Wormhole Attack Research Articles

Enhancing MANET Security: A Collaborative Dynamic Multi-Agent Approach for Wormhole Attack Detection and Mitigation (CDMA-Worm)

The proposed research focus on the Collaborative Dynamic Multi-Agent Wormhole Detection and Anomaly Mitigation Framework for Secure Networks (CDMA-Worm). It is a comprehensive solution designed to protect large dynamic networks from wormhole attacks and other malicious activities. The architecture is built around three key algorithms namely- Dynamic Multi-Agent Generation and Broadcasting (DMGB), Anomaly Detection and Isolation (ADI), and Collaborative Wormhole Detection and Network-Wide Threat Mitigation(CWD-NWTM). The first algorithm, Dynamic Multi-Agent Generation and Broadcasting, focuses on the creation of agents that are tested for integrity and distributed across the network. These agents work collaboratively to monitor network activity in real-time, detecting any anomalies. The second algorithm, Anomaly Detection and Isolation, identifies irregularities by analyzing agent behavior, calculating affinity scores, and isolating malicious nodes to prevent potential damage. The third algorithm, Collaborative Wormhole Detection and Network-Wide Threat Mitigation, targets wormhole attacks by tracking Round-Trip Time (RTT) and validating neighbor sets. If wormhole nodes are detected, they are isolated and blocked, with joint threat levels computed to coordinate mitigation efforts across the network.The CDMA-Worm framework ensures high detection accuracy, efficient node isolation, and minimal impact on network latency. Its scalability makes it suitable for the networks with different number of nodes that, offering robust protection against wormhole and other attacks. By leveraging multi-agent collaboration and dynamic anomaly detection, the architecture adapts to evolving network conditions, providing continuous and effective security.

ABCD: advanced blockchain DSR algorithm for MANET to mitigate the different security threats

Mobile ad hoc networks (MANETs) facilitate data communication across multiple nodes and hop stations, characterized by their dynamic topology. This inherent flexibility, however, makes MANETs vulnerable to various security threats, notably blackhole and wormhole attacks, where malicious nodes can intercept and manipulate data. This study investigates the security vulnerabilities of MANETs, particularly against blackhole, Sybil, and wormhole attacks, and introduces the Advanced Blockchain Dynamic Source Routing (ABCD) algorithm to address these challenges. Motivated by the need for robust and decentralized security solutions in MANETs, the proposed algorithm integrates blockchain technology and homomorphic encryption to secure data communication without intermediate decryption. The ABCD algorithm leverages Dijkstra’s algorithm for optimal routing and employs a tamper-proof, decentralized data storage approach. Comparative analysis under attack scenarios reveals that the ABCD algorithm outperforms the standard DSR protocol across multiple quality of service metrics, demonstrating a significant improvement in MANET security over equivalent studies. The packet delivery rate is also improved from 81 to 92% using the modified ABCD algorithm.

A Trust-Based Multipath Congestion-Aware Routing Technique to Curb Wormhole Attacks for Mobile Nodes in WSNs

A Trust-Based Multipath Congestion-Aware Routing Technique to Curb Wormhole Attacks for Mobile Nodes in WSNs

Trust Attributes in Multi-Path Congestion Avoidance Techniques to Curb Wormhole Attacks in Wireless Sensor Networks

Trust Attributes in Multi-Path Congestion Avoidance Techniques to Curb Wormhole Attacks in Wireless Sensor Networks

Wormhole Attack Mitigation in Wireless Network Using Propagation Delay

Wormhole Attack Mitigation in Wireless Network Using Propagation Delay

Enhancing RPL Security in the Internet of Things for Preventive Network Threats with Machine Learning and NANTAR

"Internet of Things" refers to a collection of physical devices that are connected to the Internet and can communicate with one another. Rank, Sinkhole, and Wormhole attacks can seriously impair the performance of the Routing Protocol for Low-Power and Lossy Networks (RPL), which is essential for effective data transfer in Internet of Things networks. Due to its lightweight core, RPL cannot support resource-intensive and computationally intensive security implementation techniques. As a result, security attacks, which may be roughly divided into RPL-specific and sensor-network-inherited assaults, can affect both IoT and RPL. They take advantage of RPL resources and components, such as maintenance methods, routing settings, administrative messages, and network sensor components. The research presented here suggests an innovative machine learning-based method to increase RPL's security. We present a Novel Algorithm for Network Traffic Analysis and Response (NANTAR). This innovative approach that uses AI-based techniques to optimize the security and efficacy of RPL routing, in conjunction with a reinforcement learning module for dynamic and adaptive routing. This framework significantly improves packet false positive rate, lowers latency, and boosts network throughput while maintaining minimal jitter.It effectively secures the RPL protocol in IoT-enabled wireless sensor networks by achieving a high detection rate, few false positives, and quick reaction to security problems

