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اصغری, حامد, مدیری, مهدی. (1404). راهبرد افزایش امنیت زیرساخت‌های حیاتی با هوش مصنوعی. پدافند الکترونیکی و سایبری, 16(3), 1-20.
حامد اصغری; مهدی مدیری. "راهبرد افزایش امنیت زیرساخت‌های حیاتی با هوش مصنوعی". پدافند الکترونیکی و سایبری, 16, 3, 1404, 1-20.
اصغری, حامد, مدیری, مهدی. (1404). 'راهبرد افزایش امنیت زیرساخت‌های حیاتی با هوش مصنوعی', پدافند الکترونیکی و سایبری, 16(3), pp. 1-20.
اصغری, حامد, مدیری, مهدی. راهبرد افزایش امنیت زیرساخت‌های حیاتی با هوش مصنوعی. پدافند الکترونیکی و سایبری, 1404; 16(3): 1-20.

راهبرد افزایش امنیت زیرساخت‌های حیاتی با هوش مصنوعی

پدافند غیرعامل
دوره 16، شماره 3 - شماره پیاپی 63، مهر 1404، صفحه 1-20    اصل مقاله (1014.93 K)
نوع مقاله: مقاله پژوهشی
نویسندگان
حامد اصغری1؛ مهدی مدیری* 2
1کارشناسی ارشد مدیریت بحران مجتمع دانشگاهی مهندسی و پدافند غیر عامل، دانشگاه صنعتی مالک اشتر، تهران، ایران
2استاد مجتمع دانشگاهی مهندسی و پدافند غیر عامل، دانشگاه صنعتی مالک‌اشتر، تهران، ایران
تاریخ دریافت: 04 آذر 1403،  تاریخ بازنگری: 28 اردیبهشت 1404،  تاریخ پذیرش: 29 شهریور 1404 
چکیده
زیرساخت حیاتی (CI) شامل سامانه‌ها، دارایی‌ها و خدمات حیاتی است که برای عملکرد و رفاه جامعه و اقتصاد ضروری هستند. با این حال، رشد سریع تهدیدات سایبری در محیط‌های دیجیتال خطرات جدی برای کارایی این زیرساخت‌ها ایجاد می‌کند و چالش‌های قابل توجهی برای امنیت عمومی، ثبات اقتصادی و امنیت ملی به همراه دارد. این وضعیت نیاز به راه‌حل‌های مؤثر در زمینه امنیت سایبری را، به‌ویژه در حوزه اتوماسیون و تصمیم‌گیری هوشمند، تقویت می‌کند؛ جایی که مدل‌سازی مبتنی بر هوش مصنوعی می‌تواند نقش کلیدی ایفا کند. در این راستا، بحث ما بر مقایسه سامانه‌های هوش مصنوعی مبتنی بر قانون متمرکز است، زیرا این رویکرد از شفافیت، تفسیرپذیری و قابلیت اعتماد بالاتری نسبت به روش‌های یادگیری عمیق برخوردار است و امکان بررسی و اعتبارسنجی تصمیمات را توسط تحلیلگران انسانی فراهم می‌سازد؛ امری که در حوزه امنیت سایبری حیاتی و گریزناپذیر است. این پژوهش به تحلیل هوش مصنوعی مبتنی بر قوانین چندلایه می‌پردازد که هدف آن تسهیل تصمیمات قابل فهم انسانی در کنار فرآیندهای خودکار برای زیرساخت‌های حیاتی است. علاوه بر این، انواع روش‌های تولید قانون را دسته‌بندی کرده و رویکردهای مبتنی بر دانش و داده برای استخراج بینش‌های معنادار از اطلاعات بررسی می‌شود. این فهم به تحلیلگران امنیتی کمک می‌کند تا تهدیدات را شناسایی کرده، حملات را بررسی کنند و تصمیمات آگاهانه‌ای در بخش‌های مختلف بگیرند. همچنین به این موضوع پرداخته می شود که چگونه این روش‌ها می‌توانند به چالش‌های امنیت سایبری در بخش‌های حیاتی مانند انرژی، دفاع، حمل‌ونقل، بهداشت، منابع آب و کشاورزی پاسخ دهند و در نتیجه به بهبود تدابیر امنیتی کمک کنند. این مقاله با شناسایی چالش‌های موجود، فرصت‌های تحقیقاتی آینده و ارائه راهبردهای نوآورانه برای مقابله با تهدیدات سایبری آینده به پایان می‌رسد.
