Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine
- Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
- Date: 2020
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 9, no. 1 (2020), p.
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- Description: Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks
- Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
- Date: 2019
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 8, no. 11 (2019), p.
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- Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.