Intrusion Detection System Using Bagging Ensemble Method of Machine Learning

Gaikwad, D. P.; Thool, Ravindra C.

doi:10.1109/iccubea.2015.61

Cited by 114 publications

(40 citation statements)

References 9 publications

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“…Moreover, ensemble methods are machine learning techniques that combine several base models in order to reduce false positive rates and produce more accurate solutions than a single model would. Gaikwad and Thool [33] proposed a bagging ensemble method using REPTree as its base classifier, which takes less time to build the model and provides highest classification accuracy with lowest false positives on the NSL-KDD dataset. Jabbar et al [41] proposed a clusterbased ensemble classifier for IDS, which is built with Alternating Decision Tree (ADTree) and k-Nearest Neighbor algorithm (kNN).…”

Section: On Ensemble Classifiersmentioning

confidence: 99%

Building an efficient intrusion detection system based on feature selection and ensemble classifier

et al. 2020

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A B S T R A C T Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics.

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Section: On Ensemble Classifiersmentioning

confidence: 99%

Building an efficient intrusion detection system based on feature selection and ensemble classifier

et al. 2020

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“…Bagging [16] is an example of an ensemble method that combines Bootstrapping and Aggregation. From a sample of the dataset, multiple bootstrapped samples are taken upon which a decision tree is formed for each one.…”

Section: Ensemble Methodsmentioning

confidence: 99%

Building a NIDS Using a Two-Stage Classifier and Feature Reduction through Statistical Methods

Raj

Gupta

Lohia

et al. 2020

Wseas Transactions on Systems and Control

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Today, data is produced, transmitted and stored across the internet in abundant quantities. In such a world, modern-day network security systems have had to develop at an equally astounding rate in order to keep up with the data deluge. Subsequently, companies spend a substantial amount of money, time and effort in developing intrusion detection systems to ensure timely detection and prevention of malicious activity in order to preserve system security. In our paper, we propose a two-stage algorithm to solve the problem of NIDS resulting in fewer false positives and false negatives. We built and evaluated the performance of our IDS using the benchmarked NSL-KDD dataset, which consists of 41 features, of which 3 are categorical and the remaining numeric. Categorical features have been transformed via One Hot Encoding due to which the feature space explodes to 122 features. Our aim henceforth is to reduce this feature space through methods of feature selection and dimensionality reduction to develop a computationally inexpensive classifier capable of operating on the reduced feature space using sequential models. The work has involved using autoencoders for feature space reduction followed by a feed forward network, and has delivered encouraging results. We have then extended our analysis to identify features which can be eliminated without any substantial loss of information available for the classification algorithm. The remaining set of features can then be input into a different model to possibly provide better results or reduce training and evaluation time.

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“…The time complexity of an EL system is higher than that of a single classifier-based system. [163][164][165][166][167].…”

Section: Detection Of Intrusion [156]mentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

838

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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Intrusion Detection System Using Bagging Ensemble Method of Machine Learning

Cited by 114 publications

References 9 publications

Building an efficient intrusion detection system based on feature selection and ensemble classifier

Building an efficient intrusion detection system based on feature selection and ensemble classifier

Building a NIDS Using a Two-Stage Classifier and Feature Reduction through Statistical Methods

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

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