 
کلیدواژه‌ها
هوش مصنوعی مبتنی بر قوانین؛ امنیت سایبری؛ زیرساخت‌های حیاتی؛ مدل‌سازی تصمیم‌گیری هوشمند؛ تحلیل حملات
عنوان مقاله [English]
Strategy for Enhancing the Security of Critical Infrastructure Using Artificial Intelligence
نویسندگان [English]
HAMED ASGHARI1؛ Mehdi Modiri2
1Master's degree in crisis management, non-operating engineering and defense university complex, Malik Ashtar University of Technology
2Professor at the Engineering and Passive Defense Academic Complex, Malek Ashtar University of Technology
چکیده [English]
Critical Infrastructure (CI) includes the essential systems, assets, and services that are vital for the functioning and well-being of society and the economy. However, the rapid growth of cyber threats in digital environments poses serious risks to the efficiency of these infrastructures and presents significant challenges to public safety, economic stability, and national security. This growing threat landscape highlights the urgent need for effective cybersecurity solutions, particularly in the domains of automation and intelligent decision-making, where AI-based modeling can play a crucial role. In this regard, our discussion focuses on comparing rule-based artificial intelligence systems, as this approach offers greater transparency, interpretability, and trustworthiness compared to deep learning methods, enabling human analysts to examine and validate decisions—a critical and unavoidable requirement in cybersecurity. This research analyzes multi-layer rule-based AI systems designed to facilitate human-understandable decision-making alongside automated processes in critical infrastructure environments. It also categorizes various rule generation techniques and explores both knowledge-based and data-driven approaches for extracting meaningful insights from data. Such insights empower security analysts to identify threats, investigate attacks, and make informed decisions across various sectors. Furthermore, the study examines how these methods can address cybersecurity challenges in key sectors such as energy, defense, transportation, healthcare, water resources, and agriculture, thereby contributing to the enhancement of security measures. The paper concludes by identifying existing challenges, outlining future research opportunities, and proposing innovative strategies for countering emerging cyber threats.
کلیدواژه‌ها [English]
Rule-based Artificial Intelligence, Cybersecurity, Critical Infrastructure, Intelligent Decision-Making Modeling, Attack Analysis
مراجع
  • A. Argyroudis et al., “Digital technologies can enhance climate resilience of critical infrastructure,” Climate Risk Management, vol. 35, p. 100387, 2022, doi: https://doi.org/10.1016/j.crm.2021.100387.
  • Chen, Y. Jiang, E. Wang, Y. Wang, and J. Zhang, “Measuring Urban Infrastructure Resilience via Pressure-State-Response Framework in Four Chinese Municipalities,” Applied Sciences, vol. 12, no. 6, p. 2819, Mar. 2022, doi: https://doi.org/10.3390/app12062819.
  • Mishra, Y. I. Alzoubi, M. J. Anwar, and A. Q. Gill, “Attributes impacting cybersecurity policy development: An evidence from seven nations,” Computers & Security, vol. 120, no. 1, p. 102820, Sep. 2022, doi: https://doi.org/10.1016/j.cose.2022.102820.
  • Alqudhaibi, M. Albarrak, A. Aloseel, S. Jagtap, and K. Salonitis, “Predicting Cybersecurity Threats in Critical Infrastructure for Industry 4.0: A Proactive Approach Based on Attacker Motivations,” Sensors, vol. 23, no. 9, p. 4539, Jan. 2023, doi: https://doi.org/10.3390/s23094539.
  • A. Samson, “Exploring security, performance and privacy in the internet of things: A comprehensive survey,” GSC Advanced Research and Reviews, vol. 21, no. 1, pp. 280–319, Oct. 2024, doi: https://doi.org/10.30574/gscarr.2024.21.1.0388.
