Cyber Intelligence in Counterterrorism: AI-Powered Detection of Encrypted Jihadist Threats.
Encryption offers a contradictory challenge of securely enabling communication yet allowing terrorist organizations, and particularly the jihadist organizations, to evade international counterterrorist surveillance systems. Such groups make extensive use of encrypted technologies for coordination of...
| Publicado en: | Journal of Human Security (Librello Publishing House) Vol. 21; no. 1; pp. 1 - 7 |
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| Formato: | Artículo |
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Librello Publishing House
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=187722731&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 187722731 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 18353800 ESYK jtl: Journal of Human Security (Librello Publishing House) issn: 18353800 maglogo: N pubinfo: dt: 2025 vid: 21 iid: 1 pid: 83753 pub: Librello Publishing House artinfo: ui: 187722731 10.12924/johs2025.210101 ppf: 1 ppct: 6 formats: fmt: @attributes: type: P size: 1MB tig: atl: Cyber Intelligence in Counterterrorism: AI-Powered Detection of Encrypted Jihadist Threats. aug: au: Berzinji, Ala affil: Department of Computer Science, College of Science, University of Sulaimani, Sulaimani, Iraq Stockholm University, Department of Computer and System Sciences Cyber Security Department, IQ Group Holding, Sulaymaniyah 46001, Iraq su: Artificial intelligence Cyberterrorism Natural language processing Machine learning Cyber intelligence (Computer security) Deep learning Counterterrorism sug: subj: Artificial intelligence Cyberterrorism Natural language processing Machine learning Cyber intelligence (Computer security) Deep learning Counterterrorism keyword: AI Anomaly Detection Cybersecurity Deep Learning Encrypted Communications Encryption Analysis Jihadist Networks Machine Learning Natural Language Processing AI Anomaly Detection Cybersecurity Deep Learning Encrypted Communications Encryption Analysis Jihadist Networks Machine Learning Natural Language Processing ab: Encryption offers a contradictory challenge of securely enabling communication yet allowing terrorist organizations, and particularly the jihadist organizations, to evade international counterterrorist surveillance systems. Such groups make extensive use of encrypted technologies for coordination of actions, sharing of propaganda, and illegal activities to avoid regular intelligence and cybersecurity standards. This paper discusses the innovative artificial intelligence (AI) and machine learning (ML) techniques for identifying and deciphering encrypted communications employed by the jihadist networks. A real-time analysis framework is proposed that uses deep learning models, unsupervised learning methods, and tools of natural language processing (NLP). To ensure replicability and practical implementation, a generalised algorithmic structure accompanied by pseudocode is included. The proposed system is evaluated using datasets derived from simulations, authentic scenarios, and extremist digital platforms, annotated through manual and automated methods. Comprehensive experimental results indicate that hybrid ML models can reliably flag suspicious communication patterns using only metadata, packet dimensions, and traffic flow characteristics, thus obviating the necessity for content access. The findings highlight AI's capacity to furnish intelligence agencies with novel capabilities for the proactive identification of encrypted terrorist activities, thereby reinforcing counterterrorism operations. The study further stresses the importance of continuous model refinement and ethical governance for responsible and effective deployment. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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