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Ever since the 9-11 incident, the multidisciplinary field of terrorism has experienced tremendous growth. As the domain has benefited greatly from recent advances in information technologies, more complex and challeng- ing new issues have emerged from numerous counter-terrorism-related research communities as well as governments of all levels. In t...
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... Cybersecurity is a multidisciplinary field and can have far-reaching economic, environmental, and social consequences [1][2][3]. Cybersecurity statistics indicate that there are 2200 cyber-attacks per day, with a cyber-attack happening every 39 s on average. In the US, a single data breach costs an average of USD 9.44 million, and cybercrime is predicted to cost USD 8 trillion in 2023 [4]. ...
In recent years, groups of cyber criminals/hackers have carried out cyber-attacks using various tactics with the goal of destabilizing web services in a specific context for which they are motivated. Predicting these attacks is a critical task that assists in determining what actions should be taken to mitigate the effects of such attacks and to prevent them in the future. Although there are programs to detect security concerns on the internet, there is currently no system that can anticipate or foretell whether the attacks will be successful. This research aims to develop sustainable strategies to reduce threats, vulnerability, and data manipulation of chatbots, consequently improving cyber security. To achieve this goal, we develop a conversational chatbot, an application that uses artificial intelligence (AI) to communicate, and deploy it on social media sites (e.g., Twitter) for cyber security purposes. Chatbots have the capacity to consume large amounts of information and give an appropriate response in an efficient and timely manner, thus rendering them useful in predicting threats emanating from social media. The research utilizes sentiment analysis strategy by employing chatbots on Twitter (and analyzing Twitter data) for predicting future threats and cyber-attacks. The strategy is based on a daily collection of tweets from two types of users: those who use the platform to voice their opinions on important and relevant subjects, and those who use it to share information on cyber security attacks. The research provides tools and strategies for developing chatbots that can be used for assessing cyber threats on social media through sentiment analysis leading to a global sustainable development of businesses. Future research may utilize and improvise on the tools and strategies suggested in our research to strengthen the knowledge domain of chatbots, cyber security, and social media.
... Cybersecurity is a multidisciplinary field and can have far-reaching economic, environmental, and social consequences [1][2][3]. Cybersecurity statistics indicate that there are 2,200 cyber-attacks per day, with a cyber-attack happening every 39 seconds on average. In the US, a single data breach costs an average of $9.44M, and cybercrime is predicted to cost $8 trillion in 2023 [4]. ...
... The relevant replies are generated using one of three models: rule-based, retrieval-based, and generative. Another classification for chatbots is based on how much human-aid 3 is included in its components. Human computation is used in at least one element of a human-aid chatbot. ...
In recent years, groups of cyber criminals/hackers have carried out cyber-attacks using various tactics with the goal of destabilizing web services in a specific context for which they are motivated. Predicting these attacks is a critical task that assists in determining what actions should be taken to mitigate the effects of such attacks and to prevent them in the future. Although there are programs to detect security concerns on the internet, there is currently no system that can anticipate or foretell whether the attacks will be successful. This research aims to develop sustain-able strategies to reduce threats, vulnerability, and data manipulation of chatbots, consequently improving cyber security. To achieve this goal, we develop a conversational chatbot, an application that uses artificial intelligence (AI) to communicate, and deploy it on social media sites (e.g., Twitter) for cyber security purposes. Chatbots have the capacity to consume large amounts of information and give an appropriate response in an efficient and timely manner, thus rendering them useful in predicting threats emanating from social media. The research utilizes sentiment analysis strategy by employing chatbots on Twitter (and analyzing Twitter data) for predicting future threats and cyber-attacks. The strategy is based on a daily collection of tweets from two types of users: those who use the platform to voice their opinions on important and relevant subjects, and those who use it to share information on cyber security attacks. The research pro-vides tools and strategies for developing chatbots that can be used for assessing cyber threats on social media through sentiment analysis leading to a global sustainable development of businesses. Future research may utilize and improvise on the tools and strategies suggested in our research to strengthen the knowledge domain of chatbots, cyber security, and social media.
... Comparing our suggested approach to related work already described in existing research literature (see e.g., [26], [17], [7]), two main differences can be identified: 1) our focus on lone wolf terrorists rather than terror organizations, and 2) our focus on semi-automated tools for supporting the analyst, rather than fully automated tools. ...
