Cyberattack Detection and Prevention Using Empowering AI Tools


Year : 2024 | Volume : 02 | Issue : 02 | Page : 1-7
    By

    Ayush Giri,

  • Bhupendra Singh Rajput,

  • Abhuday Tripathi,

  • Atul Kumar Appu,

  • Ghanshyam Prasad Dubey,

  1. Student, Department of Computer Science and Engineering, Amity University, Gwalior, Madhya Pradesh, India
  2. Student, Department of Computer Science and Engineering, Amity University, Gwalior, Madhya Pradesh, India
  3. Assistant Professor, Department of Computer Science and Engineering, Sagar Institute of Science and Technology (SISTec), Bhopal, Madhya Pradesh, India
  4. Sr. System Analyst II, EbixCash Limited, Noida Special Economy Zone, Block A, Phase-2, Noida, Gautam Buddha Nagar, Uttar Pradesh, India
  5. Associate Professor, Department of Computer Science and Engineering, Amity University, Gwalior, Madhya Pradesh, India

Abstract

With more organizations entering the digital transformation sphere, the opportunities and risks in cyberspace have increased and gone up in levels of sophistication and occurrence. Many of these developments are attributed to the limits of existing cyber security solutions where addressing new threats requires advanced detection technologies and techniques. Cyber threats gained a new meaning and dimension with artificial intelligence (AI) coming into play in ways that supplement security systems in real-time and even provide exceptive analysis. This research paper reviews various ways which include but are not limited to the use of machine learning, deep learning, and natural language processing techniques to analyze the effect and mitigation of cyberattacks with a focus on AI systems. As a result, new threats go undetected because human behavior is ignored in the area of information security through AI systems, and behaviors of the systems are to be undetected because machine speed behavior objectives are joined in a slim gap. For example, algorithms in regards to the machine learning mechanisms can also adapt instantaneously as and when new attack patterns arise improving the performance of the system over time, whereas system patterns that involve deep learning mechanisms allow efficiency in exploring and identifying complicated attacks through the use of complex data focused structures. In addition, natural language processing allows these intelligent systems to monitor communication channels, avoid phishing attempts, and even recognize insider threats by scrutinizing communication patterns through text. As much as it is clear, there are some issues in the adoption of AI in the improvement of cyber security systems. Such concerns can be raised when systems employing AI techniques solicit the use of very large datasets to enhance the learning process. This complicates the cognitive burden on decision-makers who would have to depend on AI models to make critical security decisions. There is also the threat of adversarial interference where the attackers utilize the AI system on which the defense is built. This survey seeks to address these concerns and examine ways in which risk can be reduced like using AI together with human judgment to make better decisions while minimizing false alarms. This paper discusses the possibilities of utilizing AI to maximize efforts in fighting against cybercrime by analyzing current developments and future patterns. The aims provided supplement the existing studies and enlighten on how best to use AI to create resilient, intelligent systems and secure digital infrastructures.

Keywords: Artificial intelligence (AI), cyber security, machine learning, deep learning, cyberattack detection, threat prevention

[This article belongs to International Journal of Information Security Engineering ]

How to cite this article:
Ayush Giri, Bhupendra Singh Rajput, Abhuday Tripathi, Atul Kumar Appu, Ghanshyam Prasad Dubey. Cyberattack Detection and Prevention Using Empowering AI Tools. International Journal of Information Security Engineering. 2024; 02(02):1-7.
How to cite this URL:
Ayush Giri, Bhupendra Singh Rajput, Abhuday Tripathi, Atul Kumar Appu, Ghanshyam Prasad Dubey. Cyberattack Detection and Prevention Using Empowering AI Tools. International Journal of Information Security Engineering. 2024; 02(02):1-7. Available from: https://journals.stmjournals.com/ijise/article=2024/view=181543


