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An Enhanced Intrusion Detection System
Based on 1Suez
Canal University,
Information System Department, Ismailia 41522, Egypt melshrkawey@ci.suez.edu.eg, marwa.alalfi@gmail.com 2Suez
Canal University, Computer
Science Department, Ismailia 41522, Egypt drhassanwesf@ci.suez.edu.eg
Abstract Wireless network has an exponential increase in
various aspects of the human community. Accordingly, transmitting a vast
volume of sensitive and non-sensitive data over the network puts them at risk
of being attacked. To avoid this, Intrusion Detection System (IDS) security
is intended to detect threats and protect devices from attacks. IDS usually uses
one of the following alternative approaches: signature-based, anomaly-based,
or hybrid of the two. In spite of the IDS has been the focus of much research
in recent years, there is still space for improvement. Based on the anomaly
based approach, this paper proposes a modified algorithm called a Multi-layer
Feature Selection and Reduction IDS (MFSR-IDS) for providing high-level
protection against Denial-of-Service (DoS) and Probe attacks. The MFSR-IDS
framework makes three major contributions. First, it reduces the feature
dimensionality of the network dataset across three layers. Second, it has a
fast and accurate detection system. Third, it provides a mathematical model
of the framework under consideration. The MFSR-IDS algorithm selects optimal
number of features from KDDCUP’99 dataset which used to train the predictive
model based on different learning classifiers and ensemble methodology. The
performance of MFSR-IDS is evaluated in terms of Detection Rate (DR), False
Positive Rate (FPR), FScore, ROC area, Accuracy (Acc) and Processing time.
The experiments indicate that, the proposed MFSR-IDS outperforms some
existing IDS frameworks in terms of DR, FPR, Acc and Processing time in
detecting DoS and Probe attacks. Keywords: Intrusion Detection System, Anomaly
Based Detection, KDDCUP’99 Dataset, Feature Selection. +: Corresponding author: Hassan Al-Mahdi
Journal of Internet Services and
Information Security (JISIS), 11(4): 61-78, November 2021 |