Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

An, Xueli; Zhou, Xuebing; Xing, Lu; Lin, Fuhong; Lei, Yang

doi:10.1155/2018/7472095

Cited by 82 publications

(71 citation statements)

References 26 publications

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“…Each ED j needing task computation is a selfish and rational player with the aim of minimizing its task overall completion time, according to (3), (4), and (5). In particular, each ED j forms its preference list over  by sorting the computation sites to find min , ,…”

Section: Eds and Fns Preference Listmentioning

confidence: 99%

“…Within this context, the development of efficient approaches to meet specific real‐time applications constraints to store and process data became a major study issue. In parallel, IoT security has also become a central issue in many application scenarios . Over the past decade, cloud computing (CC) has been foreseen as a powerful network architecture to execute remote storage and data computation .…”

Section: Introductionmentioning

confidence: 99%

“…In parallel, IoT security has also become a central issue in many application scenarios. 5,6 Over the past decade, cloud computing (CC) has been foreseen as a powerful network architecture to execute remote storage and data computation. 3,7 Generally speaking, the CC paradigm consists of thousands of data centers to support computational intensive applications and data storage.…”

Section: Introductionmentioning

confidence: 99%

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A matching game for tasks offloading in integrated edge‐fog computing systems

Chiti

Fantacci

Picano

2019

Trans Emerging Tel Tech

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Edge and fog computing paradigms have recently emerged as promising approaches to overcome latency and network congestion drawbacks of the cloud network architecture alternative. In this direction, the paper deals with an integrated edge‐fog computing system to provide computational offloading capabilities to end‐devices by assuming that each task can be alternatively run locally at the end‐device site, offloaded to a nearby device through a direct communication link (ie, device‐to‐device) or to a more far and computational powerful node, ie, a fog node. In particular, the goal of this paper is to identify a suitable tasks allocation strategy in order to minimize both the system energy consumption and the worst overall task completion time. The related optimization problem is formulated here as a matching game with externalities with incomplete preferences lists between tasks and computation sites due to the fact that each end‐device can reach only a subset of the fog nodes of the integrated computing system. Furthermore, this paper pursuits the stability analysis of the outcome matching, and provides a post matching procedure to reach a stable final matching configuration. Finally, the good behavior of the proposed approach is validated by providing performance comparisons with different alternatives, namely a typical cloud approach architecture, potential game and other matching theory based approaches recently proposed in literature.

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Section: Eds and Fns Preference Listmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A matching game for tasks offloading in integrated edge‐fog computing systems

Chiti

Fantacci

Picano

2019

Trans Emerging Tel Tech

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“…The focus of this study is on the intrusion response. Intrusion response is an important function of IDS framework [8] as shown in Figure 3.…”

Section: Intrusion In Fog Networkmentioning

confidence: 99%

“…Intrusion detection system [6] (IDS) is a measure that can provide effective security for fog network [7]. Our previous work [8,9] has proposed a general IDS framework to protect cloud servers and fog nodes from security threats. One of the functions of IDS is to make corresponding response strategy based on attackers' behaviors.…”

Section: Introductionmentioning

confidence: 99%

A Novel Differential Game Model-Based Intrusion Response Strategy in Fog Computing

Lin

et al. 2018

Security and Communication Networks

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Fog computing is an emerging network paradigm. Due to its characteristics (e.g., geo-location and constrained resource), fog computing is subject to a broad range of security threats. Intrusion detection system (IDS) is an essential security technology to deal with the security threats in fog computing. We have introduced a fog computing IDS (FC-IDS) framework in our previous work. In this paper, we study the optimal intrusion response strategy in fog computing based on the FC-IDS scheme proposed in our previous work. We postulate the intrusion process in fog computing and describe it with a mathematical model based on differential game theory. According to this model, the optimal response strategy is obtained corresponding to the optimal intrusion strategy. Theoretical analysis and simulation results demonstrate that our security model can effectively stabilize the intrusion frequency of the invaders in fog computing.

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Adversarial learning techniques for security and privacy preservation: A comprehensive review

Hathaliya

Tanwar

Sharma

2022

Security and Privacy

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In recent years, the use of smart devices has increased exponentially, resulting in massive amounts of data. To handle this data, effective data storage and management has required. Cloud computing (CC) is a promising solution to deal with this huge amount of data. Electronic devices are collecting real‐time data from sensors and applications through a wireless communication channel in the digital era. In some cases, CC cannot protect against various malicious attacks in the wireless communication channel. To address this issue, we have used machine learning (ML) and deep learning (DL) techniques for attack detection in a wireless channel on an early basis. It trains a model to predict malicious activities of attackers, which aids in the security of CC's sensitive data. We employed adversarial learning techniques (AL) to add fake data into the model to ensure that the trained model was correct. The trained model can distinguish between the fake and real data from the training samples and improve the training samples' performance. AL provides different defense mechanisms to preserve the privacy of ML‐ and DL‐based model but does not ensure the system's robustness. To improve the system's robustness, we have used federated learning with blockchain technology to make a system more robust, reliable, accurate, and transparent. This integration aids in providing high‐graded security against adversarial attacks. This paper presents a comprehensive review to highlight the recent improvements in AL techniques. Moreover, we explored the various AL applications in security and privacy preservation. Finally, open research issues and future directions are discussed to show future research avenues.

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Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

Cited by 82 publications

References 26 publications

A matching game for tasks offloading in integrated edge‐fog computing systems

A matching game for tasks offloading in integrated edge‐fog computing systems

A Novel Differential Game Model-Based Intrusion Response Strategy in Fog Computing

Adversarial learning techniques for security and privacy preservation: A comprehensive review

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