Performance comparison of intrusion detection system based anomaly detection using artificial neural network and support vector machine

Cahyo, Aditya Nur; Hidayat, Risanuri; Adhipta, Dani

doi:10.1063/1.4958506

Cited by 19 publications

(7 citation statements)

References 12 publications

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“…The research was successful at creating the model but proved to be only a slight improvement over regular SVM, showing that the model even without enhancements or augmenting is capable of accurately classifying attack data. Other more recent research compared SVM and ANN’s ability to classify attack data [ 18 ]. As previously mentioned, SVM relies on placing a hyperplane to separate data which can be expressed as follows:

where a is the vector of the same dimensions as the input feature vector x and b is the bias.…”

Section: Background and Related Workmentioning

confidence: 99%

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

Churcher

Ullah

Ahmad

et al. 2021

Sensors

145

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In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

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where a is the vector of the same dimensions as the input feature vector x and b is the bias.…”

Section: Background and Related Workmentioning

confidence: 99%

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

Churcher

Ullah

Ahmad

et al. 2021

Sensors

145

View full text Add to dashboard Cite

show abstract

“…We now compare the performance of the proposed RFID‐based IDS model with the recent data mining‐based NIDS. Tables 7–11 and Figures 12–16 show the comparative performance of the proposed RFID‐based IDS model with random forest‐based IDS, SVM‐based IDS, and principal component analysis‐based IDS . From these tables and figures, it can be clearly observed that the PNN/GRRN gives high detection accuracy with low false alarm rate in comparison with other IDS models due to the prior knowledge discovery about the dependencies with high accuracy and efficiency.…”

Section: Resultsmentioning

confidence: 96%

Intrusion detection in RFID system using computational intelligence approach for underground mines

Gautam

2018

Int J Communication

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The radio frequency identification technology (RFID) is commonly used for object tracking and monitoring. In this paper, we discuss a model for intrusion detection system based on RFID to identify the abnormal behavior of underground mines' toxic gases. This model consists of various types of sensor nodes that are integrated with RFID tag, which are deployed in the underground mines by using Zigbee protocol. It consists of coordinators, routers, and sensor nodes, according to different capabilities and the probabilities of intrusive activities that occur in underground mines. It can detect the real-time abnormal

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“…Intrusion can be detected based on the two modules: Signature-Based and Anomaly Based (Bhuyan et al, 2014). The signature-based approach detects intrusion by examining the pattern and match it with the signature stored in the existing database of the system (Cahyo et al, 2016). Signature Based has great accuracy in detection.…”

Section: B Detection Type Of Wireless Sensor Network Intrusion Detection Systemmentioning

confidence: 99%

“…Anomaly-based is a detection approach of IDS that detects intrusion based on the created profile of the system (Cahyo et al, 2016). The profile is created to define the normal traffic pattern and the detection is done by matching the pattern of the traffic.…”

Section: B Detection Type Of Wireless Sensor Network Intrusion Detection Systemmentioning

confidence: 99%

A Review of Support Vector Machine-based Intrusion Detection System for Wireless Sensor Network with Different Kernel Functions

Hamzah

Othman

2021

Int J Innov Comp

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Wireless sensor network (WSN) is among the popular communication technology which capable of self-configured and infrastructure-less wireless networks to monitor physical or environmental conditions. WSN also is the most standard services employed in commercial and industrial applications, because of its technical development in a processor, communication, and low-power usage of embedded computing devices. However, WSN is vulnerable due to the dynamic nature of wireless network. One of the best solutions to mitigate the risk is implementing Intrusion Detection System (IDS) to the network. Numerous researches were done to improve the efficiency of WSN-IDS because attacks in networks has been evolved due to the rapid growth of technology. Support Vector Machine (SVM) is one of the best algorithms for the enhancement of WSN-IDS. Nevertheless, the efficiency of classification in SVM is based on the kernel function used. Since dynamic environment of WSN consist of nonlinear data, linear classification of SVM has limitations in maximizing its margin during the classification. It is important to have the best kernel in classifying nonlinear data as the main goal of SVM to maximize the margin in the feature space during classification. In this research, kernel function of SVM such as Linear, RBF, Polynomial and Sigmoid were used separately in data classification. In addition, a modified version of KDD’99, NSL-KDD was used for the experiment of this research. Performance evaluation was made based on the experimental result obtained. Finally, this research found out that RBF kernel provides the best classification result with 91% accuracy.

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Performance comparison of intrusion detection system based anomaly detection using artificial neural network and support vector machine

Cited by 19 publications

References 12 publications

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

Intrusion detection in RFID system using computational intelligence approach for underground mines

A Review of Support Vector Machine-based Intrusion Detection System for Wireless Sensor Network with Different Kernel Functions

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