Behavioural Threat Analysis and Detection for Ids Using Attack Matrix Framework
Author(s):
V Sandhiya , Sri Venkateswara college of Science and TechnologyKeywords:
Intrusion Detection System, Artificial Intelligence, Deep Neural Network, Modified NaiveBayes Intrusion Detection System, Anomaly detectionAbstract:
Intrusion detection systems defines an important and dynamic research area for cyber security. The role of Intrusion Detection System within security architecture is to improve a security level by identification of all malicious and also suspicious events that could be observed in computer or network system. One of the more specific research areas related to intrusion detection is anomaly detection. The goal of this project is verification of the anomaly detection systems’ ability using behavioural algorithms to resist the attack. The main focus of this is solved by using a deep network which passes information through several layers to learn the underlying hidden patterns of normal and attack network connection records and finally aggregates these learned features of each layer together to effectively distinguish the normal from the various attacks of network connection records. To achieve an acceptable detection rate, we tweak the various configurations of network settings and its parameters in deep network so that it is able to form behaviour based feature analysis to detect attacks like virus threats, man-in-the-middle attacks, Denial of Services and so on.
Other Details:
| Manuscript Id | : | J4RV8I5001 |
| Published in | : | Volume : 8, Issue : 5 |
| Publication Date | : | 01/08/2022 |
| Page(s) | : | 9-16 |
