Abstract
Intrusion Detection System (IDS) plays a very important role in security systems. Among its different types, Network Intrusion Detection System (NIDS) has an effective role in monitoring computer networks systems for malicious and illegal activities. In the literature, the detection of DoS and Probe attacks were with reasonable accuracy in most of the NIDS researches. However, the detection accuracy of other categories of attacks is still low, such as the R2L and U2R in KDDCUP99 dataset along with the Backdoors and Worms in UNSW-NB15 dataset. Computational Intelligence (CI) techniques have the characteristics to address such imprecision problem. In this research, a Hybrid Nested Genetic-Fuzzy Algorithm (HNGFA) framework has been developed to produce highly optimized outputs for security experts in classifying both major and minor categories of attacks. The adaptive model is evolved using two-nested Genetic-Fuzzy Algorithms (GFA). Each GFA consists of two-nested Genetic Algorithms (GA). The outer is to evolve fuzzy sets and the inner is to evolve fuzzy rules. The outer GFA assists the inner GFA in training phase, where the best individual in outer GFA interacts with the weak individual in inner GFA to generate new solutions that enhance the prediction of mutated attacks. Both GFA interact together to evolve the best rules for normal, major and minor categories of attacks through the optimization process. Several experiments have been conducted with different settings over different datasets. The obtained results show that the developed model has good accuracy and is more efficient compared with several state-of-the-art techniques.
Highlights
With the emergence of new technologies in Internet services, such as cloud computing and Internet of Things (IoT), the vast use of communication networks technology has been increased
The KDDCUP99 dataset is a subset of a larger dataset provided by the Defense Advanced Research Projects Agency (DARPA) (1998), as an operational traffic simulation for US Air Force base on Local Area Network (LAN)
WORK In this paper, a novel Hybrid Nested Genetic-Fuzzy Algorithm (HNGFA) framework has been developed for detecting normal traffic and most of the intrusions’ categories, whether major or minor categories of attacks, the categories that have rare information in datasets
Summary
With the emergence of new technologies in Internet services, such as cloud computing and Internet of Things (IoT), the vast use of communication networks technology has been increased. In this regard, computer networks security has been one of the major concerns in computer societies [1]. Intrusion Detection System (IDS) plays a core function in computer networks security, where it provides proper protection against malicious activities [2]–[4]. The second type is anomaly-based, which is designed to focus on the behavior of activities over the normal environment. The third type can be hybrid from signaturebased and anomaly-based [8]
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