  • Oluwatobiloba Okusi, “Leveraging AI and Machine Learning for the Protection of Critical National Infrastructure,” Asian Journal of Research in Computer Science, vol. 17, no. 10, pp. 1–11, Sep. 2024, doi: https://doi.org/10.9734/ajrcos/2024/v17i10505.
  • ‌I. H. Sarker, A. S. M. Kayes, S. Badsha, H. Alqahtani, P. Watters, and A. Ng, “Cybersecurity data science: an overview from machine learning perspective,” Journal of Big Data, vol. 7, no. 1, Jul. 2020, Available: https://link.springer.com/article/10.1186/s40537-020-00318-5.
  • Ajala, C. Okoye, None Onyeka Chrisanctus Ofodile, A. Arinze, and D. Daraojimba, “Review of AI and machine learning applications to predict and Thwart cyber-attacks in real-time,” Magna Scientia Advanced Research and Reviews, vol. 10, no. 1, pp. 312–320, Feb. 2024, doi: https://doi.org/10.30574/msarr.2024.10.1.0037.
  • Merve Ozkan-Ozay et al., “A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions,” IEEE Access, vol. 12, pp. 12229–12256, Jan. 2024, doi: https://doi.org/10.1109/access.2024.3355547.
  • Yu et al., “Securing Critical Infrastructures: Deep-Learning-Based Threat Detection in IIoT,” IEEE Communications Magazine, vol. 59, no. 10, pp. 76–82, Oct. 2021, doi: https://doi.org/10.1109/MCOM.101.2001126.
  • H. Sarker, “Machine Learning for Intelligent Data Analysis and Automation in Cybersecurity: Current and Future Prospects,” Annals of Data Science, vol. 10, pp. 1473–1498, Sep. 2022, doi: https://doi.org/10.1007/s40745-022-00444-2.
  • H. Sarker, H. Janicke, Mohamed Amine Ferrag, and Alsharif Abuadbba, “Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions toward automation, intelligence and transparent cybersecurity modeling for critical infrastructures,” Internet of Things, pp. 101110–101110, Feb. 2024, doi: https://doi.org/10.1016/j.iot.2024.101110.
  • H. Sarker, M. H. Furhad, and R. Nowrozy, “AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions,” SN Computer Science, vol. 2, no. 3, Mar. 2021, doi: https://doi.org/10.1007/s42979-021-00557-0.
  • H. Sarker, “Multi aspects AI based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview,” SECURITY AND PRIVACY, vol. 6, no. 5, Jan. 2023, doi: https://doi.org/10.1002/spy2.295.
  • A. Sheikh, Y. Singh, P. K. Singh, and K. Z. Ghafoor, “Intelligent and secure framework for critical infrastructure (CPS): Current trends, challenges, and future scope,” Computer Communications, vol. 193, pp. 302–331, Sep. 2022, doi: https://doi.org/10.1016/j.comcom.2022.07.007.
  • V. Gheorghe, D. V. Vamanu, P. F. Katina, and R. Pulfer, Critical Infrastructures, Key Resources, Key Assets. Springer Nature (Netherlands), 2018. doi: https://doi.org/10.1007/978-3-319-69224-1.
  • Kok, A. Martinetti, and J. Braaksma, “The impact of integrating information technology with operational technology in physical assets: a literature review,” IEEE Access, pp. 1–1, Jan. 2024, doi: https://doi.org/10.1109/access.2024.3442443.
  • [18] Stouffer, “Guide to Operational Technology (OT) Security,” Jan. 2023, doi: https://doi.org/10.6028/nist.sp.800-82r3.
  • Knowles, D. Prince, D. Hutchison, J. F. P. Disso, and K. Jones, “A survey of cyber security management in industrial control systems,” International Journal of Critical Infrastructure Protection, vol. 9, pp. 52–80, Jun. 2015, doi: https://doi.org/10.1016/j.ijcip.2015.02.002.