Detection of Wormhole Attack Via Bio-Inspired Ant Colony Optimization Based Trust Model in WSN Assisted IoT Network

Detection of Wormhole Attack Via Bio-Inspired Ant Colony Optimization Based Trust Model in WSN Assisted IoT Network

Secure positioning of wireless sensor networks against wormhole attacks

Secure positioning of wireless sensor networks against wormhole attacks

Wormhole attack detection and mitigation model for Internet of Things and WSN using machine learning.

The Internet of Things (IoT) is revolutionizing diverse sectors like business, healthcare, and the military, but its widespread adoption has also led to significant security challenges. IoT networks, in particular, face increasing vulnerabilities due to the rapid proliferation of connected devices within smart infrastructures. Wireless sensor networks (WSNs) comprise software, gateways, and small sensors that wirelessly transmit and receive data. WSNs consist of two types of nodes: generic nodes with sensing capabilities and gateway nodes that manage data routing. These sensor nodes operate under constraints of limited battery power, storage capacity, and processing capabilities, exposing them to various threats, including wormhole attacks. This study focuses on detecting wormhole attacks by analyzing the connectivity details of network nodes. Machine learning (ML) techniques are proposed as effective solutions to address these modern challenges in wormhole attack detection within sensor networks. The base station employs two ML models, a support vector machine (SVM) and a deep neural network (DNN), to classify traffic data and identify malicious nodes in the network. The effectiveness of these algorithms is validated using traffic generated by the NS3.37 simulator and tested against real-world scenarios. Evaluation metrics such as average recall, false positive rates, latency, end-to-end delay, response time, throughput, energy consumption, and CPU utilization are used to assess the performance of the proposed models. Results indicate that the proposed model outperforms existing methods in terms of efficacy and efficiency.

Detection of Wormhole Attack in Wireless Sensor Networks: A Survey

Detection of Wormhole Attack in Wireless Sensor Networks: A Survey

A Study on Different Types of Blackhole and Wormhole Attack in MANET

Ad-hoc mobile networking (MANET) is an important technology in wireless networks with mobile nodes. Where nodes are collaborate with each other in a distributed manner and to provide multiple communications between sources and destination node. In general, the basic assumption of MANET is that each node is a trusted node. However, in a real case, there are some untrusted nodes that misbehave and attack the network like a black hole and wormhole. Where black hole nodes attract all traffic, providing false information about small hop count path to destination and with very sequence number to destinations. In wormhole attack, attacker collect data packets at one place and tunnels this data packet to another place in the network. For this attacker will give false information to the source node that the attacker node has the shortest path between sender and receiver. Source node will eventually choose that path for transmission. Once the path is established, both the nodes will drop all the incoming and outgoing packets and this causes denial of service attack, using wormhole attacks in this paper, we will discuss black hole and worm hole attack and discuss the different techniques which is used to detect and prevent these two attacks by different researchers.

A novel Q-learning-based secure routing scheme with a robust defensive system against wormhole attacks in flying ad hoc networks

A novel Q-learning-based secure routing scheme with a robust defensive system against wormhole attacks in flying ad hoc networks

JamholeHunter: On detecting new wormhole attack in Opportunistic Mobile Networks

JamholeHunter: On detecting new wormhole attack in Opportunistic Mobile Networks

Secure Cooperative Routing in Wireless Sensor Networks

In wireless sensor networks (WSNs), sensor nodes are randomly distributed to transmit sensed data packets to the base station periodically. These sensor nodes, because of constrained battery power and storage space, cannot utilize conventional security measures. The widely held challenging issues for the network layer of WSNs are the packet-dropping attacks, mainly sinkhole and wormhole attacks, which focus on the routing pattern of the protocol. This thesis presents an improved version of the second level of the guard to the system, intrusion detection systems (IDSs), to limit the hostile impact of these attacks in a Low Energy Adaptive Clustering Hierarchy (LEACH) environment. The proposed system named multipath intrusion detection system (MIDS) integrates an IDs with ad hoc on-demand Multipath Distance Vector (AOMDV) protocol. The IDS agent uses the number of packets transmitted and received to calculate intrusion ratio (IR), which helps to mitigate sinkhole attacks and from AOMDV protocol round trip time (RTT) is computed by taking the difference between route request and route reply time to mitigate wormhole attack. MATLAB simulation results show that this cooperative model is an effective technique due to the higher packet delivery ratio (PDR), throughput, and detection accuracy. The proposed MIDS algorithm is proven to be more efficient when compared with an existing LEACH-based IDS system and MS-LEACH in terms of overall energy consumption, lifetime, and throughput of the network.

