A GIS-Based Road-Mapping Network for Responding to Future Terrorist Activities in Colombo, Sri Lanka

The contemporary world has been moving to a new direction where anti-terrorist strategies are adopted intelligently to confront new challenges of global terrorism. Sri Lanka experienced a brutal terrorist attack again just recently after a decade of the total defeat of one of the dangerous terrorist organizations in the World, Liberation Tigers of Tamil Eelam (LTTE), in 2019. The LTTE demanded the Northern and Eastern provinces. Colombo had been a major vulnerable location and the LTTE had been frequently carrying out attacks against civilian targets, key politicians, government officials, military installations, as well as economic and commercial targets for about three decades. This paper therefore focuses on the applicability of GIS-based road-mapping analysisto respond to future terrorist activities based on the previous experience of terrorist attacks in Sri Lanka. Based on the methodology used in this study, 29 GN Divisions of Colombo DSD were selected as research sites and network, and hotspots analysis were then employed. Research results revealed that there were approximately 97 vulnerable locations at marginalized spaces. Distance from security installation to vulnerable location ranges from 0.8 km to 2.4 km. Average responding time to any adversary action varies from 3 to 5 minutes. Junctions within the 400 m to 500 m distance from all vulnerable locations in the study area were identified as suitable road blocking points. Six GNDs were identified as major potential areas for terrorist activities. Petta and its neighboring wards seem to be the most vulnerable locations for terrorist activities in the future. Based on the accurate information from particular areas, terrorist activities can be monitored and prevented by this risk and emergency management road network system

TERRORISM FROM A GLOBAL PERSPECTIVE: INFLUENCE AND NETWORK STRUCTURE ANALYSIS

While terrorism is not new, today’s terrorist threat is different from that of the past. Terrorism has evolved, and terrorist groups today are more structured and better organized. Modern technology enables terrorists to plan and operate worldwide as never before. Through the constant exchange of information between parties, perpetrator groups may influence or be influenced by other perpetrator groups to improve their efficacy. This study moves away from the traditional analysis of terrorist groups and examines terrorist networks from a global perspective. Using network science and our proposed methodology to calculate influence strength, this thesis looks at the extent of influence of one perpetrator group with another based on their activities and locations. We observe that some perpetrator groups, like ISIL and Al–Nusrah Front, have high and increasing influence strength. Some of these perpetrator groups are, from a network science perspective, neighbors. In addition, the community detection algorithm shows that most of the perpetrator groups with high influence strength exist within the same network-defined community. Our proposed influence score metric allows measurement of a node's actual influence score based on the responses of other nodes around it, as compared to existing measures, which determine the node's influential strength by its position in the network. We hope our study provides insights into terrorism and how influence spreads among perpetrators.http://archive.org/details/terrorismfromagl1094560417Outstanding ThesisArmy, SingaporeApproved for public release; distribution is unlimited

Dynamic Portfolio Selection to Counter Terrorism by using Quantum Neural Network Approach

Not only Pakistan but the whole world is facing the problems of prevailing terrorist activities and attacks in many forms. Terrorism has diverse aspects and to eradicate this growing problem a hybrid model of quantum and classical neurons is suggested for the prediction of the risk involved and returns of investments in recommended areas to minimize terrorism. These areas are recommended on the basis of the findings of Crime analysts and professionals from other related domains after a deep analysis of the situation of the country and terrorist activities. The identification of the areas which causes terrorism is a core step towards counter the terrorism. Hopfield neural network is used to predict best possible portfolio from available resources. The recommended multilayer hybrid Quantum Neural Network holds hidden layer of quantum neurons while the visible layer is of classical neurons. With the help of QNN an appropriate portfolio can be selected whose risk factor will be minimum and the output generated from investments in identified areas will be maximum.  Keywords:Quantum neural network, Portfolio selection, Resource allocation, Quantum back propagation, Quantum computation

Positing the problem : enhancing classification of extremist web content through textual analysis

Webpages with terrorist and extremist content are key factors in the recruitment and radicalization of disaffected young adults who may then engage in terrorist activities at home or fight alongside terrorist groups abroad. This paper reports on advances in techniques for classifying data collected by the Terrorism and Extremism Network Extractor (TENE) web-crawler, a custom-written program that browses the World Wide Web, collecting vast amounts of data, retrieving the pages it visits, analyzing them, and recursively following the links out of those pages. The textual content is subjected to enhanced classification through software analysis, using the Posit textual analysis toolset, generating a detailed frequency analysis of the syntax, including multi-word units and associated part-of-speech components. Results are then deployed in a knowledge extraction process using knowledge extraction algorithms, e.g., from the WEKA system. Indications are that the use of the data enrichment through application of Posit analysis affords a greater degree of match between automatic and manual classification than previously attained. Furthermore, the incorporation and deployment of these technologies promises to provide public safety officials with techniques that can help to detect terrorist webpages, gauge the intensity of their content, discriminate between webpages that do or do not require a concerted response, and take appropriate action where warranted

When Terrorist Disengagement Processes Are Consistent with Previous Violent Radicalization: Two Case Studies

Although terrorist disengagement is a dynamic process, this study proposes the likelihood of a continuity in the prevailing factors influencing exit from terrorism and the prevailing dimensions which initially influenced violent radicalization. Through the analysis of two contrasting cases featuring third-generation Muslims formerly involved in jihadist activities in Spain, we assess a connection between the prevailing push and pull factors which sparked individuals to cease their terrorist engagement and the predominant dimensions that earlier prompted the radicalization which led them to terrorist involvement. Drawing from in-depth interviews with the two former jihadists, Hassan and Omar, conducted while they were serving prison sentences for terrorism offences, we suggest that the significance of ideology and network in, respectively, their journeys from Islamic fundamentalism towards jihadism is similarly reflected in their accounts of ending jihadist involvement, even in the presence of secondary factors that also play a role in such a complex process