  • Bhamare, M. Zolanvari, A. Erbad, R. Jain, K. Khan, and N. Meskin, “Cybersecurity for industrial control systems: A survey,” Computers & Security, vol. 89, p. 101677, Feb. 2020, doi: https://doi.org/10.1016/j.cose.2019.101677.
  • Yadav and K. Paul, “Architecture and security of SCADA systems: A review,” International Journal of Critical Infrastructure Protection, vol. 34, p. 100433, Sep. 2021, doi: https://doi.org/10.1016/j.ijcip.2021.100433.
  • Abdullahi et al., “Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review,” Electronics, vol. 11, no. 2, p. 198, Jan. 2022, doi: https://doi.org/10.3390/electronics11020198.
  • ‌ A. Djenna, S. Harous, and D. E. Saidouni, “Internet of Things Meet Internet of Threats: New Concern Cyber Security Issues of Critical Cyber Infrastructure,” Applied Sciences, vol. 11, no. 10, p. 4580, May 2021, doi: https://doi.org/10.3390/app11104580.
  • F. Safitra, M. Lubis, and H. Fakhrurroja, “Counterattacking Cyber Threats: A Framework for the Future of Cybersecurity,” Sustainability, vol. 15, no. 18, p. 13369, Jan. 2023, doi: https://doi.org/10.3390/su151813369.
  • Lehto, “Cyber-Attacks Against Critical Infrastructure,” Computational Methods in Applied Sciences, vol. 56, pp. 3–42, 2022, doi: https://doi.org/10.1007/978-3-030-91293-2_1.
  • C. Tok and S. Chattopadhyay, “Identifying threats, cybercrime and digital forensic opportunities in Smart City Infrastructure via threat modeling,” Forensic Science International: Digital Investigation, vol. 45, p. 301540, Jun. 2023, doi: https://doi.org/10.1016/j.fsidi.2023.301540.
  • H. Sarker, “AI for Critical Infrastructure Protection and Resilience,” pp. 153–172, Jan. 2024, doi: https://doi.org/10.1007/978-3-031-54497-2_9.
  • ‌ Govea, W. Gaibor-Naranjo, and W. Villegas-Ch, “Transforming Cybersecurity into Critical Energy Infrastructure: A Study on the Effectiveness of Artificial Intelligence,” Systems, vol. 12, no. 5, p. 165, May 2024, doi: https://doi.org/10.3390/systems12050165.
  • Roshanaei, M. R. Khan, and N. N. Sylvester, “Enhancing Cybersecurity through AI and ML: Strategies, Challenges, and Future Directions,” Journal of Information Security, vol. 15, no. 3, pp. 320–339, May 2024, doi: https://doi.org/10.4236/jis.2024.153019.
  • ‌ W. Ding, M. Abdel-Basset, H. Hawash, and A. M. Ali, “Explainability of Artificial Intelligence Methods, Applications and Challenges: A Comprehensive Survey,” Information Sciences, Oct. 2022, doi: https://doi.org/10.1016/j.ins.2022.10.013.
  • ‌ X. Gu, J. Han, Q. Shen, and P. Angelov, “Autonomous learning for fuzzy systems: a review,” Artificial Intelligence Review, vol. 56, no. 8, pp. 7549–7595, Dec. 2022, doi: https://doi.org/10.1007/s10462-022-10355-6.
  • ‌ Butala and A. Sluga, “Autonomous Work Systems in Manufacturing Networks,” Jan. 2006, doi: https://doi.org/10.1016/s0007-8506(07)60473-9.
  • ‌ A. Dolunay, F. Kasap, and G. Keçeci, “Freedom of Mass Communication in the Digital Age in the Case of the Internet: ‘Freedom House’ and the USA Example,” Sustainability, vol. 9, no. 10, p. 1739, Oct. 2017, doi: https://doi.org/10.3390/su9101739.
  • Tsiknas, D. Taketzis, K. Demertzis, and C. Skianis, “Cyber Threats to Industrial IoT: A Survey on Attacks and Countermeasures,” IoT, vol. 2, no. 1, pp. 163–186, Mar. 2021, doi: https://doi.org/10.3390/iot2010009.
