Pdf Proposal Paper On Terrorism And Development
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Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life.
- Journal of Development Economics
- Prediction of Future Terrorist Activities Using Deep Neural Networks
- Country Reports on Terrorism 2019
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Journal of Development Economics
Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism.
Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence AI.
Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network DNN are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not?
Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network NN , five-layer DNN, and three traditional machine learning algorithms, i.
This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.
Terrorism means the use of intentional indiscriminate and illegal power and violence for creating terror amongst general population in order to gain some political, monetary, religious, or legal objectives. According to Global Terrorism Database GTD , in alone 1, different terrorist attacks have happened, causing 6, fatalities and badly affecting the quality of life of individuals in the society. The orange color shows high intensity value as a combination of incident fatalities and injuries.
The map shows a very high rate of terrorism in South Asia and the Middle East. The response of terrorist events is constant sense of fear, feeling helpless, experiencing fear and anger, and intolerance or aggression towards certain ethnicity or religious groups. It is equally important that the emotional reactions of the population is understood in regard to terrorist events so that we are able to design assistance to effectively help those who are suffering from these issues or they do not react to carry out another terrorist activity as a revenge.
Terrorism has been studied for decades to understand the major factors causing the act of terrorism or understanding how to perform counterterrorism or understanding the social and economic effects of terrorism [ 3 , 4 ]. However, because of the complex nature of terrorism, it is difficult to find an effective solution that can be used as a counterterrorism to protect the lives of individuals. Identification of terrorist ideologies and prediction of future terrorist attacks have been proven to be of great importance and time-consuming process.
Machine learning algorithms have been used recently to study the different factors of terrorism [ 5 , 6 ]. NN and particularly DNN are getting popularity mainly because of the fact that a huge amount of labelled data is available recently.
The advancements in computer technologies [ 7 — 9 ] have been able to create much powerful computer systems to perform the required computation in DNN. In this paper, NN and DNN models are used to make predictions of different factors that lead to terrorist activities. The model is helpful for law enforcement agencies to make prediction before an incident actually happens and potentially causes the loss of precious lives. The predicted factors are explained below.
These predictions are important to understand in order to perform counterterrorism. Deep learning can make these predictions efficiently and can help law enforcement agencies to devise mechanisms to deal with terrorists and protect the lives of individuals. With the help of these tools, a terrorist activity can be stopped before it can actually happen and make destructions in terms of lives, infrastructure, or law.
The rest of the paper is organized as follows. Related work is explained in Section 2 to highlight the current state-of-the-art research work in the field. Proposed methodology is explained in Section 3.
It also gives a detailed analysis of the dataset, and the architectures of NN and DNN used for the prediction of different factors are explained. Results are demonstrated in Section 4 , and the paper is concluded in Section 5 with possible future research directions. Terrorism can affect a society very badly and can have a huge impact on the people. The topic has been studied extensively over the last few decades to understand its causes and how to develop an effective counterterrorism mechanism to reduce the chances of terrorist activities.
Machine learning algorithms and data mining techniques have also been applied to understand the different factors involved in a terrorist activity.
The system can detect, track, and predict the potential terrorist activities in real time. In , Tranchita et al. They have developed a new security analysis methods that predicts events uncertainties. In [ 12 ], Godwin et al. The paper demonstrates a sequence comparison from bioinformatics, modified to incorporate the element of time. The paper has claimed that the system reveals relationships between entities that are not easily detectable using traditional methods.
In , Ozgul et al. The authors demonstrated that ensemble framework has better figures compared to individual models. In , Dixon et al. The authors used a game that is designed by criminologist and psychologists to generate data that can test the suitability of AI techniques to look for counterterrorism.
In , Toure and Gangopadhyay [ 16 ] collected incident data from a real-time system to develop a risk model that calculates the terrorism risk level of different locations.
A set of rules was also proposed along with the risk model to make prediction of the future terrorist activities. In another study by Saha et al. In , Mo et al. In [ 19 ], Ding et al. In , Garg et al. Different factors of tweets were taken into account such as last retweet, number of retweets, and number of favorites, which were used to study the sentiments of tweets.
Five different machine learning models, i. In , Li et al. The paper has claimed that the framework has made accurate prediction of the behavior of the terrorist groups. Zhang et al. The model was used to build a spatial risk assessment model of terrorist attacks. In another study by Hao et al.
