The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Intrusion detection is the process of monitoring network traffic or system activity to identify unauthorized access, policy violations, or malicious behavior. Intrusion detection systems are generally classified as signature-based, which match activity against known attack patterns, or anomaly-based, which flag deviations from an established baseline of normal behavior and can therefore detect previously unseen attacks. Deployment architectures include network-based systems that inspect traffic at chokepoints and host-based systems that monitor activity on individual machines, often combined in layered defense strategies. Recent research combining machine learning with IoT network traffic has reported detection accuracy above 99 percent on benchmark datasets such as IoTID20, alongside a broader shift toward deep learning architectures, including transformers, for more effective pattern recognition. Other active areas include federated learning approaches and detecting intrusions in encrypted traffic and industrial control systems. As an open-access intrusion detection journal, IJACSA publishes research evaluating intrusion detection models against benchmark datasets, alongside applied detection systems for specific network environments.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed September 2026
As networks continue to expand rapidly, the number and diversity of cyberattacks are also increasing, posing a significant challenge for organizations worldwide. Consequently, brute-force attacks targeting FTP and SSH pr…
The amount of data being exchanged over the internet is enormous. Attackers are finding novel ways to evade rules, investigate network defenses, and launch successful attacks. Intrusion detection is one of the effective…
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As cyber-attacks evolve in complexity and frequency; the development of effective network intrusion detection systems (NIDS) has become increasingly important. This paper investigates the efficacy of the XGBoost algorith…
In a cloud environment, software defined security services are highly vulnerable to malicious virus attacks. In response to software security issues, this project plans to use machine learning technology to achieve autom…
Machine Learning (ML) is seen as a promising application that offers autonomous learning and provides optimized solutions to complex problems. The current Multiprotocol Label Switching (MPLS)-based communication system i…
An intrusion detection system (IDS) is one of the most effective ways to secure a network and prevent unauthorized access and security attacks. But due to the lack of adequately labeled network traffic data, researchers…
