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Ayush Giri,
Bhupendra Singh Rajput,
Abhuday Tripathi,
Atul Kumar Appu,
Ghanshyam Prasad Dubey,
- Student, Department of Computer Science & Engineering, Amity, Gwalior, Madhya Pradesh, India
- Student, Department of Computer Science & Engineering, Amity, Gwalior, Madhya Pradesh, India
- Assistant Professor, Department of Computer Science & Engineering, Sagar Institute of Science and Technology (SISTec), Bhopal, Madhya Pradesh, India
- Sr. System Analyst II, EbixCash Limited, Noida Special Economy Zone, Block A, Phase-2, Noida, Uttar Pradesh, India
- Associate Professor, Department of Computer Science & Engineering, Amity University, Gwalior, Madhya Pradesh, India
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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 to address new threats require advanced detection technologies and its techniques. Cyber threats gained a new meaning and dimension with Artificial Intelligence coming into play in ways that supplement security systems in real time and even provide exceptive analysis. This research paper reviews various ways in 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 cyber-attacks 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, aviates phishing attempts, 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, Cyber Attack Detection, Threat Prevention
[This article belongs to International Journal of Information Security Engineering (ijise)]
Ayush Giri, Bhupendra Singh Rajput, Abhuday Tripathi, Atul Kumar Appu, Ghanshyam Prasad Dubey. Cyber-attacks Detection and Prevention using Empowering AI Tools. International Journal of Information Security Engineering. 2024; 02(02):-.
Ayush Giri, Bhupendra Singh Rajput, Abhuday Tripathi, Atul Kumar Appu, Ghanshyam Prasad Dubey. Cyber-attacks Detection and Prevention using Empowering AI Tools. International Journal of Information Security Engineering. 2024; 02(02):-. Available from: https://journals.stmjournals.com/ijise/article=2024/view=0
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| Volume | 02 |
| Issue | 02 |
| Received | 24/09/2024 |
| Accepted | 03/10/2024 |
| Published | 07/11/2024 |
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