Gunay Abdiyeva-Aliyeva - Academia.edu (original) (raw)
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Papers by Gunay Abdiyeva-Aliyeva
Procedia Computer Science
Journal of Advances in Information Technology
Cyber-attacks threatening the network and information security have increased, especially during ... more Cyber-attacks threatening the network and information security have increased, especially during the current rapid IT revolution. Therefore, a monitoring and protection system should be used to secure the computer networks. An intrusion detection system is very crucial on the market since it helps to control the network traffic and alerts the users during illegal access to the network. IDS is divided into three types: signature-based IDS, anomaly-based IDS, and both. Automatically updating the attack list to overcome new attack types is one of the main challenges of signaturebased IDS. Most IDS or websites use recently detected attack signatures to update their databases manually or remotely. This article proposes a new AI model that uses a filter engine that functions as a second IDS engine to automatically update the attack list by AI. The results show that using the proposed model can improve the overall accuracy of IDS. The proposed model uses an IP-Factor (IPF) and Non-IP-Factor (NIPF) blacklist that can automatically detect the threats and update the IDS database with new attack features without manual intervention, as well as define new attack features based on similarity.
2021 2nd Global Conference for Advancement in Technology (GCAT), 2021
Artificial intelligence (AI) technologies have given the cyber security industry a huge leverage ... more 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.
Procedia Computer Science
Journal of Advances in Information Technology
Cyber-attacks threatening the network and information security have increased, especially during ... more Cyber-attacks threatening the network and information security have increased, especially during the current rapid IT revolution. Therefore, a monitoring and protection system should be used to secure the computer networks. An intrusion detection system is very crucial on the market since it helps to control the network traffic and alerts the users during illegal access to the network. IDS is divided into three types: signature-based IDS, anomaly-based IDS, and both. Automatically updating the attack list to overcome new attack types is one of the main challenges of signaturebased IDS. Most IDS or websites use recently detected attack signatures to update their databases manually or remotely. This article proposes a new AI model that uses a filter engine that functions as a second IDS engine to automatically update the attack list by AI. The results show that using the proposed model can improve the overall accuracy of IDS. The proposed model uses an IP-Factor (IPF) and Non-IP-Factor (NIPF) blacklist that can automatically detect the threats and update the IDS database with new attack features without manual intervention, as well as define new attack features based on similarity.
2021 2nd Global Conference for Advancement in Technology (GCAT), 2021
Artificial intelligence (AI) technologies have given the cyber security industry a huge leverage ... more 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.