intrusion detection system

XIDINTV: XGBoost-based intrusion detection of imbalance network traffic via variational auto-encoder

In networks characterized by imbalanced traffic, detecting malicious cyber-attacks poses a significant challenge due to their ability to blend seamlessly with regular data volumes.  This creates a formidable hurdle for Network Intrusion Detection Systems (NIDS) striving for accurate and timely identification.  The imbalance in normal and attack data, coupled with the diversity among attack categories, complicates intrusion detection.  This research proposes a novel approach to address this issue by combining Extreme Gradient Boosting with variational autoencoder (XIDINT

MEANS OF DETECTING CYBERNETIC ATTACKS ON INFORMATION SYSTEMS

Systems for detecting network intrusions and detecting signs of cyber attacks on information systems have long been used as one of the necessary lines of defense of information systems. Today, intrusion and attack detection systems are usually software or hardware-software solutions that automate the process of monitoring events occurring in the information system or network, as well as independently analyze these events in search of signs of security problems.

Модель системи протидії вторгненням в інформаційних системах

The article proposes a model of intrusion detection systems (IDS), which reflects the main
processes that take place in the system in order to optimize the processes of anti-intrusion. Such
processes in general can be represented as processes of allocation and use of resources that are
allocated for the protection of information. The use of modeling techniques to ensure the
appropriate level of information security has led to the development of many formal security