DMAIDPS: a distributed multi-agent intrusion detection and prevention system for cloud IoT environments

Javadpour, Amir; Pinto, Pedro; Ja’fari, Forough; Zhang, Weizhe

doi:10.1007/s10586-022-03621-3

Cited by 32 publications

(8 citation statements)

References 40 publications

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“…Specificity: it is the probability of negative test results, conditioned on the proportion of truly being negative. It is calculated using in (10).…”

Section: Evaluation Parametersmentioning

confidence: 99%

“…It is understood that intrusion detection is crucial for safeguarding any system against malicious activities, and maintaining the system's security so that it may function as intended [9]. IDS is a security tool that tracks the entire network's activity and identifies any malicious activity that is taking place [10]. The most common function of an IDS is to monitor risks and capture intruders before they cause serious damage to the network.…”

Section: Introductionmentioning

confidence: 99%

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Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

Thirumaran,

Joseph,

Srikanth

2024

IJEECS

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<div>Cloud computing (CC) is a rapidly developing IT approach with intrusion detection system being a crucial tool for safeguarding virtual networks and machines from potential threats, thereby mitigating security concerns in the cloud environment. The intrusion detection system (IDS) system demands significant improvements, primarily based on optimizing performance and bolstering security measures. This research aims to implement an IDS in cloud computing utilizing deep learning (DL) method. The DL model is a promising technique and is widely used to detect intrusions. The implemented hierarchical long short-term memory (HLSTM) method’s performance is evaluated for feature selection through variance threshold-based regression (VTR) on two IDS network datasets: Bot-IoT and network security lab-knowledge discovery and data mining (NSL-KDD). This paper concludes the use of an intrusion detection network resulting in high security and performance. Moreover, the implemented method on the NSL-KDD and Bot-IoT datasets obtains respective accuracies of 99.50% and 0.995. It is compared with the existing methods namely, ensemble ID model for CC utilizing DL, LeNet, fuzzy deep neural network with a Honey Bader algorithm for privacy-preserving ID, and improved metaheuristics with a fuzzy logic-based IDS for cloud security, and beluga whale-tasmanian devil optimization based on deep convolutional neural network (CNN) with TL, chronological slap swarm algorithm-based deep belief network (DBN), and dragonfly improved invasive weed optimization-based Shepard CNN.</div>

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“…Specificity: it is the probability of negative test results, conditioned on the proportion of truly being negative. It is calculated using in (10).…”

Section: Evaluation Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

Thirumaran,

Joseph,

Srikanth

2024

IJEECS

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“…In recent times, deep learning (DL) is becoming a hopeful analytic pattern because of its excellent function in examining huge volumes of data [7]. Dissimilar to the conventional ML method, DL supported effective feature engineering by handling automatic and reliable feature representation and extraction [8,9]. Since a strong analytic device, DL delivered existing latency and accuracy than the conventional ML method, and it is positioned for examining huge amounts of data in the 5G-based Internet of Things (IoT) [10].…”

Section: Introductionmentioning

confidence: 99%

Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme

Alohali¹,

Elsadig²,

Al‐Wesabi³

et al. 2023

Computer Systems Science and Engineering

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With recent advancements in information and communication technology, a huge volume of corporate and sensitive user data was shared consistently across the network, making it vulnerable to an attack that may be brought some factors under risk: data availability, confidentiality, and integrity. Intrusion Detection Systems (IDS) were mostly exploited in various networks to help promptly recognize intrusions. Nowadays, blockchain (BC) technology has received much more interest as a means to share data without needing a trusted third person. Therefore, this study designs a new Blockchain Assisted Optimal Machine Learning based Cyberattack Detection and Classification (BAOML-CADC) technique. In the BAOML-CADC technique, the major focus lies in identifying cyberattacks. To do so, the presented BAOML-CADC technique applies a thermal equilibrium algorithm-based feature selection (TEA-FS) method for the optimal choice of features. The BAOML-CADC technique uses an extreme learning machine (ELM) model for cyberattack recognition. In addition, a BC-based integrity verification technique is developed to defend against the misrouting attack, showing the innovation of the work. The experimental validation of BAOML-CADC algorithm is tested on a benchmark cyberattack dataset. The obtained values implied the improved performance of the BAOML-CADC algorithm over other techniques.

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“…DDoS attacks are sophisticated and deleterious cyber threats that are categorized as powerful large-scale distributed attacks. They are becoming bigger and more common for extortion and malicious activities [2]. Hence, there is an essential need to perform security countermeasures against DDoS attacks.…”

Section: Introductionmentioning

confidence: 99%

“…by shuffling network addresses) and aims to invalidate the information gathered during network reconnaissance [3]. The advantages of MTD compared to other security mechanisms are (1) their scalability, (2) almost removing the need threat detection, and (3) frustrating the adversary. Developing a network that can change its configuration and implement MTD methods is challenging.…”

Section: Introductionmentioning

confidence: 99%

A Cost-Effective MTD Approach for DDoS Attacks in Software-Defined Networks

Javadpour

Ja’fari

Taleb

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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Protecting large-scale networks, especially Software-Defined Networks (SDNs), against distributed attacks in a costeffective manner plays a prominent role in cybersecurity. One of the pervasive approaches to plug security holes and prevent vulnerabilities from being exploited is Moving Target Defense (MTD), which can be efficiently implemented in SDN as it needs comprehensive and proactive network monitoring. The critical key in MTD is to shuffle the least number of hosts with an acceptable security impact and keep the shuffling frequency low. In this paper, we have proposed an SDN-oriented Cost-effective Edge-based MTD Approach (SCEMA) to mitigate Distributed Denial of Service (DDoS) attacks with a lower cost by shuffling an optimized set of hosts have the highest number of connections to the critical servers. These connections are named edges from a graph-theoretical point of view. We have designed a system based on SCEMA and simulated it in Mininet. The results show that SCEMA has lower (52.58 52.58 52.58%) complexity than the previous related MTD methods with improving the security level by 14.32 14.32 14.32%.

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DMAIDPS: a distributed multi-agent intrusion detection and prevention system for cloud IoT environments

Cited by 32 publications

References 40 publications

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme

A Cost-Effective MTD Approach for DDoS Attacks in Software-Defined Networks

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