The aim of the paper is systematization of thematic areas of lone-actor terrorism (LAT). Twenty areas have been identified, some of which are more pronounced in terms of content and function. The paper proposes the definition of LAT, and concludes: a) that the mapping of research fields of lone-actor terrorism has shown a very wide scope – often very productive, theoretically and methodologically established – as well as the constant expansion of thematic fields and newly formulated problems, b) that studies of LAT provide information, models and recommendations relevant to more effective counterterrorism (CT), and c) that geopolitical determinants, strategic interests that are refracted in international relations, as well as determinants that shape cultural and civilizational relations and trends in the modern world should be more strongly included in the research apparatus and the focus of LAT research.
... Consider for example the case of Cambridge Analytica [6] which sold data -downloaded from Facebook -for a large sum, and these data were later used for political purposes. There are some further important possible applications of data "scraped" from Internet forums: they both give an insight into the way people prefer to interact regarding a given topic, but it also gives a possibility -in principle -to track down radicalizing content, or even radical/terrorist groups [7,8]. Due to recent developments nearly all social media sites have been able to prevent access to their personal pages (except, of course, for those who got individual permission from the owner of the page). ...
Humans make decisions based on the information they obtain from several major sources, among which the comments of others in Internet forums play an increasing role. Such forums cover a wide spectrum of topics and represent an essential tool in choosing the best products, manipulating views or optimizing our decisions regarding a number of aspects of our everyday life. However, many forums have extremely controversial topics and contents including those which radicalize the readers or spread information about dangerous products and ideas (e.g., drugs, weapons or aggressive ideologies). These just mentioned activities are taking place mainly on the so-called "dark web" allowing the hiding of the identity of members using dark forums. We use network theoretical approaches to analyze the data we obtained by studying the connectivity features of the members and the threads within a wide selection of forums (including dark and semi-dark) and establish several characteristic behavioral patterns. Our findings reveal both common and rather different features in the two types of behavior. In particular, we show that the various distributions of quantities, like the activity of the commenters, the dynamics of the threads (defined using their lifetime) or the degree distributions corresponding to the three major types of forums we have investigated display characteristic deviations. This knowledge can be useful, for example, in identifying an activity typical for the dark web when it appears on the public web (since the public web can be accessed and used much more easily).
... Consider for example the case of Cambridge Analytica [6] which sold data -downloaded from Facebook -for a large sum, and these data were later used for political purposes. There are 10 some further important possible applications of data "scraped" from Internet forums: they both give an insight into the way people prefer to interact regarding a given topic, but it also gives a possibility -in principle -to track down radicalizing content, or even radical/terrorist groups [7,8]. Due to recent developments nearly all social media sites have been able to prevent access to their 15 personal pages (except, of course, for those who got individual permission from the owner of the page). ...
Humans make decisions based on the information they obtain from several major sources, among which the comments of others in Internet forums play an increasing role. Such forums cover a wide spectrum of topics and represent an essential tool in choosing the best products, manipulating views or optimizing our decisions regarding a number of aspects of our everyday life. However, many forums have extremely controversial topics and contents including those which radicalize the readers or spread information about dangerous products and ideas (e.g., drugs, weapons or aggressive ideologies). These just mentioned activities are taking place mainly on the so called “dark web” allowing the hiding of the identity of members using dark forums. We use network theoretical approaches to analyze the data we obtained by studying the connectivity features of the members and the threads within a wide selection of forums (including dark and semi-dark) and establish several characteristic behavioral patterns. Our findings reveal both common and rather different features in the two types of behavior. In particular, we show that the various distributions of quantities, like the activity of the commenters, the dynamics of the threads (defined using their lifetime) or the degree distributions corresponding to the three major types of forums we have investigated display characteristic deviations. This knowledge can be useful, for example, in identifying an activity typical for the dark web when it appears in the public web (since the public web can be accessed and used much more easily).
... The author stated that data collection is a difficult task for the analysis of the network because without complete network, the analysis of the intelligent data cannot be performed. Reid et al. [41] have described five information searching and analytical approaches for terrorism. These approaches are Generalpurpose and Meta-search Engines, Terrorism Research Centers' Portal, Knowledge Analytics, Social Network Analysis and Chatterbot Techniques. ...
The complex and chaotic crisis created by terrorism demands for situation awareness which is possible with the proposed Indian Terrorism Knowledge Treasure (ITKT).
Objective: This work is an effort at creating the largest comprehensive knowledge base of terrorism and related activities, people and agencies involved, and extremist movements; and providing a platform to the society, the government and the military personnel in order to combat the evolving threat of the global menace terrorism.
