Intrusion detection using MLP for MANETs

Pavani, Kolasani; Damodaram, A.

doi:10.1049/cp.2013.2626

Cited by 14 publications

(6 citation statements)

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“…DoS attacks on IoT devices or originating from IoT devices are a serious concern. One approach to prevent such attacks is to use a Multi-Layer Perceptron (MLP) based protocol that secures networks against DoS attacks [172]. The authors of [173] have proposed a particle swarm optimization and back propagation algorithm to train a MLP that helps in enhancing the security of wireless networks.…”

Section: Dos Attackmentioning

confidence: 99%

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

et al. 2019

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The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an evergrowing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed. INDEX TERMS Internet of Things (IoT), IoT security, blockchain, fog computing, edge computing, machine learning, IoT applications, distributed systems.

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Section: Dos Attackmentioning

confidence: 99%

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

et al. 2019

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“…This dataset is tested by Machine Learning Techniques from which Feature based Reduction techniques is used. The Feature reduction technique gave a good Gain; similarly the statistics of Chi-Squared is applied for the evaluation of the dataset presentation along with attribute reduction [6].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Outcomes obtained are shown using the attribute reduction that may result in the better when compared to the accuracy it has resulted from 110 to 41 is 2.4 percent [6].…”

Section: Literature Reviewmentioning

confidence: 99%

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A Literature Survey on Intrusion Detection System in Manets using Machine Learning Techniques

Fouad¹

2019

IJCA

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Since, a decade of time Mobile Ad hoc Networks (MANETs) have come with wireless networking technology. Due to its dynamic in nature of MANETs, these are vulnerable to various attacks in all the OSI layers but research shows that in Network layer the attacks are effectively done by intruders. In this survey many of the attacks at Network layer are identified in MANETs by most of the researchers which is outlined in this paper. Mostly, AODV routing protocols and other protocols are used for transferring packets in the direction of the destination. These transferred packets data is then deposited within the log files, to surveillance these routing of packets from these Log files, the techniques used in MANETs are Data mining, Support Vector Machines (SVM), Genetic algorithms (GA) and other Machine learning approaches. Further, the methodologies and techniques proposed for detecting and predicting these attacks from various kinds of intrusions within the MANETS is discussed.

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“…DoS Attack: DoS attacks on IoT or IoT devices are a major concern. A multilayer perceptron (MLP) protocol to secure networks against DoS attacks serves as an approach for preventing such attacks [200]. Pavani, K. et al proposed to create an MLP to improve the safety of wireless networks through particle swarm optimization and a backpropagation algorithm [201].…”

mentioning

confidence: 99%

State-of-the-Art Review on IoT Threats and Attacks: Taxonomy, Challenges and Solutions

et al. 2021

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The Internet of Things (IoT) plays a vital role in interconnecting physical and virtual objects that are embedded with sensors, software, and other technologies intending to connect and exchange data with devices and systems around the globe over the Internet. With a multitude of features to offer, IoT is a boon to mankind, but just as two sides of a coin, the technology, with its lack of securing information, may result in a big bane. It is estimated that by the year 2030, there will be nearly 25.44 billion IoT devices connected worldwide. Due to the unprecedented growth, IoT is endangered by numerous attacks, impairments, and misuses due to challenges such as resource limitations, heterogeneity, lack of standardization, architecture, etc. It is known that almost 98% of IoT traffic is not encrypted, exposing confidential and personal information on the network. To implement such a technology in the near future, a comprehensive implementation of security, privacy, authentication, and recovery is required. Therefore, in this paper, the comprehensive taxonomy of security and threats within the IoT paradigm is discussed. We also provide insightful findings, presumptions, and outcomes of the challenges to assist IoT developers to address risks and security flaws for better protection. A five-layer and a seven-layer IoT architecture are presented in addition to the existing three-layer architecture. The communication standards and the protocols, along with the threats and attacks corresponding to these three architectures, are discussed. In addition, the impact of different threats and attacks along with their detection, mitigation, and prevention are comprehensively presented. The state-of-the-art solutions to enhance security features in IoT devices are proposed based on Blockchain (BC) technology, Fog Computing (FC), Edge Computing (EC), and Machine Learning (ML), along with some open research problems.

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Intrusion detection using MLP for MANETs

Cited by 14 publications

References 0 publications

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

A Literature Survey on Intrusion Detection System in Manets using Machine Learning Techniques

State-of-the-Art Review on IoT Threats and Attacks: Taxonomy, Challenges and Solutions

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