An Online Intrusion Detection System to Cloud Computing Based on Neucube Algorithms

Almomani, Ammar; Alauthman, Mohammad; Albalas, Firas; Dorgham, Osama; Obeidat, Atef A.

doi:10.4018/ijcac.2018040105

Cited by 20 publications

(11 citation statements)

References 34 publications

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“…The main intuition behind building IDS was to see the impact that adversaries can produce in deviating IDS detection capability by poisoning the gradients during federated training. NSL-KDD contains 125,973 records in KDDTrain+ file [17]. We divided the records present in the file among our ten participating clients.…”

Section: E Xperimentationmentioning

confidence: 99%

Clustering based Rewarding Algorithm to Detect Adversaries in Federated Machine Learning based IoT Environment

Yadav

Gupta

2021

2021 IEEE International Conference on Consumer Electronics (ICCE)

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In recent times, federated machine learning has been very useful in building the intelligent intrusion detection system for IoT devices. As IoT devices are equipped with a security architecture vulnerable to various attacks, these security loopholes may bring a risk during federated training of decentralized IoT devices. Adversaries can take control over these IoT devices and inject false gradients to degrade the global model performance. In this paper, we have proposed an approach that detects the adversaries with the help of a clustering algorithm. After clustering, it further rewards the clients for detecting honest and malicious clients. Our proposed gradient filtration approach does not require any processing power from the client-side and does not use excessive bandwidth, making it very much feasible for IoT devices. Further, our approach has been very successful in boosting the global model accuracy, up to 99% even in the presence of 40% adversaries.

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Section: E Xperimentationmentioning

confidence: 99%

Clustering based Rewarding Algorithm to Detect Adversaries in Federated Machine Learning based IoT Environment

Yadav

Gupta

2021

2021 IEEE International Conference on Consumer Electronics (ICCE)

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“…The authors in [49] presented a cloud IDS based on the novel Spiking Neural Network (SNN) architecture (also termed the NeuCube algorithm). The Neu-Cube algorithm with SNN (core processing module) can easily manage huge data traffic thereby improving performance in classification and identification of various malicious attacks.…”

Section: Novel Deception Technologies and Limitationsmentioning

confidence: 99%

Cutting Edge Trends in Deception Based Intrusion Detection Systems—A Survey

Oluoha¹,

Yange²,

Okereke³

et al. 2021

JIS

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Cyber criminals have become a formidable treat in today's world. This present reality has placed cloud computing platforms under constant treats of cyber-attacks at all levels, with an ever-evolving treat landscape. It has been observed that the number of threats faced in cloud computing is rising exponentially mainly due to its widespread adoption, rapid expansion and a vast attack surface. One of the front-line tools employed in defense against cyber-attacks is the Intrusion Detection Systems (IDSs). In recent times, an increasing number of researchers and cyber security practitioners alike have advocated the use of deception-based techniques in IDS and other cyber security defenses as against the use of traditional methods. This paper presents an extensive overview of the deception technology environment, as well as a review of current trends and implementation models in deception-based Intrusion Detection Systems. Issues mitigating the implementation of deception based cyber security defenses are also investigated.

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“…The proposed system was setup and tested in a simulated campus network environment. The authors in [50] presented a cloud IDS based on the novel Spiking Neural Network (SNN) architecture (also termed the NeuCube algorithm). The NeuCube algorithm with SNN (core processing module) can easily manage huge data traffic thereby improving performance in classification and identification of various malicious attacks.…”

Section: Deception Based Technology In Network Intrusion Detectionmentioning

confidence: 99%

A Deception Based Intelligent Intrusion Detection System for Detecting Threats of Exploits in Cloud Based Environments.

Oluoha¹,

kereke²,

Udanor³

et al. 2021

Proceedings of the 28th iSTEAMS Multidisciplinary &Amp; Inter-Tertiary Research Conference

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Despite its numerous advantages, cloud computing faces major security threats with constantly evolving digital prints and attack-like patterns. Unfortunately, due to the share size and complexity of cloud computing, traditional approaches to Intrusion Detection Systems (IDS) have been shown to be rather defective in adapting to, identifying and mitigating threat in cloud based environment. While, anomaly-based IDS are plagued with misidentifying legitimate network activities or sometimes permitting sophisticated malicious traffic patterns, signature-based IDS on the other hand are less adaptive and practically ineffective against sophisticated attacks and advanced persistent threat (APT). This paper presents a unique design approach for deception-based intelligent Intrusion Detection Systems, which are better suited for operations in cloud based environments. Modelling and simulation was conducted using Application Characterization Engine and Flow Modelling Engine within OPNET modular to create runtimes of known attack types in a deception based environment. The machine learning scripts, attack codes and embedded socket and API integration scripts are presented in Python. The security framework was modelled with machine learning to further enhance its adaptability and predictive capabilities. Keywords: Cybersecurity, Intrusion Detection System, Deception techniques, Machine Learning

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An Online Intrusion Detection System to Cloud Computing Based on Neucube Algorithms

Cited by 20 publications

References 34 publications

Clustering based Rewarding Algorithm to Detect Adversaries in Federated Machine Learning based IoT Environment

Clustering based Rewarding Algorithm to Detect Adversaries in Federated Machine Learning based IoT Environment

Cutting Edge Trends in Deception Based Intrusion Detection Systems—A Survey

A Deception Based Intelligent Intrusion Detection System for Detecting Threats of Exploits in Cloud Based Environments.

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