RETRACTED ARTICLE: Best features based intrusion detection system by RBM model for detecting DDoS in cloud environment

Mayuranathan, M.; Murugan, Mullai; Dhanakoti, V.

doi:10.1007/s12652-019-01611-9

Cited by 64 publications

(36 citation statements)

References 18 publications

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“…Langin et al [84], Le et al [86] , Malondkar et al [85] Auto Encoder (AE) feature learning model, insider threat detection, malware detection, intrusion detection system Yousefi et al [92], Liu et al [93], Wang et al [94], Yan et al [95] Restricted Boltzmann Machine (RBM) network anomaly detection, DoS attack detection, intrusion detection Fiore et al [99], Imamverdiyev et al [100], Mayuranathan et al [125], Alom et al [126] Deep Belief Networks (DBN) intrusion detection system and optimization, phishing detection, malware detection Salama et al [104], Qu et al [105], Wei et al [103], Yi et al [127], Arshey et al [128], Saif et al [129], Hou et al [130] Generative Adversarial Network (GAN) zero-day malware detection, botnet detection, intrusion detection systems Kim et al [108], Li et al [110], Yin et al [109], Merino et al [111] Deep Transfer Learning (DTL or Deep TL) intrusion detection system, detecting unknown network attacks, malware detection, malicious software classification Wu et al [114], Zhao et al [117], Gao et al [118], Rezende et al [119] Deep Reinforcement Learning (DRL or deep RL) intrusion detection system, malware detection, Security and Privacy Lopez et al [131], Sethi et al [132], Fang et al [133], Shakeel et al [134] the quality of the security data and the performance of the learning algorithms.…”

Section: Cybersecurity Tasksmentioning

confidence: 99%

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Sarker

2021

SN COMPUT. SCI.

116

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Deep learning, which is originated from an artificial neural network (ANN), is one of the major technologies of today's smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as multi-layer perceptron, convolutional neural network, recurrent neural network or long short-term memory, self-organizing map, autoencoder, restricted Boltzmann machine, deep belief networks, generative adversarial network, deep transfer learning, as well as deep reinforcement learning, or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today's diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identification of malware or botnets, phishing, predicting cyberattacks, e.g. denial of service, fraud detection or cyberanomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.

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Section: Cybersecurity Tasksmentioning

confidence: 99%

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Sarker

2021

SN COMPUT. SCI.

116

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“…Working hierarchically, features extracted from a dataset are then passed on to the next layer as latent variables. RBMs were used in various research work [192,193] for network/IoT intrusion detection systems. The challenge of implementing RBMs is that it needs high computational resources while implementing it on low-powered IoT devices.…”

Section: Restricted Boltzmann Machine (Rbm)mentioning

confidence: 99%

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

et al. 2020

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The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

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“…The evaluation results confirmed the competitive performance of the proposed feature selection method using the improved CSA and high classification accuracy of the RNN with CSA. Mayuranathan et al [29] proposed an improved intrusion detection system with a new feature selection technique using the Random Harmony Search (RHS) optimization algorithm. The Restricted Boltzmann Machines were applied as a classifier for detection Distributed Denialof-Service (DDoS).…”

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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RETRACTED ARTICLE: Best features based intrusion detection system by RBM model for detecting DDoS in cloud environment

Cited by 64 publications

References 18 publications

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

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

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