TY  - JOUR
T1  - A New Distributed Learning Based Algorithm for Network Intrusion
Detection System
AU - Pasikhani, Aryan Mohammadi AU - Sundararajan, Elankovan A. 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 6
SP  - 1523
EP  - 1537
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.1523.1537
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.1523.1537
KW  - Network-based
KW  -signature based
KW  -distributed
KW  -multi-agent
KW  -adaptive
AB  - The significant increase in computer network usage and the huge amount of sensitive data being
stored and transferred through them has escalated the attacks and invasions on these networks. Secure data
communication over the internet and any other network is always under threat of intrusions and misuses. The
system that monitors the events occurring in a computer system or a network and analyzes the events for signs
of intrusion is known as an Intrusion Detection System (IDS). In information protection, the Intrusion Detection
System (IDS) has become a crucial component in terms of computer and network security which monitors the
network traffic to detect possible security threats. There are various approaches being utilized in intrusion
detections but unfortunately any of the systems so far are not completely flawless and suffer from a number
of drawbacks such as low accuracy to detect new types of intrusions and misclassification of normal and
malicious traffic, in addition to long response time. It is necessary to develop an IDS that is accurate, adaptive
and extensible to overcome these weaknesses. In this study, we proposed a learning-based method which
improves IDS adaptability to new attacks and reduces false alarms. The method that has a distributed
architecture to increase performance and scalability of the IDS and uses C4.5 decision trees with the feedback
learning technique to adapt dynamic network behaviors. To evaluate the proposed method we used
some well-known datasets in this context such as KDD Cup 99 and did several tests with approximately 97%
detection accuracy on benchmarks. According to the promising results, the adaptable IDS approach is more
accurate than traditional systems and it is more efficient against new complex network attacks.
ER  -