Distributed intrusion detection scheme using dual-axis dimensionality reduction for Internet of things (IoT)

Gavel, Shashank; Raghuvanshi, Ajay Singh; Tiwari, Sudarshan

doi:10.1007/s11227-021-03697-5

Cited by 19 publications

(5 citation statements)

References 39 publications

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“…e multi-agent distributed intrusion detection system could improve the scalability of the system, which was flexible and easy to implement in programming [4]. Zhao et al proposed an intrusion detection model based on a repeated game with the penalty mechanism, mainly to constrain the existing attack nodes in wireless sensor networks and restrain their attack behaviors to reduce the harm to the network.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Smartphone Trojan Detection Based on the Wireless Sensor Network

2022

Security and Communication Networks

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In order to reduce false positives of Trojan horse detection algorithms in smartphones, a voting algorithm based on multiple machine learning algorithms in wireless sensor networks was proposed. Through setting up the experiment, the preliminary preparations of the experiment, including sample set selection, feature set extraction method, and algorithm effect evaluation criteria, were described first. The K-nearest neighbors algorithm, random forests algorithm, support vector machine, and voting algorithm were compared. The experimental results showed that SVM and KNN algorithms took the shortest time, about 0.3 seconds. Judging by the test results, the voting algorithm still performed the best among the four algorithms as the voting algorithm was an extension of the three machine learning algorithms. In these randomly selected samples, malicious programs and nonmalicious programs were successfully distinguished by the voting algorithm. As the amount of test data increased, the test results would be closer to the actual situation. Namely, the voting algorithm would also have a small probability of false positives, which could meet the design requirements of the system. It was concluded that the method could effectively reduce false positives of the Trojan horse detection algorithm in smartphones.

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Section: Literature Reviewmentioning

confidence: 99%

Research on Smartphone Trojan Detection Based on the Wireless Sensor Network

2022

Security and Communication Networks

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“…They used three kernels, namely, sigmoid, Gaussian and hard limit functions. The Multi-objective Artificial Bee Colony Optimization algorithm 36 , PSO algorithm 37 , SSA, wrapper based FS 38 , multiobjective Grey Wolf Optimisation algorithm 39 are all used for DR.…”

Section: Analysis Of Articlesmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Real time Network Intrusion Detection with Applications in Cybersecurity

Gondhalekar,

Chattamvelli

2024

Def. Sc. J.

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This paper reviews popular signature and anomaly-based intrusion detection systems (IDS). Dimensionality reduction techniques (DRT) are used to increase the efficiency of such systems for real-time operation. Autoencoder-based IDS is rapidly gaining in popularity, primarily due to its inherent ability to denoise data and reduce dimensionality. In addition to the efficiency, we also look at the classification techniques used by various authors, and the overall impact of a model in terms of performance metrics. This article is written for novices in cybersecurityto get a jumpstart on the latest IDS algorithms. The purpose is to give useful insights into the broad and progressive view of various techniques in wide use, expose high-impact future research areas and to summarize classic IDS methods and feature selection techniques.

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“…In [ 131 ], Gavel et al designed a fog-based model for intrusion detection in an IoT network. The model is based on a combination of the Kalman filter and the salp swarm algorithm.…”

Section: Artificial Intelligence In Edge-based Iot Applications: Lite...mentioning

confidence: 99%

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

Bourechak

Zedadra

Kouahla

et al. 2023

Sensors

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Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.

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Distributed intrusion detection scheme using dual-axis dimensionality reduction for Internet of things (IoT)

Cited by 19 publications

References 39 publications

Research on Smartphone Trojan Detection Based on the Wireless Sensor Network

Research on Smartphone Trojan Detection Based on the Wireless Sensor Network

A Comprehensive Review of Dimensionality Reduction Techniques for Real time Network Intrusion Detection with Applications in Cybersecurity

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

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