A Smart Approach for Intrusion Detection and Prevention System in Mobile Ad Hoc Networks Against Security Attacks

Islabudeen, M.; Devi, M. K. Kavitha

doi:10.1007/s11277-019-07022-5

Cited by 55 publications

(38 citation statements)

References 32 publications

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“…Securing the routing protocols has become a challenging task, due to the excessive usage of wireless connections. Typically, there are several motivations for the eavesdropper to attack the routing protocols [112]. Routing protocols in UAVs are vulnerable to different cyber-attacks because of various reasons [112]- [114].…”

Section: F Vulnerabilities In Packet Forwarding and Routing Protocolsmentioning

confidence: 99%

“…Typically, there are several motivations for the eavesdropper to attack the routing protocols [112]. Routing protocols in UAVs are vulnerable to different cyber-attacks because of various reasons [112]- [114]. For instance, they rely heavily on False alarm rate (FAR) is set A log-likelihood radio test to be fixed, FAR should be updated scheme [87] Gives the approximate value with time of FAR…”

Section: F Vulnerabilities In Packet Forwarding and Routing Protocolsmentioning

confidence: 99%

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Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

2021

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With the rapid growth in technology, the use of Unmanned Aerial Vehicles (UAVs) have increased in civil and military applications including rescue operations, disaster recovery, and military operations. Despite the utility and advantages of UAVs, they may lead to major security breaches in the context of hardware, software, and communication channel, due their ease of use and availability. UAVs are vulnerable to various types of attacks such as spoofing, false data injection, jamming, fuzzing, availability, confidentiality, and integrity attacks. To overcome these security threats, researchers have been investigating strong security protocols to keep UAVs safe from the attackers. Nevertheless, there are many flaws in the developed protocols which can be exploited by hackers. Therefore, it is becomes crucial to study and analyze the existing security protocols used in UAVs to discover and address their vulnerabilities and weaknesses. The purpose of this study is to explore the vulnerabilities in the security protocols and propose guidelines to improve the security and provide future research directions.

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Section: F Vulnerabilities In Packet Forwarding and Routing Protocolsmentioning

confidence: 99%

Section: F Vulnerabilities In Packet Forwarding and Routing Protocolsmentioning

confidence: 99%

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

2021

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“…Machine learning techniques have been used in IDS design [9][10][11][12] . In [9] the authors propose a misuse intrusion detection framework for a wireless local area network based on majority voting that differentiates between attacker and legitimate node patterns by examining mac-layer frames.…”

Section: Related Workmentioning

confidence: 99%

“…Their work does not consider lifetime and only considers blackhole and flooding attacks without considering colluding and smart attackers. In [12], a smart approach for intrusion detection and prevention system in mobile ad hoc networks is proposed. Their proposed system relies on machine learning approaches methods where intrusion detection depends on the four entities of packet analyzer, preprocessing unit, feature extraction unit and classification unit.…”

Section: Related Workmentioning

confidence: 99%

“…They perform security analysis by considering false positive rate and detection rate. These works cited above [9][10][11][12] all utilize machine learning techniques for providing host-level intrusion detection. Our work is different in that we aim to provide system-level intrusion detection in the form of IDS voting by which each voting node selected (which can be good or bad) reports its host-level intrusion detection outcome as input.…”

Section: Related Workmentioning

confidence: 99%

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Attack and Defense Strategies for Intrusion Detection in Autonomous Distributed IoT Systems

et al. 2020

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In this paper, we develop a methodology to capture and analyze the interplay of attack-defense strategies for intrusion detection in an autonomous distributed Internet of Things (IoT) system. In our formulation, every node must participate in lightweight intrusion detection of a neighbor target node. Consequently, every good node would play a set of defense strategies to faithfully defend the system while every bad node would play a set of attack strategies for achieving their own goals. We develop an analytical model based on Stochastic Petri Net (SPN) modeling techniques. Our methodology allows the optimal defense strategies to be played by good nodes to maximize the system lifetime when given a set of parameter values characterizing the distributed IoT system operational environment. We conduct a detailed performance evaluation based on an experiment dataset deriving from a reference autonomous distributed IoT system comprising 128 sensor-carrying mobile nodes and show how IDS defense mechanisms can counter malicious attack mechanisms under the ADIoTS system while considering multiple failure conditions.

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Machine learning solutions for mobile internet of things security: A literature review and research agenda

Messabih,

Kerrache,

Cheriguene

et al. 2024

Trans Emerging Tel Tech

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In recent years, the advancements in wireless technologies and sensor networks have promoted the Mobile Internet of Things (MIoT) paradigm. However, the unique characteristics of MIoT networks expose them to significant security vulnerabilities and threats, necessitating robust cybersecurity measures, including effective attack detection and mitigation techniques. Among these strategies, Artificial Intelligence (AI), and particularly Machine Learning‐ (ML) based approaches, emerge as a pivotal method for bolstering MIoT security. In this paper, we present a comprehensive literature survey regarding the utilization of ML for enhancing security in MIoT. Through an exhaustive review of existing research articles, we analyze the diverse array of ML‐based approaches employed to safeguard MIoT ecosystems and provide a holistic understanding of the current landscape, elucidating the strengths and limitations of prevailing methodologies. We propose a structured taxonomy to categorize recent works in this domain, by distinguishing approaches based on Shallow Supervised Learning (SSL), Shallow Unsupervised Learning (SUL), Deep Learning (DL), and Reinforcement Learning (RL). By delineating existing challenges and potential future directions for cybersecurity in MIoT, we aim to stimulate discourse and inspire novel approaches towards more resilient and secure MIoT ecosystems.

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A Smart Approach for Intrusion Detection and Prevention System in Mobile Ad Hoc Networks Against Security Attacks

Cited by 55 publications

References 32 publications

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

Attack and Defense Strategies for Intrusion Detection in Autonomous Distributed IoT Systems

Machine learning solutions for mobile internet of things security: A literature review and research agenda

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