Legal Analysis and Legislative Challenges of Artificial Intelligence in the Prevention and Counteraction of Computer Crimes and the Enhancement of Cybersecurity
Keywords:
network security, artificial intelligence, phishing, cyber security, computer crimesAbstract
Computer crimes are among the prominent issues in the field of criminal law, and the importance of combating them—particularly in today’s digital world—is becoming increasingly evident. In this regard, artificial intelligence (AI) is remarkably influential and can lead to profound transformations in computer technologies and cyberspace. The primary aim of this article is to examine how AI can play an effective role in countering computer crimes and improving cybersecurity performance. Based on research findings, given the limitations of human capabilities as well as the significant advancements in intelligent computer viruses and worms, the design of intelligent systems using advanced sensors and algorithms to combat computer crimes can be an effective step. One of the areas in which AI can strengthen cybersecurity is in the detection and identification of crimes. This technology, in addition to identifying threats, is also capable of preventing the occurrence of computer crimes. AI can enhance cybersecurity in various domains, including the detection of cyberattacks and threats such as botnets, the identification of spam and phishing attacks, combating fake accounts, protecting user information and data, authentication processes and fraud detection in authentication, monitoring user activities, and ensuring application security. Collectively, these measures can play a significant role in improving the safety of cyberspace. However, the use of AI to enhance cybersecurity also faces challenges. One of the major issues is the lack of transparency in AI decision-making processes, which may raise legal and ethical questions and challenges. Additionally, the requirement for large volumes of data to train algorithms and the necessity of human participation in decision-making processes are among the other challenges of this technology. Furthermore, the influence of various factors and complex variables in AI systems may cause difficulties in analyzing and predicting security threats. Ultimately, this article examines the legal dimensions and legislative challenges associated with AI in the prevention and counteraction of computer crimes and the enhancement of cybersecurity, and it proposes possible solutions to address these challenges.
References
Adekunle, Y. (2019). Holistic exploration of gaps vis-à-vis artificial intelligence in automated teller machine and internet banking. International Journal of Applied Information Systems, 2(25), 12.
Ali, M. H., Abbas, B., Al, D., Ismail, A., & Zolkipli, M. F. (2017). A New Intrusion Detection System Based on Fast Learning Network and Particle Swarm Optimization. IEEE Access, 6, 232-261. https://doi.org/10.1109/ACCESS.2018.2820092
Bahramizadeh, A. H., & Sadeghzadeh, S. (2022). Artificial intelligence and challenges in ensuring cybersecurity. Proceedings of the 16th National Conference on Computer Science and Information Technology, Mazandaran, Iran.
Bakhshi, B., & Veisi, H. (2019). End to end fingerprint verification based on convolutional neural network. 27th Iranian Conference on Electrical Engineering (ICEE), USA.
Barth, A., Rubinstein, B. I. P., Sundararajan, M., Mitchell, J. C., Song, D., & Bartlett, P. L. (2012). A Learning-Based Approach to Reactive Security. Ieee Transactions on Dependable and Secure Computing, 9(4), 482-490. https://doi.org/10.1109/TDSC.2011.42
Bowman, B., & Huang, H. H. (2019). Securing malware cognitive systems against adversarial attacks. 17th IEEE International Conference on Cognitive Computing (ICCC), USA.
Brenner, S. W. (2010). Cybercrime: Criminal Threats from Cyberspace. Greenwood Publishing Group. https://doi.org/10.5040/9798400636554
Calzavara, S., Tolomei, G., Casini, A., Bugliesi, M., & Orlando, S. (2015). A Supervised Learning Approach to Protect Client Authentication on the Web. Acm Transactions on the Web, 9(1), 1-30. https://doi.org/10.1145/2754933
Chang, C., T, E., & Obando Carbajal, L. E. (2016). Biometric authentication by keystroke dynamics for remote evaluation with one-class classification. Advances in Artificial Intelligence: 29th Canadian Conference on Artificial Intelligence, Canada.
