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 October 2026
The management of critical infrastructure heavily relies on Supervisory Control and Data Acquisition [SCADA] systems, but as they become more connected, insider attacks become a greater concern. Insider threat detection…
Network attacks are diversified, rare and Universal generalization. This has made the exploration and construction of network information flow packet threat detection systems, which becomes a hot research topic in preven…
In recent years, deep learning-based network intrusion detection systems (IDS) have shown impressive results in detecting attacks. However, most existing IDS can only recognize known attacks that were included in their t…
In the rapidly evolving landscape of cyber threats, the efficacy of traditional rule-based network intrusion detection systems has become increasingly questionable. This paper introduces a novel framework for identifying…
In today's rapidly evolving digital landscape, ensuring the security of networks and systems has become more crucial than ever before. The ever-present threat of hackers and intruders attempting to disrupt networks and c…
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…
Industrial control systems (ICS) play a crucial role in various industries and ensuring their security is paramount for maintaining process continuity and reliability. In ICS, the most damaging cyber-attacks often come f…
As network attacks are more and more frequent and network security is more and more serious, it is important to detect network intrusion accurately and efficiently. With the continuous development of deep learning, a lot…
The key advantage of the cloud is that it fluidly propagates to fulfil changeable requirements and provides an environment that is repeatable and can be scaled down instantly when needed. Therefore, it is necessary to pr…
