Reputation-Based Opportunistic Routing Protocol Using Q-Learning for MANET Attacked by Malicious Nodes

Ryu, Joonsu; Kim, Sungwook

doi:10.1109/access.2023.3242608

Cited by 9 publications

(7 citation statements)

References 38 publications

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“…Q-learning is an algorithm that uses trial and error in a Markov environment to determine optimal behavior and maximize rewards without an environmental model [24]. In Q-learning, all 𝑄(𝑠, 𝑢) have a respective Q-value.…”

Section: Q-learningmentioning

confidence: 99%

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

2024

IJIES

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Mobile Adhoc Networks (MANETs) are vulnerable to various attacks such as Black Hole Attack (BHA), Gray Hole Attack (GHA), and Wormhole Attacks (WHA). While researchers have focused on detecting and mitigating individual attacks, protection against collaborative attacks is limited. Therefore, this article introduces a new Tunicate Swarm Optimization Q-learning-based Collaborative Attacker Detection Algorithm (TSOQCADA) to identify and prevent collaborative attackers like BHA, GHA, and WHA, thereby improving routing efficiency. This algorithm utilizes feedback about node properties such as energy, reputation, buffer space, transmission delay, and packet transfer rate from all nodes to determine efficient packet routing. In the TSOQ-learning algorithm, the TSO is adopted to set the Q-table values, resulting in faster convergence of Q-learning. First, a Q-table with prior knowledge is trained to enhance searchability. Additionally, a novel selective search mechanism is adopted to improve exploration efficiency and reduce unwanted explorations by considering the correlation between current and target locations. Furthermore, a nonlinear function is designed to achieve a tradeoff between search and use abilities in Q-learning, dynamically changing ε value in the ε-greedy method according to the number of iterations. Thus, the TSOQ-learning can efficiently obtain a routing path by isolating collaborative attackers with low reputation values. Simulation results show that the TSOQCADA achieves a Packet Delivery Ratio (PDR) of 94.8%, Packet Loss Rate (PLR) of 5.2%, energy consumption of 2.53J energy/packet, throughput of 355Kbps, and End-to-End (E2E) delay of 35ms for a network of 100 nodes with 20 malicious nodes in MANET, outperforming the Efficient Trust-based Routing Scheme (ETRS), Hybrid Trust-based Reputation Mechanism (HTRM) and Deep Neural Learned Projective Pursuit Regression-based Watchdog Malicious Node Detection and Isolation (DNLPPR-WMNDI) algorithms.

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Section: Q-learningmentioning

confidence: 99%

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

2024

IJIES

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“…A new MANET routing protocol based on reinforcement learning and named reputation opportunistic routing by Ryu and Kim [16] is proposed (RORQ). This protocol uses game theory to identify and blacklist rogue nodes in a network, allowing for more streamlined traffic flow.…”

Section: Related Workmentioning

confidence: 99%

“…Individuals who are actively seeking something. Create copies based on their own the best position C k Equation ( 15) and modify the selected dimension based on the best global solution Cg Equation (16).…”

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confidence: 99%

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

Vatambeti¹,

Mamidisetti²

2023

IDA

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Smart cities, ITS, supply chains, and smart industries may all be developed with minimal human interaction thanks to the increasing prevalence of automation enabled by machine-type communication (MTC). Yet, MTC has substantial security difficulties because of diverse data, public network access, and an insufficient security mechanism. In this study, we develop a novel IIOT attack detection basis by joining the following four main steps: (a) data collection, (b) pre-processing, (c) attack detection, and (d) optimisation for high classification accuracy. At the initial stage of processing, known as "pre-processing," the collected raw data (input) is normalised. Attack detection requires the creation of an intelligent security architecture for IIoT networks. In this work, we present a learning model that can recognise previously unrecognised attacks on an IIoT network without the use of a labelled training set. An IoT network intrusion detection system-generated labelled dataset. The study also introduces a hybrid optimisation algorithm for pinpointing the optimal LSTM weight when it comes to intrusion detection. When trained on the labelled dataset provided by the proposed method, the improved LSTM outperforms the other models with a finding accuracy of 95%, as exposed in the research.

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“…Q-learning [ 18 ] stands out as a classic algorithm in the realm of reinforcement learning and has found widespread application in packet routing. The Q-learning algorithm has been effectively employed in the design of various types of ad hoc networks, thus encompassing VANETs, among others [ 19 , 20 , 21 , 22 ]. It possesses the capability to make optimal decisions through continuous interaction with the environment, even in the absence of prior knowledge about the environment.…”

Section: Introductionmentioning

confidence: 99%

Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks

Jiang,

Zhu,

Yang

2023

Sensors

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With the rapid development of the intelligent transportation system (ITS), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. The high mobility of vehicles in urban streets poses serious challenges to routing protocols and has a significant impact on network performance. Existing topology-based routing is not suitable for highly dynamic VANETs, thereby making location-based routing protocols the preferred choice due to their scalability. However, the working environment of VANETs is complex and interference-prone. In wireless-network communication, the channel contention introduced by the high density of vehicles, coupled with urban structures, significantly increases the difficulty of designing high-quality communication protocols. In this context, compared to topology-based routing protocols, location-based geographic routing is widely employed in VANETs due to its avoidance of the route construction and maintenance phases. Considering the characteristics of VANETs, this paper proposes a novel environment-aware adaptive reinforcement routing (EARR) protocol aimed at establishing reliable connections between source and destination nodes. The protocol adopts periodic beacons to perceive and explore the surrounding environment, thereby constructing a local topology. By applying reinforcement learning to the vehicle network’s route selection, it adaptively adjusts the Q table through the perception of multiple metrics from beacons, including vehicle speed, available bandwidth, signal-reception strength, etc., thereby assisting the selection of relay vehicles and alleviating the challenges posed by the high dynamics, shadow fading, and limited bandwidth in VANETs. The combination of reinforcement learning and beacons accelerates the establishment of end-to-end routes, thereby guiding each vehicle to choose the optimal next hop and forming suboptimal routes throughout the entire communication process. The adaptive adjustment feature of the protocol enables it to address sudden link interruptions, thereby enhancing communication reliability. In experiments, the EARR protocol demonstrates significant improvements across various performance metrics compared to existing routing protocols. Throughout the simulation process, the EARR protocol maintains a consistently high packet-delivery rate and throughput compared to other protocols, as well as demonstrates stable performance across various scenarios. Finally, the proposed protocol demonstrates relatively consistent standardized latency and low overhead in all experiments.

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Reputation-Based Opportunistic Routing Protocol Using Q-Learning for MANET Attacked by Malicious Nodes

Cited by 9 publications

References 38 publications

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

Routing Attack Detection Using Ensemble Deep Learning Model for IIoT

Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks

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