  • A. Adelani, E. S. Okafor, B. S. Jacks, and O. A. Ajala, “THEORETICAL FRAMEWORKS FOR THE ROLE OF AI AND MACHINE LEARNING IN WATER CYBERSECURITY: INSIGHTS FROM AFRICAN AND U.S. APPLICATIONS,” Computer Science & IT Research Journal, vol. 5, no. 3, pp. 681–692, Mar. 2024, doi: https://doi.org/10.51594/csitrj.v5i3.928.
  • Pollini et al., “Leveraging human factors in cybersecurity: an integrated methodological approach,” Cognition, Technology & Work, vol. 24, no. 2, Jun. 2021, doi: https://doi.org/10.1007/s10111-021-00683-y.
  • Mehmood, G. Epiphaniou, C. Maple, N. Ersotelos, and R. Wiseman, “A Hybrid Methodology to Assess Cyber Resilience of IoT in Energy Management and Connected Sites,” Sensors, vol. 23, no. 21, p. 8720, Jan. 2023, doi: https://doi.org/10.3390/s23218720.
  • Kim, Myung kil Ahn, S. Lee, D. Lee, M. Park, and D. Shin, “Improved Cyber Defense Modeling Framework for Modeling and Simulating the Lifecycle of Cyber Defense Activities,” IEEE Access, vol. 11, pp. 114187–114200, Jan. 2023, doi: https://doi.org/10.1109/access.2023.3324901.
  • AlDaajeh, H. Saleous, S. Alrabaee, E. Barka, F. Breitinger, and K.-K. Raymond Choo, “The role of national cybersecurity strategies on the improvement of cybersecurity education,” Computers & Security, vol. 119, no. 1, p. 102754, Aug. 2022, doi: https://doi.org/10.1016/j.cose.2022.102754.
  • Bada and J. R. C. Nurse, “The social and psychological impact of cyberattacks,” Emerging Cyber Threats and Cognitive Vulnerabilities, pp. 73–92, 2020, doi: https://doi.org/10.1016/b978-0-12-816203-3.00004-6.
  • F. Franco, F. Künzler, J. von der Assen, C. Feng, and B. Stiller, “RCVaR: An economic approach to estimate cyberattacks costs using data from industry reports,” Computers & Security, vol. 139, p. 103737, Apr. 2024, doi: https://doi.org/10.1016/j.cose.2024.103737.
  • Arif, A. Kumar, M. Fahad, and H. K. Hussain, “Future Horizons: AI-Enhanced Threat Detection in Cloud Environments: Unveiling Opportunities for Research,” International Journal of Multidisciplinary Sciences and Arts, vol. 2, no. 2, pp. 242–251, 2023, doi: https://doi.org/10.47709/ijmdsa.v2i2.3452.
  • K. Vashishth, V. Sharma, K. K. Sharma, S. Vidyant, and A. Bhardwaj, “Future Trends and Challenges in Pharmaceutical Microbiology with the Integration of Artificial Intelligence,” Advances in Medical Technologies and Clinical Practice, pp. 315–344, Sep. 2024, doi: https://doi.org/10.4018/979-8-3693-3212-2.ch012.
  • Krause, “Generative AI in FinTech: Transforming Financial Activities through Advanced Technologies,” Jan. 2024, doi: https://doi.org/10.2139/ssrn.4923224.
  • Pfeiffer, J. F. Lachenmaier, O. Hinz, and van, “New Laws and Regulation,” Business & Information Systems Engineering, Oct. 2024, doi: https://doi.org/10.1007/s12599-024-00902-6.
  • ‌ Shepherd and A. A. Jacob, “A Detailed Investigation on Digital Technology and AI in Social Sectors,” Advances in computational intelligence and robotics book series, pp. 33–62, Sep. 2024, doi: https://doi.org/10.4018/979-8-3693-5533-6.ch002.
  • ‌ Roumen Trifonov, Evgeni Sabev, G. Pavlova, and Kamelia Raynova, “Analysis of deep learning methods for cybersecurity in industry 4.0,” AIP conference proceedings, Jan. 2024, doi: https://doi.org/10.1063/5.0193712.
