Survey on Intrusion Detection System using Machine Learning Techniques

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect “underfitting” condition.

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“…Some machine learning methods have been applied with the hope of improving detection rates and adaptive capability [36]. …”

Section: Simulation In Big Data Stream: Intrusion Detection Systemmentioning

confidence: 99%

Adaptive Online Sequential ELM for Concept Drift Tackling

Budiman

Fanany

Basaruddin

2016

Computational Intelligence and Neuroscience

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“…Machine learning methods [3]- [8] have been widely used in intrusion detection to identify malicious traffic. However, these methods belong to shallow learning and often emphasize feature engineering and selection.…”

Section: Introductionmentioning

confidence: 99%

BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

et al. 2020

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Intrusion detection can identify unknown attacks from network traffics and has been an effective means of network security. Nowadays, existing methods for network anomaly detection are usually based on traditional machine learning models, such as KNN, SVM, etc. Although these methods can obtain some outstanding features, they get a relatively low accuracy and rely heavily on manual design of traffic features, which has been obsolete in the age of big data. To solve the problems of low accuracy and feature engineering in intrusion detection, a traffic anomaly detection model BAT is proposed. The BAT model combines BLSTM (Bidirectional Long Short-term memory) and attention mechanism. Attention mechanism is used to screen the network flow vector composed of packet vectors generated by the BLSTM model, which can obtain the key features for network traffic classification. In addition, we adopt multiple convolutional layers to capture the local features of traffic data. As multiple convolutional layers are used to process data samples, we refer BAT model as BAT-MC. The softmax classifier is used for network traffic classification. The proposed end-to-end model does not use any feature engineering skills and can automatically learn the key features of the hierarchy. It can well describe the network traffic behavior and improve the ability of anomaly detection effectively. We test our model on a public benchmark dataset, and the experimental results demonstrate our model has better performance than other comparison methods.

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“…Over the years, several survey papers (e.g., Kwon et al, 3 Tong et al, 4 Gardiner and Nagaraja, 5 Shanbhogue and Beena, 8 Liu et al, 9 Bou-Harb et al, 10 Luh et al, 11 Shabut et al, 12 Humayed et al, 13 Wang et al, 14 Deng et al, 15 Bou-Harb, 16 Beasley et al, 17 Ucci et al, 18 Ye et al, 19 Bazrafshan et al, 20 Souri and Hosseini, 21 Barriga and Yoo, 22 Bontupalli and Taha, 23 Buczak and Guven, 24 Resende and Drummond, 25 KishorWagh et al, 26 Liu et al, 27 Sultana et al, 28 Gupta et al, 29 and Wang et al 33 ) and case studies (e.g., Al-Enezi et al 31 and Alotaibi et al 32 ) on the use of computational intelligence in cybersecurity have been published, but with limited scope. For instance, Humayed et al, 13 Wang et al, 14 Deng et al, 15 Bou-Harb, 16 and Beasley et al 17 discussed only cybersecurity related to cyber-physical systems (e.g., smart grid 33 ) or specific applications (e.g., block-chain 34 ), while Ucci et al, 18 Ye et al, 19 Bazrafshan et al, 20 Souri and Hosseini, 21 and Barriga and Yoo 23 gave details about DM and ML techniques used for malware detection.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Humayed et al, 13 Wang et al, 14 Deng et al, 15 Bou-Harb, 16 and Beasley et al 17 discussed only cybersecurity related to cyber-physical systems (e.g., smart grid 33 ) or specific applications (e.g., block-chain 34 ), while Ucci et al, 18 Ye et al, 19 Bazrafshan et al, 20 Souri and Hosseini, 21 and Barriga and Yoo 23 gave details about DM and ML techniques used for malware detection. Similarly, Bontupalli and Taha, 23 Buczak and Guven, 24 Resende and Drummond, 25 and KishorWagh et al 26 focused only on the IDS and its related ML approaches, whereas Liu et al 35 and Kwon et al 3 studied, respectively, insider threats and DL-based anomaly detection. Furthermore, Sultana et al 28 provided a review on ML and DL security techniques in the IoTs, Gupta et al 29 surveyed the literature on phishing attacks, and Wang et al 30 elucidated about the game-theoretic approach used for cybersecurity.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in cybersecurity: a comprehensive survey

Dasgupta

Akhtar

Sen

2020

Journal of Defense Modeling & Simulation

125

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Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minute millions of data bytes are being generated, processed, exchanged, shared, and utilized to yield outcomes in specific applications. Thus, securing the data, machines (devices), and user’s privacy in cyberspace has become an utmost concern for individuals, business organizations, and national governments. In recent years, machine learning (ML) has been widely employed in cybersecurity, for example, intrusion or malware detection and biometric-based user authentication. However, ML algorithms are vulnerable to attacks both in the training and testing phases, which usually leads to remarkable performance decreases and security breaches. Comparatively, limited studies have been conducted to understand the essence and degree of the vulnerabilities of ML techniques against security threats and their defensive mechanisms. It is imperative to systematize recent works related to cybersecurity using ML to seek the attention of researchers, scientists, and engineers. Therefore, in this paper, we provide a comprehensive survey of the works that have been carried out most recently (from 2013 to 2018) on ML in cybersecurity, describing the basics of cyber-attacks and corresponding defenses, the basics of the most commonly used ML algorithms, and proposed ML and data mining schemes for cybersecurity in terms of features, dimensionality reduction, and classification/detection techniques. In this context, this article also provides an overview of adversarial ML, including the security characteristics of deep learning methods. Finally, open issues and challenges in cybersecurity are highlighted and potential future research directions are discussed.

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Survey on Intrusion Detection System using Machine Learning Techniques

Cited by 42 publications

References 19 publications

Adaptive Online Sequential ELM for Concept Drift Tackling

Adaptive Online Sequential ELM for Concept Drift Tackling

BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset

Machine learning in cybersecurity: a comprehensive survey

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