An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment

Saisindhutheja, Reddy; Shyam, Gopal K.

doi:10.1016/j.asoc.2020.106997

Cited by 94 publications

(46 citation statements)

References 23 publications

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“…This subsection studies the IDS schemes [110], [122]- [129], which apply RNN in the detection of intrusions. For instance, for detecting attacks within the IoT network, Roy et al [130] incorporated an LSTM RNN.…”

Section: Recurrent Neural Network-based Schemesmentioning

confidence: 99%

Deep Learning-Based Intrusion Detection Systems: A Systematic Review

et al. 2021

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Nowadays, the ever-increasing complication and severity of security attacks on computer networks have inspired security researchers to incorporate different machine learning methods to protect the organizations' data and reputation. Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts. This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. It first presents the primary background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods utilized in each of them. It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.INDEX TERMS Intrusion detection, auto-encoder, recurrent neural network, Boltzmann machine, CNN.

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Section: Recurrent Neural Network-based Schemesmentioning

confidence: 99%

Deep Learning-Based Intrusion Detection Systems: A Systematic Review

et al. 2021

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“…SaiSindhuTheja and Shyam [30] proposed a new Detection of Denial of Service (DoS) attack detection system using a modified Crow Search Algorithm (CSA) for feature selection. The Opposition Based Learning (OBL) is combined with the CSA to boost its performance.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, metaheuristics (MH) optimization algorithms have been adopted for various complex problems, including feature selection. They have been also applied for intrusion detection, example, genetic algorithm [23]- [25], particle swarm optimization (PSO) [26], gery wolf optimizer (GWO) [27], [28], random harmony search (RHS) [29], and crow search algorithm (CSA) [30].…”

Section: Introductionmentioning

confidence: 99%

IoT Intrusion Detection System Using Deep Learning and Enhanced Transient Search Optimization

et al. 2021

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The great advancements in communication, cloud computing, and the internet of things (IoT) have opened critical challenges in security. With these developments, cyberattacks are also rapidly growing since the current security mechanisms do not provide efficient solutions. Recently, various artificial intelligence (AI) based solutions have been proposed for different security applications, including intrusion detection. In this paper, we propose an efficient AI-based mechanism for intrusion detection systems (IDS) in IoT systems. We leverage the advancements of deep learnings and metaheuristics (MH) algorithms that approved their efficiency in solving complex engineering problems. We propose a feature extraction method using the convolutional neural networks (CNNs) to extract relevant features. Also, we develop a new feature selection method using a new variant of the transient search optimization (TSO) algorithm, called TSODE, using the operators of differential evolution (DE) algorithm. The proposed TSODE uses the DE to improve the process of balancing between exploitation and exploration phases. Furthermore, we use three public datasets, KDDCup-99, NSL-KDD, BoT-IoT, and CICIDS-2017 to assess the performance of the developed method, which achieved higher accuracy compared to several existing approaches.

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“…The developed hybrid approach was evaluated on the NSL-KDD dataset and was found to consume lesser storage space due to the decreased number of dimensions from feature selection, and also require lower training time, thereby improving classification performance. A meta-heuristic algorithm based feature selection and recurrent neural network for DoS attack detection was proposed in [46]. Mazini, M., et al, in [38] proposed a novel hybrid approach for an Anomaly Network IDS, using ABC and AdaBoost algorithms to obtain higher detection rate and lower false positive rate.…”

Section: Review Of Hybrid Meta-heuristic Ids Approaches In Cloudmentioning

confidence: 99%