<i>Ad Hoc</i> Vehicular Fog Enabling Cooperative Low-Latency Intrusion Detection

Mourad, Azzam; Tout, Hanine; Wahab, Omar Abdel; Otrok, Hadi; Dbouk, Toufic

doi:10.1109/jiot.2020.3008488

Cited by 75 publications

(19 citation statements)

References 46 publications

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“…Mourad et al 78 suggested a fog‐enabled vehicular edge computing architecture that allows intrusion detection tasks to be offloaded to federated vehicle nodes located inside adjacent created ad hoc vehicular fog and conducted collaboratively with minimal delay. The challenge was presented as a multi‐objective optimization model and addressed through a genetic algorithm that maximized offloading survival while reducing compute execution time and energy usage in the context of high mobility.…”

Section: Review Of the Articlesmentioning

confidence: 99%

A deep and systematic review of the intrusion detection systems in the fog environment

Yi¹,

Yin²,

Darbandi

2022

Trans Emerging Tel Tech

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Fog computing has arisen to complement cloud computing, offering a cost-effective architecture to power the Internet of things. Fog computing is a network computing and service paradigm that expands cloud computing and services to the network's edge to solve the cloud's fundamental shortcomings, like a lack of location awareness, mobility, and latency. Besides, fog devices may have security issues because fog nodes are near end-users and have limited computational power. As the linked devices in the Internet of things grow exponentially, so does the potential for cyber-attack. Intrusion detection systems have been developed as a feasible tool for detecting and preventing harmful activity in this vein. This work used a systematic literature review to examine intrusion detection system approaches in a fog environment. By categorizing intrusion detection systems in the literature, we produce a review. An intrusion detection system's characteristic is early identification of an attack and the decrease of false alarms as a result. Some of the issues that these systems' designers will face are also discussed. Recall rate, detection rates, detection delay, and detection precision were the parameters that received the most attention. This report will serve as a roadmap for future network intrusion detection studies in fog computing. 1• Numerous fog nodes are managed and controlled by a cloud computing center.

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Section: Review Of the Articlesmentioning

confidence: 99%

A deep and systematic review of the intrusion detection systems in the fog environment

Yi¹,

Yin²,

Darbandi

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…The results of EIDS-ACC-OD technique showed the accuracy, precision, recall, specificity, F1-score, false positive ratio (FRT), and false negative ratio (FNR). From the review, [21][22][23][24][25][26][27][28][29][30][31][32][33] with the increased use of the Internet and the services it provides, there has been an increase in cyber attacks for the use of information. Cloud computing is a technology used to store and maintain user information due to its simplicity and low budget services.…”

Section: Research Gapmentioning

confidence: 99%

“…CLNN stores complete information that is relevant and relevant to the assignment. The parent capsule i is calculated using the Equation ( 26), when the output of the capsule j is P i|j P i|j = Z ji P j (26) During the rearward pass, Z ji indicates the learned matrix weight. The coupling coefficient is calculated using the "Softmax Activation" function, which measures the similarity and compatibility between the capsules and the parent layers in the layers below.…”

Section: Instruction Detection Using Capsule Learn Based Neural Netwo...mentioning

confidence: 99%

“…Mourad et al, 26 proposed an activated vehicular‐edge computing (VEC) fog program that allows infiltration detection tasks to be performed on a nearby temporary vehicle in fog with minimal interruptions to loads. As a diversified optimization model, this problem is solved using a genetic algorithm that increases the existence of high dynamics and reduces computational time and energy consumption.…”

Section: Literature Surveymentioning

confidence: 99%

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A hybrid soft computing technique for intrusion detection in web and cloud environment

Maheswari¹,

Siva²,

Nalinipriya

2022

Concurrency and Computation

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Cloud computing environment contains important, essential, or confidential information; therefore, a security solution is needed to prevent this environment from potential attacks. In short, cloud computing has become one of the most sought after technologies in the field of information technology, and among the most dangerous threats. In this article, we propose a hybrid soft computing technique for intrusion detection in web and cloud environment (ST‐IDS). In ST‐IDS, we illustrate whale integrated slap swarm optimization algorithm for pre‐processing which remove the unwanted/repeated data's in dataset. We introduce new clustering technique based on modified tug‐of‐war optimization algorithm which groups the data in different segments. Then, we develop hybrid machine learning technique that is, capsule learning based neural network which categorize the attack in cloud environment. Finally, the proposed ST‐IDS technique can evaluate through standard open source datasets are KDD cup'99 and NSL‐KDD. The performance comparison of the proposed ST‐IDS technique using existing innovative technologies in terms of accuracy, precession, recall, specificity, F measure, false positive rate, and false negative rate.

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“…Protecting the location privacy of vehicles has become an important research topic of vehicular networks in recent years. To address this problem, several intrusion detection [10] [11] and pseudonym solutions [12] have been proposed. The method that uses pseudonyms when broadcasting security safety messages is now being widely accepted.…”

Section: Introductionmentioning

confidence: 99%

Personalized Location Privacy Protection Based on Vehicle Movement Regularity in Vehicular Networks

Zhong¹,

Ni²,

Zhang³

et al. 2022

IEEE Systems Journal

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Currently, the issue of location privacy has been attracting increasing attention. In traditional pseudonym-based solutions, the pseudonym age of each vehicle increases at the same rate and vehicles change their pseudonyms when they meet vehicles which also want to change pseudonyms at the same location. However, we observe that according to the individual characteristics of each vehicle's owner, the movement of each vehicle is regular, i.e., there are some locations that vehicles visit frequently, and some locations that they rarely visit. Evidently, pseudonym change strategies for these locations are different. To address these issues, we propose a sensitivity-based pseudonym change mechanism, which can take full advantage of the regularity of a vehicle's movement, thereby achieving personalized location privacy. A pseudonym age analytical model is used to quantify the achieved location privacy. Furthermore, we propose a new metric to measure the level of personalized location privacy protection. The results of the performance evaluation demonstrate that our approach significantly outperforms existing approaches in realizing personalized location privacy.

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Ad Hoc Vehicular Fog Enabling Cooperative Low-Latency Intrusion Detection

Cited by 75 publications

References 46 publications

A deep and systematic review of the intrusion detection systems in the fog environment

A deep and systematic review of the intrusion detection systems in the fog environment

A hybrid soft computing technique for intrusion detection in web and cloud environment

Personalized Location Privacy Protection Based on Vehicle Movement Regularity in Vehicular Networks

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