A L-IDS against Dropping Attack to Secure and Improve RPL Performance in WSN Aided IoT

Soni, Gaurav; Sudhakar, R

doi:10.1109/spin48934.2020.9071118

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…Te anomaly IDS is making use of the location and temporal constraints to identify the malicious devices. Diferent types of attacks are in communication such as DoS attacks [25][26][27], Sybil attacks [28,29], selective forwarding attacks [30][31][32], worm whole attacks [33], black hole attacks [34][35][36], sink hole attacks [37][38][39], jamming attacks, and false data injection attacks [40]. Terefore, many investigators paid their thoughts in the detection of one or more of these attacks by proposing diferent methods for intrusion detection and prevention.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

Kumar

Selvi

Kannan

2023

Computational Intelligence and Neuroscience

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The Internet of Things (IoT) is a distributed system which is made up of the connections of smart objects (things) that can continuously sense the events in their sensing domain and transmit the data via the Internet. IoT is considered as the next revolution of the Internet since it has provided vast improvements in day-to-day activities of humans including the provision of efficient healthcare services and development of smart cities and intelligent transport systems. The IoT environment, by the application of suitable security mechanisms through efficient security management techniques, intrusion detection systems provide a wall of defence against the attacks on the Internet and on the devices connected with Internet by effective monitoring of the Internet traffic. Therefore, the intrusion detection system (IDS) is a resolution proposed by the researchers to monitor and secure the IoT communication. In this work, a meticulous analysis of the security of IoT networks based on quality-of-service metrics is performed for deploying intrusion detection systems by carrying out experiments on secured communication and measuring the network’s performance based on comparing them with the existing security metrics. Finally, we propose a new and effective IDS using a deep learning-based classification approach, namely, fuzzy CNN, for improving the security of communication. The major and foremost advantages of this system include an upsurge in detection accuracy, the accurate detection of denial of service (DoS) attacks more efficiently, and the reduction of false positive rates.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

Kumar

Selvi

Kannan

2023

Computational Intelligence and Neuroscience

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“…where, 𝑃 𝐴 𝑡 is represent the attack coefficient at iteration t and 𝑃 𝑏 represent the cruise coefficient and 𝑅 ⃗ 1 and 𝑅 ⃗ 2 represent the random vectors between the interval of [0,1]. And‖𝐴 𝑖 ‖ and ‖𝑏 ⃗ 𝑖 ‖ represent Euclidean norm of attack that is defined as (10).…”

Section: First Level Idsmentioning

confidence: 99%

“…Moreover, this process helps to reduce the complexity and also increase the detection rate [9]. IDS has adversarial intrusion; detection of adversarial intrusions is difficult because they look like normal packets [10]. Adversarial learning is used for detecting these types of intrusions which learning adversarial environments and detecting adversarial intrusions in an accurate manner [11].…”

Section: Introductionmentioning

confidence: 99%

Three level intrusion detection system based on conditional generative adversarial network

Abdulameer¹,

Musa²,

Salim

2023

IJECE

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<span lang="EN-US">Security threat protection is important in the internet of things (IoT) applications since both the connected device and the captured data can be hacked or hijacked or both at the same time. To tackle the above-mentioned problem, we proposed three-level intrusion detection system conditional generative adversarial network (3LIDS-CGAN) model which includes four phases such as first-level intrusion detection system (IDS), second-level IDS, third-level IDS, and attack type classification. In first-level IDS, features of the incoming packets are extracted by the firewall. Based on the extracted features the packets are classified into three classes such as normal, malicious, and suspicious using support vector machine and golden eagle optimization. Suspicious packets are forwarded to the second-level IDS which classified the suspicious packets as normal or malicious. Here, signature-based intrusions are detected using attack history information, and anomaly-based intrusions are detected using event-based semantic mapping. In third-level IDS, adversary packets are detected using CGAN which automatically learns the adversarial environment and detects adversary packets accurately. Finally, proximal policy optimization is proposed to detect the attack type. Experiments are conducted using the NS-3.26 network simulator and performance is evaluated by various performance metrics which results that the proposed 3LIDS-CGAN model outperforming other existing works.</span>

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“…Also, using MANET in multimedia applications is an interesting [13]. Limited bandwidth, limited range of nodes, random motion of mobile nodes, energy efficient routing and symmetric nodes are the challenges in MANET In the current scenario, mobile ad hoc network are gaining t of their demands in various applications such as the internet of things (IoT) [16]. The sensor nodes are also able to collect and forward information.…”

Section: Routing Protocols In Manetmentioning

confidence: 99%

“…In the current scenario, mobile ad hoc network are gaining popularity due to the advancement of their demands in various applications such as the internet of things (IoT) [16]. The sensor nodes are also able to collect and forward information.…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of an Energy-Efficient Load Balancing and Bandwidth Aware Adaptive Multipath N-Channel Routing Approach in MANET

2022

Self Cite

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Mobile Ad Hoc Network (MANET) is a collection of spontaneous nodes that form a dynamic network without any centralized administration. Routing decisions for mobile communication is a challenging task because the continuous movement of nodes increases the routing overhead and energy consumption. Numerous studies have been conducted in the field of MANET to reduce the routing decision overhead and energy consumption. Their concepts are good for improving efficiency in terms of load balancing, congestion control, and energy consumption. This article provides new concepts that are able to give better outcomes. This paper, proposes an adaptive Multipath Multichannel (N-channel) Energy Efficient (MMEE) routing approach in which route selection strategies are dependent on predictive energy consumption per packet (calculated accustomed data delivery), available bandwidth, queue length, and channel utilization. Multipath provides multiple routes and balances network load, whereas multichannel reduces network collisions via a channel ideal assignment mechanism. In the multichannel mechanism, link bandwidth is divided into multiple sub-channels. Multiple source nodes access the channel bandwidth in a simultaneous manner that minimizes the network collision. The collaborative multipath multichannel mechanism provides more than one path between a single source or multiple sources to the destination without collision and congestion. The path selection is based on the MMEE routing approach. In the proposed MMEE, a load and bandwidth aware routing method selects the path, based on node energy and predicts their lifetime, which increases the network reliability. The result shows the comparative analysis between various multichannel medium access techniques such as MMAC, Parallel Rendezvous Multi Channel Medium Access Protocol (PRMMAC), Quality of Service Ad hoc On Demand Multipath Distance Vector (QoS-AOMDV), Q-learning based Multipath Routing (QMR), Topological Change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV), and the proposed MMEE method. The outcome concludes that the MMEE method performs better than other schemes. The proposed MMEE protocol provides better performance in terms of percentage of data received, throughput, and minimum energy utilization.

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A L-IDS against Dropping Attack to Secure and Improve RPL Performance in WSN Aided IoT

Cited by 11 publications

References 17 publications

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

Three level intrusion detection system based on conditional generative adversarial network

Design and Analysis of an Energy-Efficient Load Balancing and Bandwidth Aware Adaptive Multipath N-Channel Routing Approach in MANET

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