Random Forest is used to predict the risk of terrorist attacks using 15 driving factors. In , Agarwal et al. Different data mining and machine learning algorithms such as SVM, Random Forest, and logistic regression have been used to understand the dataset and predict different factors such as the success of terrorist attack, the group that was involved in terrorist attack and the effect of different external factors involved in terrorist attack.
In , Kalaiarasi et al. They used the GTD dataset for detection of terrorism. In , Maniraj et al. They analyzed the GTD dataset and used machine learning algorithm that can predict the probability of attacks in different regions.
In , Christie in his thesis [ 28 ] carried out a study to understand the dynamics of unclaimed terrorism events in Pakistan using machine learning algorithms. They made predictions on terrorist attributes such as attack, target, weapon type, spatial attack, and lethality of attacks. The study made an attempt to match the unattributed terrorist attack to known terrorist groups.
In , Ahmad et al. The focus was to classify tweets into two categories: extremist and nonextremist classes. The system uses deep learning-based sentiment analysis to make a classification about the tweets. Other similar studies in can be found in [ 30 — 32 ].
All previous studies have applied machine learning and deep learning techniques to make AI-based model for terrorism. Current state-of-the-art research papers are based on understanding the pattern of terrorism and have proposed different solutions to analyze factors of terrorism.
However, no research work is carried out in order to make prediction of future terrorist activities and predict different factors such as success, suicide, weapon type, attack type, and region. Clearly, there is a research gap for modeling and predicting future terrorist activities using deep learning. This research paper compares the performance of traditional machine learning and deep neural networks and concludes that deep neural network is a suitable model for prediction of future terrorist activities.
In this section, a detailed analysis of the dataset is given. The preprocessing performed on the dataset is also explained. GTD contains information about terrorist activities from until , including more than , different instances of terrorism. In this paper, 34 attributes some attributes are redundant and hence discarded are taken for the analysis. These attributes along with description are given in Table 1.
The following are different factors that neural network and deep neural network will be trained to learn. This field indicates whether the attack is suicide or not suicide. Dimension of the dataset is. This field indicates the success of a terrorist strike. Each class has , instances. This field indicates the general type of weapon used in the incident.
In the dataset, 13 different labels are used to represent different type of weapon. These labels are explained below. Each class has 92, instances. This field indicates 12 different regions. These regions are explained below. Each class has 50, instances.
This field indicates the general method of attack and broad class of tactics used.
Prediction of Future Terrorist Activities Using Deep Neural Networks
Within the framework of the General Assembly, Member States have been backing initiatives, coordinating actions and drawing up rules to fight terrorism as effectively as possible. Among the progress made, undoubtedly worthy of mention is the United Nations Global Counter-Terrorism Strategy approved in , a strategy that is based on four main pillars: tackling the conditions that lead to the spread of terrorism; increasing the capabilities of States to prevent and deal with terrorism; and ensuring respect for human rights for everyone and guaranteeing the rule of law in the fight against terrorism. The mandate of the CTITF is to step up the coordination and coherence of United Nations activities in the field of the global fight against terrorism. In addition to the Global Strategy, the United Nations has established an institutional counter-terrorism architecture within the framework of the Security Council SC , particularly through the Counter-Terrorism Committee. This is commissioned with fostering the application of Resolution , approved by the Security Council shortly after the attacks on 11 September , and which contains a raft of response measures.
Terrorism in all its forms poses a direct threat to the security of the citizens of NATO countries, and to international stability and prosperity. It is a persistent global threat that knows no border, nationality or religion and is a challenge that the international community must tackle together. NATO will continue to fight this threat in all its forms and manifestations with determination and in full solidarity. In support of national authorities, NATO ensures shared awareness of the terrorist threat through consultations, enhanced intelligence-sharing and continuous strategic analysis and assessment. The way NATO handles sensitive information has gradually evolved, based on successive summit decisions and continuing reform of intelligence structures since
Country Reports on Terrorism 2019
In the wake of the terrorist attacks of September 11, , the United States launched an international war on terrorism defined by military intervention, nation building, and efforts to reshape the politics of the Middle East. As of , however, it has become clear that the American strategy has destabilized the Middle East while doing little to protect the United States from terrorism. Whatever President Trump decides to do, an evaluation of the War on Terror should inform his policies. We argue that the War on Terror failed. This failure has two fundamental—and related—sources.
