the impact of changing parameters and interactions between the actors of the model. Simulation models have been used for analysing the performance characteristics based on application characteristics and user behaviour for a long time now – some of the popular Capacity & performance management tools use the technique. Similar techniques can be applied to analyse the response of Cyber systems to threats, designing a fault-tolerant architecture and analysing the extent of emergent robustness due to diversity of implementation.

One of the key areas of focus in Agent Based modelling is the “self-learning” process of agents. In the real world, the behaviour of an attacker would evolve with experience. This aspect of an agent’s behaviour is implemented by a learning process for agents, Genetic Algorithm’s being one of the most popular technique for that. Genetic Algorithms have been used for designing automobile and aeronautics engineering, optimising the performance of Formula one cars [17] and simulating the investor learning behaviour in simulated stock markets (implemented using Agent Based models).

An interesting visualisation of Genetic Algorithm – or a self-learning process in action – is the demo of a simple 2D car design process that starts from scratch with a set of simple rules and end up with a workable car from a blob of different parts: http://rednuht.org/genetic_cars_2/

The self-learning process of agents is based on “Mutations” and “Crossovers” – two basic operators in Genetic Algorithm implementation. They emulate the DNA crossover and mutations in biological evolution of life forms. Through crossovers and mutations, agents learn from their own experiences and mistakes. These could be used to simulate the learning behaviour of potential attackers, without the need to manually imagine all the use cases and user journeys that an attacker might try to break a Cyber system with.

5. Conclusion

Complexity in Cyber systems, especially the use of Agent Based modelling to assess the emergent behaviour of systems is a relatively new field of study with very little research done on it yet. There is still some way to go before using Agent Based Modelling becomes a commercial proposition for organisations. But given the focus on Cyber security and inadequacies in our current stance, Complexity science is certainly an avenue that practitioners and academia are increasing their focus on.

Commercially available products or services using Complexity based techniques will however take a while till they enter the mainstream commercial organisations.

References

[1] J. A. Lewis and S. Baker, “The Economic Impact of Cybercrime and Cyber Espionage,” 22 July 2013. [Online]

[2] L. Kugel, “Terrorism and the Global Economy,” E-Internatonal Relations Students, 31 Aug 2011. [Online].

[3] “Cybersecurity – Facts and Figures,” International Telecommunications Union, [Online].

[4] “Interesting Facts on Cybersecurity,” Florida Tech University Online, [Online].

[5] “Global security spending to hit $86B in 2016,” 14 Sep 2012. [Online].

[6] S. Forrest, S. Hofmeyr and B. Edwards, “The Complex Science of Cyber Defense,” 24 June 2013. [Online].

[7] “Cynefin Framework (David Snowden) – Wikipedia” [Online].

[8] “Metaphysics (Aristotle) – Wikipedia” [Online].

[9] R. Armstrong, “Motivation for the Study and Simulation of Cybersecurity as a Complex System,” 2008.

[10] S. A. McLeod, Reductionism and Holism, 2008.

[11] R. C. Armstrong, J. R. Mayo and F. Siebenlist, “Complexity Science Challenges in Cybersecurity,” March 2009.

[12] B. Salamat, T. Jackson, A. Gal and M. Franz, “Orchestra: Intrusion Detection Using Parallel Execution and Monitoring of Program Variants in User-Space,” Proceedings of the 4th ACM European conference on Computer systems, pp. 33-46, April 2009.

[13] R. C. Armstrong and J. R. Mayo, “Leveraging Complexity in Software for Cybersecurity (Abstract),” Association of Computing Machinery, pp. 978-1-60558-518-5, 2009.

[14] C. Liming and A. Avizienis, “N-VERSION PROGRAMMINC: A FAULT-TOLERANCE APPROACH TO RELlABlLlTY OF SOFTWARE OPERATlON,” Fault-Tolerant Computing, p. 113, Jun1995.

[15] J. Oberheide, E. Cooke and F. Jahanian, “CloudAV: N-Version Antivirus in the Network Cloud,” University of Michigan, Ann Arbor, MI 48109, 2008.

[16] J. H. Holland, Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence, Michigan: University of Michigan Press, 1975.

[17] K. &. B. P. J. Wloch, “Optimising the performance of a formula one car using a genetic algorithm,” Parallel Problem Solving from Nature-PPSN VIII, pp. 702-711, January 2004.