Ghorbanpour Vishkasouqi, S., & Mirazimi Tabalvandani, S. A. (2023). A comprehensive guide to the applications of artificial intelligence in cybersecurity. Proceedings of the 11th National Conference on Novel Ideas in Humanities and Engineering, Rasht, Iran.
Gordon, S., & Ford, R. (2006). On the definition and classification of cybercrime. Journal in Computer Virology, 2(1), 13-20. https://doi.org/10.1007/s11416-006-0015-z
Gou, X., Jin, W., & Zhao, D. (2006). Multi-agent system for worm detection and containment in metropolitan area networks. Journal of Electronics, 23(2), 259-265. https://doi.org/10.1007/s11767-004-0111-5
Guan, Y., & Ge, X. (2017). Distributed attack detection and secure estimation of networked cyber-physical systems against false data injection attacks and jamming attacks. IEEE Transactions on Signal and Information Processing over Networks, 4(1), 48-59. https://doi.org/10.1109/TSIPN.2017.2749959
Keshavarz, Z., & Hosseini, H. R. (2023). Artificial intelligence in cybersecurity: Applications, challenges, and opportunities. Proceedings of the 6th National Conference on New Technologies in Electrical, Computer, and Mechanical Engineering in Iran, Tehran, Iran.
Kleinmann, A., & Wool, A. (2016). Automatic Construction of Statechart-Based Anomaly Detection Models for MultiThreaded Industrial Control Systems. International Publication, 8(4), 1-21. https://doi.org/10.1145/3011018
Kotenko, I., & Ulanov, A. (2007). Multi-Agent Framework for Simulation of Adaptive Cooperative Defense Against Internet Attacks. International Workshop on Autonomous Intelligent Systems Agents and Data Mining,
Kowert, W. (2017). The foreseeability of human-artificial intelligence interactions. Texas Law Review, 96(1), 181.
Lebbe, M. A., Agbinya, J. I., Chaczko, Z., & Chiang, F. (2007). Self-Organized Classification of Dangers for Secure Wireless Mesh Networks. Australasian Telecommunication Networks and Applications Conference, Australia.
Nunes, D. S., Zhang, P., & S.S, J. (2015). A survey on human-in-the-loop applications towards an internet of all. Ieee Communications Surveys & Tutorials, 17(2), 944-965. https://doi.org/10.1109/COMST.2015.2398816
Ojugo, A. A., Eboka, A. O., Okonta, O. E., Yoro, R. E., & Aghware, F. O. (2012). Genetic Algorithm Rule-Based Intrusion Detection System (GAIDS). Journal of Emerging Trends in Computing and Information Sciences, 3(8), 118-119.
Phillips, L., Link, H., Smith, R., & Weiland, L. (2006). Agent-Based Control of Distributed Infrastructure Resources. USA Department of Energy (Sandia National Laboratories).
Regev, O. (2009). On lattices, learning with errors, random linear codes and cryptography. Journal of the ACM, 56(6), 1-43. https://doi.org/10.1145/1568318.1568324
Schlegel, U. (2019). Towards a rigorous evaluation of XAI methods on time series. International Conference on Computer Vision Workshop (ICCVW), New York, USA.
Singh, K. (2017). Sparse proximity based robust fingerprint recognition. International Conference on Computing, Communication and Automation (ICCCA), England.
Sirisanyalak, B., & Sornil, O. (2007). An artificial immunity-based spam detection system. IEEE Congress on Evolutionary Computation (CEC 2007), USA.
Tsai, C. F., Hsu, Y. F., Lin, C. Y., & Lin, W. Y. (2009). Intrusion detection by machine learning: A review. Expert Systems with Applications, 36, 11994-12333. https://doi.org/10.1016/j.eswa.2009.05.029
Xiao, R. (2018). Attacking network isolation in software-defined networks: New attacks and countermeasures. International Conference on Computer Communication and Networks (ICCCN), Japan.
Zhu, Y., & Tan, Y. (2011). A local-concentration-based feature extraction approach for spam filtering. Ieee Transactions on Information Forensics and Security, 6(2), 486-490. https://doi.org/10.1109/TIFS.2010.2103060
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