In , the United States and our partners made major strides to defeat and degrade international terrorist organizations. And throughout the year, a number of countries in Western Europe and South America joined the United States in designating Iran-backed Hizballah as a terrorist group in its entirety. Despite these successes, dangerous terrorist threats persisted around the world. Even as ISIS lost its leader and territory, the group adapted to continue the fight from its affiliates across the globe and by inspiring followers to commit attacks.
Within the framework of the General Assembly, Member States have been backing initiatives, coordinating actions and drawing up rules to fight terrorism as effectively as possible. Among the progress made, undoubtedly worthy of mention is the United Nations Global Counter-Terrorism Strategy approved in , a strategy that is based on four main pillars: tackling the conditions that lead to the spread of terrorism; increasing the capabilities of States to prevent and deal with terrorism; and ensuring respect for human rights for everyone and guaranteeing the rule of law in the fight against terrorism. The mandate of the CTITF is to step up the coordination and coherence of United Nations activities in the field of the global fight against terrorism. In addition to the Global Strategy, the United Nations has established an institutional counter-terrorism architecture within the framework of the Security Council SC , particularly through the Counter-Terrorism Committee. This is commissioned with fostering the application of Resolution , approved by the Security Council shortly after the attacks on 11 September , and which contains a raft of response measures. The SC also fosters the effective application of Resolution , which establishes measures to prevent the incitement to undertake acts of terrorism and to promote tolerance and dialogue.
The information infrastructure is increasingly under attack by cyber criminals. The number, cost, and sophistication of attacks are increasing at alarming rates. Worldwide aggregate annual damage from attacks is now measured in billions of U. Attacks threaten the substantial and growing reliance of commerce, governments, and the public upon the information infrastructure to conduct business, carry messages, and process information. Most significant attacks are transnational by design, with victims throughout the world. Measures thus far adopted by the private and public sectors have not provided an adequate level of security. While new methods of attack have been accurately predicted by experts and some large attacks have been detected in early stages, efforts to prevent or deter them have been largely unsuccessful, with increasingly damaging consequences.
Rule of law—based criminal justice responses to terrorism are most effectively ensured when they are practiced within a criminal justice system capable of handling ordinary criminal offenses while protecting the rights of the accused and when all are equally accountable under the law. Building the capacity of weak criminal justice systems to safeguard mutual rights and responsibilities of governments and their citizens is essential for the alleviation of a number of conditions conducive to violent extremism and the spread of terrorism. A new wave of multilateral counterterrorism initiatives has the opportunity to recalibrate how criminal justice and rule of law—oriented counterterrorism capacity-building assistance is delivered to developing states with weak institutions. This policy brief argues that aligning counterterrorism capacity-building agendas within a framework informed by the Paris Principles and the development cooperation experience could greatly enhance the effectiveness and sustainability of criminal justice and rule of law capacity-building assistance in general and in preventing terrorism specifically. Women's Meaningful Participation in Peacebuilding and Governance. Capacity and accountability in the military: some examples from the SSD-program, Burundi.
Building on the Millennium Development Goals, the strategy incorporated Amartya Sen's capability-based approach to development. Although the first pillar of the strategy emphasised human rights and social progress over isolated economic growth, poverty, violence and retrogression in conflict zones since have led to the deaths of millions.
Timothy Schofield. Global ecosystems are emerging as both targets and conduits of terrorist activity. The end of the Cold War and the changing face of terrorism have contributed to this development. Domestic law has not, however, kept pace with this threat. Applicable legal doctrines do not operate effectively with existing anti-terrorism strategies and fail to express adequately societal outrage at such conduct.
Я ее убиваю. Стратмор мгновенно взвесил все варианты. Если он позволит Хейлу вывести Сьюзан из шифровалки и уехать, у него не будет никаких гарантий. Они уедут, потом остановятся где-нибудь в лесу.
Выпустите меня! - Она испуганно смотрела на открытую дверь его кабинета. Стратмор понял, что она смертельно напугана.