  • Wang, P. Liu, J. Huang, H. Bin, X. Wang, and H. Zhu, “KnowCTI: Knowledge-based Cyber threat intelligence entity and relation extraction,” Computers & security, vol. 141, pp. 103824–103824, Jun. 2024, doi: https://doi.org/10.1016/j.cose.2024.103824.
  • AI-Powered Data-Driven Cybersecurity Techniques: Boosting Threat Identification and Reaction,” Nanotechnology Perceptions, vol. 20, no. S10, Aug. 2024, doi: https://doi.org/10.62441/nano-ntp.v20is10.25.
  • Al-Quayed, Z. Ahmad, and M. Humayun, “A situation based predictive approach for cybersecurity intrusion detection and prevention using machine learning and deep learning algorithms in wireless sensor networks of industry 4.0,” IEEE access, pp. 1–1, Jan. 2024, doi: https://doi.org/10.1109/access.2024.3372187.
  • N. Halgamuge, “Leveraging Deep Learning to Strengthen the Cyber-Resilience of Renewable Energy Supply Chains: A Survey,” IEEE Communications Surveys and Tutorials, pp. 1–1, Jan. 2024, doi: https://doi.org/10.1109/comst.2024.3365076.
  • C. Phillips, S. Taylor, M. Boniface, S. Modafferi, and M. Surridge, “Automated Knowledge-Based Cybersecurity Risk Assessment of Cyber-Physical Systems,” IEEE Access, pp. 1–1, Jan. 2024, doi: https://doi.org/10.1109/access.2024.3404264.
  • Z. Kolk, S. Ruipérez-Campillo, A. A. Wilde, R. E. Knops, S. M. Narayan, and F. V. Tjong, “Prediction of sudden cardiac death using artificial intelligence: Current status and future directions,” Heart Rhythm, Sep. 2024, doi: https://doi.org/10.1016/j.hrthm.2024.09.003.
  • Obi, V. Akagha, S. Onimisi, A. Chigozie, None Shedrack Onwusinkwue, and A. Ibrahim, “COMPREHENSIVE REVIEW ON CYBERSECURITY: MODERN THREATS AND ADVANCED DEFENSE STRATEGIES,” Computer science & IT research journal, vol. 5, no. 2, pp. 293–310, Feb. 2024, doi: https://doi.org/10.51594/csitrj.v5i2.758.
  • Davy Preuveneers and Wouter Joosen, “An Ontology-Based Cybersecurity Framework for AI-Enabled Systems and Applications,” Future internet, vol. 16, no. 3, pp. 69–69, Feb. 2024, doi: https://doi.org/10.3390/fi16030069.
  • ‌ Andrei Chiş, O. I. Stoica, Ana-Maria Ghiran, and R. A. Buchmann, “A Knowledge Graph Approach to Cyber Threat Mitigation Derived from Data Flow Diagrams,” 2022 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), vol. 8, pp. 1–6, May 2024, doi: https://doi.org/10.1109/aqtr61889.2024.10554074.
  • Oloyede, “AI-Driven Cybersecurity Solutions: Enhancing Defense Mechanisms in the Digital Era,” SSRN Electronic Journal, Jan. 2024, doi: https://doi.org/10.2139/ssrn.4976103.
  • Mehmood, A. Shafique, Moatsum Alawida, and Abdul Nasir Khan, “Advances and Vulnerabilities in Modern Cryptographic Techniques: A Comprehensive Survey on Cybersecurity in the Domain of Machine/Deep Learning and Quantum Techniques,” IEEE access, pp. 1–1, Jan. 2024, doi: https://doi.org/10.1109/access.2024.3367232.
  • Wang, Z. Ding, K. Liu, L. Xin, Y. Zhao, and Y. Zhou, “Multi-Relation Extraction for Cybersecurity Based on Ontology Rule-Enhanced Prompt Learning,” Electronics, vol. 13, no. 12, pp. 2379–2379, Jun. 2024, doi: https://doi.org/10.3390/electronics13122379.