Method: For representing knowledge of the domain semantically, an ontology has been used in order to better integrate data and information from multiple heterogeneous sources. An Indian Terrorism Knowledge Base is created consisting of information about past terrorist attacks, actions taken at time of those attacks, available resources and more. An Indian Terrorism Resource Manager is conceived comprising of various use cases catering to searching a specified keyword for its description, navigating the complete knowledge base of Indian Terrorism and finding any answers to any type of queries pertaining to terrorism.
Results: The managerial implications of this work are two-fold. All the involved parties, i.e., the government officials, military, police, emergency personnel, fire department, NGOs, media, public etc will be better informed in case of emergency and will be able to communicate with each other; hence improving situation awareness and providing decision support.
Keywords: Database, Resource Manager, Knowledge Treasure, Knowledge Base
... In [15], the authors implemented a counterterrorism infrastructure called the Terrorism Knowledge Discovery Project, in the context of the "Making the Nation Safer" project. This project acquired, integrated, and interpreted large amounts of terrorist information from many sources, using diverse techniques, such as knowledge-based reasoning, data integration, data science, natural language processing technologies for information extraction and multilingual retrieval, and visualization. ...
The frequency of terrorist events in Pakistan has increased considerably in the past several years. These events are frequent and not random, making it important to identify useful patterns in their occurrences to assist counterterrorism organizations. In this paper, we conducted such an analytical activity for the first time in Pakistan. We acquired data of terrorist events from reliable online sources and applied data preprocessing techniques followed by cluster analysis. Based on statistical correlation, we discovered clusters over the following combinations: (1) “Event of Terrorism—Target of Terrorism”; and (2) “Event of Terrorism—Method of Terrorism”. A more significant clustering is one which groups distinct combinations into separate clusters. We analyzed these clusters along three dimensions: (1) Annually for the time period 1988–2012; (2) for each Pakistani province; and (3) for different types of terrorist events. We also proposed a statistic for gauging the intensity of terrorism and analyzed it along the same three dimensions. Our results were extensive, but generally indicated significant Event–Target and Event–Method clusters, as well as increasing and decreasing trends in terrorism intensity. These can assist counterterrorism authorities in thwarting future attacks and arresting the responsible criminals.
... They were hidden from the eyes of law enforcement agencies and national security. They also used technologies to their benefit, using cell phones and computers to an advanced degree as the basis of their entire plan (Reid et al., 2004). Ever since the commencement of such attacks, some scholars have tried, over the years, to come up with a simple profile for such terrorists. ...
... Applying this approach in SNF context introduces the need for using Social Networks Mining (SNM) and Knowledge Discovery (KDD) techniques. [93,94,95,96,97] are of such researches. Also, finding trends and frequent (interesting) patterns through SNs is another useful task of SNM techniques for analyzing SNs [98,99]. ...
Nowadays, Social Networks (SNs) are penetrating into all areas of human life including relationships, shopping, education and so on and this growing expansion is inevitable. In addition to their invaluable benefits, due to the plethora of confidential private/corporate information in SNs, these places become the potential target for criminal/illegal activities such as identity theft, fraud, organized crimes and even terrorist attacks. To cope with such issues, it is useful to incorporate social network forensics (SNF) techniques for analyzing and surveying social interactions to detect, predict and prevent all forms of potential criminal activities. This chapter is organized in two main parts. First, SNs, their security and privacy issues are introduced and analyzed. Then, as a reference point for future studies in the field, forensics methods within SNs are explained and classified; then the related literature is reviewed.
... Applying this approach in SNF context introduces the need for using Social Networks Mining (SNM) and Knowledge Discovery (KDD) techniques. [93,94,95,96,97] are of such researches. Also, finding trends and frequent (interesting) patterns through SNs is another useful task of SNM techniques for analyzing SNs [98,99]. ...
Nowadays, Social Networks (SNs) are penetrating into all areas of human life including relationships, shopping, education and so on and this growing expansion is inevitable. In addition to their invaluable benefits, due to the plethora of confidential private/corporate information in SNs, these places become the potential target for criminal/illegal activities such as identity theft, fraud, organized crimes and even terrorist attacks. To cope with such issues, it is useful to incorporate social network forensics (SNF) techniques for analyzing and surveying social interactions to detect, predict and prevent all forms of potential criminal activities. This chapter is organized in two main parts. First, SNs, their security and privacy issues are introduced and analyzed. Then, as a reference point for future studies in the field, forensics methods within SNs are explained and classified; then the related literature is reviewed.
