Suspects Prediction towards Terrorist Attacks Based on Machine Learning

Gao, Yingying; Wang, Xianghan; Chen, Qiuyu; Guo, Yu; Yang, Qingqing; Yang, Kewei; Fang, Taosheng

doi:10.1109/bigdia.2019.8802726

Cited by 6 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Place, the type, the terrorist group, or the target SVM [13], Decision tree [58], Random forest [59], and KNN [4]. Agarwal,et al [60] (2019) GTD Attacktype, nkill, Timestamp Support Vector Machine [13], Random Forest [59], and Logistic Regression [24] Gao, et al [61] (2019)…”

Section: Terrorist Attack Detection Methodsmentioning

confidence: 99%

Detection and Prediction of Terrorist Activities and Threatening Events in Twitter - A Survey

Leenuse,

Pankaj,

2023

2023 International Conference on Control, Communication and Computing (ICCC)

View full text Add to dashboard Cite

Twitter is one of the prominent social media applications which influence civilians due to its fast information dissemination. Researchers have shown that the majority of the tweets posted involve the current issues prevailing in society. Terrorism is a global threat to the peaceful existence of humanity. Terrorists widely use online social media platforms to communicate and propagate their activities. User-to-user interaction leads a major role in the formation of complex communication in the terrorism domain. This paper aims to discuss the different methods in literature used to identify both single and groups of users who are related to terrorist activities. The study focuses on the different techniques for identifying threatening activities, detecting terrorist attacks and predicting terrorist attacks.

show abstract

Section: Terrorist Attack Detection Methodsmentioning

confidence: 99%

Detection and Prediction of Terrorist Activities and Threatening Events in Twitter - A Survey

Leenuse,

Pankaj,

2023

2023 International Conference on Control, Communication and Computing (ICCC)

View full text Add to dashboard Cite

show abstract

“…Gao et al [5] also used the GTD to compare five classification ML algorithms: the decision tree (DT), LR, a Gaussian Bayesian Network (GBN), RF and AdaBoost. The experiment results showed that classification based on the DT had the highest precision at 94.8%.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The experiment results showed that classification based on the DT had the highest precision at 94.8%. Moreover, the GBN could list all the possibilities according to the probabilities and showed 94.7% of the results [5]. In another study, Mehmood et al [2] acquired data concerning terrorism in Pakistan between 1998 and 2012 from the SATP.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Acts of terrorism encompass a broad range of activities, including criminal communication, planning, transportation, logistics, reconnaissance of targets, harbouring, providing materials and items such as weapons, and supporting other relevant materials and finances. Acts of terrorism by extremist or terrorist organisations create psychological pressure amongst civilians and governments; the military, due to the casualties; and property losses, social unrest, and economic disruption [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning

Mahmood¹,

Khan²

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

This study proposes an architecture for the prediction of extremist human behaviour from projected suicide bombings. By linking 'dots' of police data comprising scattered information of people, groups, logistics, locations, communication, and spatiotemporal characters on different social media groups, the proposed architecture will spawn beneficial information. This useful information will, in turn, help the police both in predicting potential terrorist events and in investigating previous events. Furthermore, this architecture will aid in the identification of criminals and their associates and handlers. Terrorism is psychological warfare, which, in the broadest sense, can be defined as the utilisation of deliberate violence for economic, political or religious purposes. In this study, a supervised learning-based approach was adopted to develop the proposed architecture. The dataset was prepared from the suicide bomb blast data of Pakistan obtained from the South Asia Terrorism Portal (SATP). As the proposed architecture was simulated, the supervised learning-based classifiers naïve Bayes and Hoeffding Tree reached 72.17% accuracy. One of the additional benefits this study offers is the ability to predict the target audience of potential suicide bomb blasts, which may be used to eliminate future threats or, at least, minimise the number of casualties and other property losses.

show abstract

“…These groups constitute violent criminal attacks of state and non-state actors in their covert and overt interdependence [3]. It has the highest negative social and economic consequence to any nation as it hinders the economic development of any nation and degrades its gross domestic products [4]. Attacks of terrorist have great lethal and destructive impact on every nation in economic terms of lives and property.…”

Section: Introductionmentioning

confidence: 99%

Computational Robotics: An Alternative Approach for Predicting Terrorist Networks

Nwanga¹,

Okafor

Chukwudebe

et al. 2021

Int. J. Robot. Autom. Technol.

View full text Add to dashboard Cite

Increasing terrorist activities globally have attracted the attention of many researchers, policy makers and security agencies towards counterterrorism. The clandestine nature of terrorist networks have made them difficult for detection. Existing works have failed to explore computational characterization to design an efficient threat-mining surveillance system. In this paper, a computationally-aware surveillance robot that auto-generates threat information, and transmit same to the cloud-analytics engine is developed. The system offers hidden intelligence to security agencies without any form of interception by terrorist elements. A miniaturized surveillance robot with Hidden Markov Model (MSRHMM) for terrorist computational dissection is then derived. Also, the computational framework for MERHMM is discussed while showing the adjacency matrix of terrorist network as a determinant factor for its operation. The model indicates that the terrorist network have a property of symmetric adjacency matrix while the social network have both asymmetric and symmetric adjacency matrix. Similarly, the characteristic determinant of adjacency matrix as an important operator for terrorist network is computed to be -1 while that of a symmetric and an asymmetric in social network is 0 and 1 respectively. In conclusion, it was observed that the unique properties of terrorist networks such as symmetric and idempotent property conferred a special protection for the terrorist network resilience. Computational robotics is shown to have the capability of utilizing the hidden intelligence in attack prediction of terrorist elements. This concept is expected to contribute in national security challenges, defense and military intelligence.

show abstract

Suspects Prediction towards Terrorist Attacks Based on Machine Learning

Cited by 6 publications

References 5 publications

Detection and Prediction of Terrorist Activities and Threatening Events in Twitter - A Survey

Detection and Prediction of Terrorist Activities and Threatening Events in Twitter - A Survey

Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning

Computational Robotics: An Alternative Approach for Predicting Terrorist Networks

Contact Info

Product

Resources

About