References

  1. Admass WS, Munaye YY, Diro AA. Cyber security: State of the art, challenges and future directions. Cyber Secur Appl. 2024;2:100031. DOI: 10.1016/j.csa.2023.100031.
  2. Bhatt C, Goyal P, Dubey GP, Singh S, Kumar V. Detection of cyber-bullying in social-media using classification algorithms of machine learning. Community Pract. 2024;21:793–804.
  3. Kuzlu M, Fair C, Guler O. Role of artificial intelligence in the Internet of things (IoT) cybersecurity. Discov Internet Things. 2021;1:7. DOI: 10.1007/s43926-020-00001-4.
  4. Dubey GP, Bhujade RK. Improving the performance of intrusion detection system using machine learning based approaches. Int J Emerg Trends Eng Res. 2020;8:4947–51. DOI: 10.30534/ijeter/2020/09892020.
  5. Dunsin D, Ghanem MC, Ouazzane K, Vassilev V. A comprehensive analysis of the role of artificial intelligence and machine learning in modern digital forensics and incident response. Forensic Sci Int Digit Investig. 2024;48:301675.
  6. Dubey GP. Investigating the impact of feature reduction through information gain and correlation on the performance of error back propagation based IDS. Int J Electr Electron Res. 2021;9:27–34. DOI: 10.37391/090302.
  7. Elguea-Aguinaco Í, Serrano-Muñoz A, Chrysostomou D, Inziarte-Hidalgo I, Bøgh S, Arana-Arexolaleiba N. A review on reinforcement learning for contact-rich robotic manipulation tasks. Robot Comput Integr Manuf. 2023;81:102517. DOI: 10.1016/j.rcim.2022.102517.
  8. Alqahtani A, Sheldon FT. A survey of crypto ransomware attack detection methodologies: An evolving outlook. Sensors (Basel). 2022;22:1837. DOI: 10.3390/s22051837. PubMed: 35270983.
  9. Xu T, Singh K, Rajivan P. Personalized persuasion: Quantifying susceptibility to information exploitation in spear-phishing attacks. Appl Ergon. 2023;108:103908. DOI: 10.1016/j.apergo.2022.103908. PubMed: 36403509.
  10. Karnani S, Shakya HK. Mitigation strategies for distributed denial of service (DDoS) in SDN: A survey and taxonomy. Inf Secur J Glob Perspect. 2023;32:444–68. DOI: 10.1080/19393555.2022.2111004.
  11. Dubey GP, Bhujade DRK. Optimal feature selection for machine learning based intrusion detection system by exploiting attribute dependence. Mater Today Proc. 2021;47:6325–31. DOI: 10.1016/j.matpr.2021.04.643.
  12. Alabadi M, Celik Y. Anomaly detection for cyber-security based on convolution neural network: A survey. 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey, 2020. pp. 1-14. p. 1–14. DOI: 10.1109/HORA49412.2020.9152899.
  13. Radoglou-Grammatikis P, Sarigiannidis P, Efstathopoulos G, Lagkas T, Fragulis G, Sarigiannidis A. A self-learning approach for detecting intrusions in healthcare systems. ICC 2021 – IEEE International Conference on Communications, Montreal, QC, Canada. 2021. pp. 1–6. DOI: 10.1109/ICC42927.2021.9500354.
  14. Himthani P, Gurbani P, Raghuwanshi KD, Patidar G, Mishra NK. Ordered ensemble classifier chain for image and emotion classification. In: Saraswat M, Sharma H, Balachandran K, Kim JH, Bansal JC, editors. Proceedings of the CIS 2021 Congress on Intelligent Systems. Vol. 1. Lecture Notes on Data Engineering and Communications Technologies. Vol. 1. Singapore: Springer Nature; 2022. p. 395–406.
  15. Angelelli M, Arima S, Catalano C, Ciavolino E. A robust statistical framework for cyber-vulnerability prioritisation under partial information in threat intelligence. Expert Syst Appl. 2024;255:124572. DOI: 10.1016/j.eswa.2024.124572.
  16. Bartwal U, Mukhopadhyay S, Negi R, Shukla S. Security orchestration, automation, and response engine for deployment of behavioural honeypots. 2022 IEEE Conference on Dependable and Secure Computing (DSC), Edinburgh, United Kingdom. 2022. p. 1–8. DOI: 10.1109/DSC54232.2022.9888808.
  17. Liang Y, Samtani S, Guo B, Yu Z. Behavioral biometrics for continuous authentication in the internet-of-things era: An artificial intelligence perspective. IEEE Internet Things J. 2020;7:9128–43. DOI: 10.1109/JIOT.2020.3004077.
Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 24/09/2024
Accepted 03/10/2024
Published 07/11/2024
Total Visits: 160


Loading citations…

PlumX Metrics