  • Onishchenko, Oleksandr Puchkov, and I. Subach, “Investigation of associative rule search method for detection of cyber incidents in information management systems and security events using CICIDS2018 test data set,” Collection Information technology and security, vol. 12, no. 1, pp. 91–101, Jun. 2024, doi: https://doi.org/10.20535/2411-1031.2024.12.1.306275.
  • K. Sah and Venkatesh K, “Anomaly-Based Intrusion Detection in Network Traffic using Machine Learning: A Comparative Study of Decision Trees and Random Forests,” vol. 9, pp. 1–7, Apr. 2024, doi: https://doi.org/10.1109/icnwc60771.2024.10537451.
  • Aziz and R. Bestak, “Insight into Anomaly Detection and Prediction and Mobile Network Security Enhancement Leveraging K-Means Clustering on Call Detail Records,” Sensors, vol. 24, no. 6, p. 1716, Jan. 2024, doi: https://doi.org/10.3390/s24061716.
  • Alsamir Alsamir and Hanan AlShaher, “Anomaly-Based Intrusion Detection Systems Using Machine Learning,” Journal of Cybersecurity and Information Management, vol. 14, no. 1, pp. 20–33, Jan. 2024, doi: https://doi.org/10.54216/jcim.140102.
  • Mumuni and F. Mumuni, “Automated data processing and feature engineering for deep learning and big data applications: a survey,” Journal of Information and Intelligence, Jan. 2024, doi: https://doi.org/10.1016/j.jiixd.2024.01.002.
  • Patil, N. L. Rane, P. Desai, and J. Rane, “Machine learning and deep learning: Methods, techniques, applications, challenges, and future research opportunities,” Trustworthy Artificial Intelligence in Industry and Society, Oct. 2024, doi: https://doi.org/10.70593/978-81-981367-4-9_2.
  • Chen et al., “Development and application of Few-shot learning methods in materials science under data scarcity,” Journal of Materials Chemistry A, Jan. 2024, doi: https://doi.org/10.1039/d4ta06452f.
  • Hassani, Ehsan Hallaji, Roozbeh Razavi-Far, and M. Saif, “Learning from high-dimensional cyber-physical data streams: a case of large-scale smart grid,” International Journal of Machine Learning and Cybernetics, Sep. 2024, doi: https://doi.org/10.1007/s13042-024-02365-3.
  • Alamin Talukder et al., “Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction,” Journal of Big Data, vol. 11, no. 1, Feb. 2024, doi: https://doi.org/10.1186/s40537-024-00886-w.
  • Pranali Dhawas, Abhishek Dhore, D. Bhagat, Ritu Dorlikar Pawar, Ashwini Kukade, and Kamlesh Kalbande, “Big Data Preprocessing, Techniques, Integration, Transformation, Normalisation, Cleaning, Discretization, and Binning,” Advances in business information systems and analytics book series, pp. 159–182, Dec. 2023, doi: https://doi.org/10.4018/979-8-3693-0413-6.ch006.
  • N. Ashraf, R. Siddiqi, and H. Farooq, “Auto encoder-based defense mechanism against popular adversarial attacks in deep learning,” PLOS ONE, vol. 19, no. 10, p. e0307363, Oct. 2024, doi: https://doi.org/10.1371/journal.pone.0307363.
  • Vasan and M. Hammoudeh, “Enhancing Resilience Against Adversarial Attacks in Medical Imaging Using Advanced Feature Transformation Training,” Current Opinion in Biomedical Engineering, pp. 100561–100561, Oct. 2024, doi: https://doi.org/10.1016/j.cobme.2024.100561.
  • Yuan, S. Han, W. Huang, H. Ye, X. Kong, and F. Zhang, “A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System,” arXiv (Cornell University), Dec. 2023, doi: https://doi.org/10.1016/j.cose.2023.103644.
  • Yuvaraja Thangavel, H. Garg, Manjunathan Alagarsamy, and D. Pradeep, “Revolutionizing breast cancer diagnosis with a comprehensive approach using digital mammogram-based feature extraction and selection for early-stage identification,” Biomedical signal processing and control, vol. 94, pp. 106268–106268, Aug. 2024, doi: https://doi.org/10.1016/j.bspc.2024.106268.
