| Title | Development of System for Detection and Prevention of Cyber Attacks Using Artifıcial Intelligence Methods |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Abdiyeva-Aliyeva, Gunay, Hematyar, Mehran, Bakan, Sefa |
| Conference Name | 2021 2nd Global Conference for Advancement in Technology (GCAT) |
| Date Published | oct |
| Keywords | ANN, black hole, composability, Computer architecture, Computer crime, DDoS Attack Prevention, Deep Learning, DNN-based models, Flooding, GAIS, Human Behavior, Malware, Metrics, Neighbor attacks, pubcrawl, Real-time Systems, resilience, Resiliency, Rushing, Scalability, telecommunication traffic, Wormhole |
| Abstract | Artificial intelligence (AI) technologies have given the cyber security industry a huge leverage with the possibility of having significantly autonomous models that can detect and prevent cyberattacks - even though there still exist some degree of human interventions. AI technologies have been utilized in gathering data which can then be processed into information that are valuable in the prevention of cyberattacks. These AI-based cybersecurity frameworks have commendable scalability about them and are able to detect malicious activities within the cyberspace in a prompter and more efficient manner than conventional security architectures. However, our one or two completed studies did not provide a complete and clear analyses to apply different machine learning algorithms on different media systems. Because of the existing methods of attack and the dynamic nature of malware or other unwanted software (adware etc.) it is important to automatically and systematically create, update and approve malicious packages that can be available to the public. Some of Complex tests have shown that DNN performs maybe can better than conventional machine learning classification. Finally, we present a multiple, large and hybrid DNN torrent structure called Scale-Hybrid-IDS-AlertNet, which can be used to effectively monitor to detect and review the impact of network traffic and host-level events to warn directly or indirectly about cyber-attacks. Besides this, they are also highly adaptable and flexible, with commensurate efficiency and accuracy when it comes to the detection and prevention of cyberattacks.There has been a multiplicity of AI-based cyber security architectures in recent years, and each of these has been found to show varying degree of effectiveness. Deep Neural Networks, which tend to be more complex and even more efficient, have been the major focus of research studies in recent times. In light of the foregoing, the objective of this paper is to discuss the use of AI methods in fighting cyberattacks like malware and DDoS attacks, with attention on DNN-based models. |
| DOI | 10.1109/GCAT52182.2021.9587584 |
| Citation Key | abdiyeva-aliyeva_development_2021 |
Development of System for Detection and Prevention of Cyber Attacks Using Artifıcial Intelligence Methods