  • Wasim, A. R. Singh, A. Pandian, R. S. Rathore, M. Bajaj, and I. Zaitsev, “A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things,” Scientific Reports, vol. 14, no. 1, Nov. 2024, doi: https://doi.org/10.1038/s41598-024-78976-1.
  • ‌ Arash Mahboubi et al., “Evolving techniques in cyber threat hunting: A systematic review,” Journal of Network and Computer Applications, pp. 104004–104004, Aug. 2024, doi: https://doi.org/10.1016/j.jnca.2024.104004.
  • Ayo Rotibi, N. Saxena, and P. Burnap, “Winning the battle with cyber risk identification tools in industrial control systems: A review,” IET Cyber-Physical Systems Theory & Applications, Nov. 2024, doi: https://doi.org/10.1049/cps2.12105.
  • Ullah, S. Rahman, and Sharmine Akther Liza, “Cyber-susiliency index: A comprehensive resiliency-sustainability-cybersecurity index for healthcare supply chain networks,” Decision Analytics Journal, vol. 9, pp. 100319–100319, Dec. 2023, doi: https://doi.org/10.1016/j.dajour.2023.100319.
  • Ranka, A. Shah, N. Vora, A. Kulkarni, and N. Patil, “Computer Vision-Based Cybersecurity Threat Detection System with GAN-Enhanced Data Augmentation,” Communications in Computer and Information Science, pp. 54–67, 2024, doi: https://doi.org/10.1007/978-3-031-53728-8_5.
  • Mohammad, F. Saeed, A. A. Almazroi, F. S. Alsubaei, and A. A. Almazroi, “Enhancing Intrusion Detection Systems Using a Deep Learning and Data Augmentation Approach,” Systems, vol. 12, no. 3, p. 79, Mar. 2024, doi: https://doi.org/10.3390/systems12030079.
  • K. Nwobodo, C. S. Nwaimo, A. E. Adegbola, L. K. Nwobodo, C. S. Nwaimo, and A. E. Adegbola, “Enhancing cybersecurity protocols in the era of big data and advanced analytics,” GSC Advanced Research and Reviews, vol. 19, no. 3, pp. 203–214, 2024, doi: https://doi.org/10.30574/gscarr.2024.19.3.0211.
  • Agrawal, A. Kaur, and S. Myneni, “A Review of Generative Models in Generating Synthetic Attack Data for Cybersecurity,” Electronics, vol. 13, no. 2, p. 322, Jan. 2024, doi: https://doi.org/10.3390/electronics13020322.
  • Saeed, S. A. Altamimi, N. A. Alkayyal, E. Alshehri, and D. A. Alabbad, “Digital Transformation and Cybersecurity Challenges for Businesses Resilience: Issues and Recommendations,” Sensors, vol. 23, no. 15, p. 6666, Jul. 2023, doi: https://doi.org/10.3390/s23156666.
  • A. Aliyu, J. Liu, and E. Gilliard, “A Decentralized and Self-Adaptive Intrusion Detection Approach Using Continuous Learning and Blockchain Technology,” Journal of Data Science and Intelligent Systems, 2024, doi: https://doi.org/10.47852/bonviewjdsis42023803.
  • E. Topcu, Y. I. Alzoubi, E. Elbasi, and E. Camalan, “Social Media Zero-Day Attack Detection Using TensorFlow,” Electronics, vol. 12, no. 17, p. 3554, Jan. 2023, doi: https://doi.org/10.3390/electronics12173554.
  • Preeja Pradeep, M. Caro-Martínez, and Anjana Wijekoon, “A practical exploration of the convergence of Case-Based Reasoning and Explainable Artificial Intelligence,” Expert Systems with Applications, vol. 255, pp. 124733–124733, Jul. 2024, doi: https://doi.org/10.1016/j.eswa.2024.124733.
  • Qi et al., “Cybersecurity knowledge graph enabled attack chain detection for cyber-physical systems,” vol. 108, pp. 108660–108660, May 2023, doi: https://doi.org/10.1016/j.compeleceng.2023.108660.
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