Enhancing Mobile Ad Hoc Network Security: A Study on Termite Colony Optimization for Wormhole Detection

An ordinary, self-orbiting communication network with the ability to manage mobile nodes is called a mobile ad-hoc network (MANET). The vulnerability of the MANET to various threats and attacks is addressed by numerous proposed protocols. The malicious node takes advantage of these weaknesses to launch attacks, such as wormhole attacks, particularly when nodes are mobile and the network's topology is unstable. The termite method for detecting wormhole attacks in MANETs is presented in this research. Keywords: MANET, Denial of service, Wormhole attack, Termite approach

Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are essential in many areas, from healthcare to environmental monitoring. However, WSNs are vulnerable to routing attacks that might jeopardize network performance and data integrity due to their inherent vulnerabilities. This work suggests a unique method for enhancing WSN security through the detection of routing threats using feed-forward artificial neural networks (ANNs). The proposed solution makes use of ANNs' learning capabilities to model the network's dynamic behavior and recognize routing attacks like black-hole, gray-hole, and wormhole attacks. CICIDS2017 is a heterogeneous dataset that was used to train and test the proposed system in order to guarantee its robustness and adaptability. The system's ability to recognize both known and novel attack patterns enhances its efficacy in real-world deployment. Experimental assessments using an NS2 simulator show how well the proposed method works to improve routing protocol security. The proposed system's performance was assessed using a confusion matrix. The simulation and analysis demonstrated how much better the proposed system performs compared to the existing methods for routing attack detection. With an average detection rate of 99.21% and a high accuracy of 99.49%, the proposed system minimizes the rate of false positives. The study advances secure communication in WSNs and provides a reliable means of protecting sensitive data in resource-constrained settings.

A Novel Detection and Localization Scheme for Wormhole Attack in Internet of Things

A Novel Detection and Localization Scheme for Wormhole Attack in Internet of Things

Detection and isolation of wormhole nodes in wireless ad hoc networks based on post-wormhole actions

The wormhole attack is one of the most treacherous attacks projected at the routing layer that can bypass cryptographic measures and derail the entire communication network. It is too difficult to prevent a priori; all the possible countermeasures are either too expensive or ineffective. Indeed, literature solutions either require expensive hardware (typically UWB or secure GPS transceivers) or pose specific constraints to the adversarial behavior (doing or not doing a suspicious action). The proposed solution belongs to the second category because the adversary is assumed to have done one or more known suspicious actions. In this solution, we adopt a heuristic approach to detect wormholes in ad hoc networks based on the detection of their illicit behaviors. Wormhole and post wormhole attacks are often confused in literature; that’s why we clearly state that our methodology does not provide a defence against wormholes, but rather against the actions that an adversary does after the wormhole, such as packet dropping, tampering with TTL, replaying and looping, etc. In terms of contributions, the proposed solution addresses the knock-out capability of attackers that is less targeted by the researcher’s community. In addition, it neither requires any additional hardware nor a change in it; instead, it is compatible with the existing network stack. The idea is simulated in ns2.30, and the average detection rate of the proposed solution is found to be 98-99%. The theoretical time to detect a wormhole node lies between 0.07-0.71 seconds. But, from the simulation, the average detection and isolation time is 0.67 seconds. In term of packet loss, the proposed solution has a relatively overhead of ≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx$$\\end{document} 22%. It works well in static and mobile scenarios, but the frame losses are higher in mobile scenarios as compared to static ones. The computational complexity of the solution is O(n). Simulation results advocate that the solution is effective in terms of memory, processing, bandwidth, and energy cost. The solution is validated using statistical parameters such as Accuracy, Precision, F1-Score and Matthews correlation coefficient (Mcc\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_{cc}$$\\end{document}).

Multi‐hop anonymous payment channel network based on onion routing

AbstractPayment channel networks (PCNs) are one of the most promising off‐chain solutions to the blockchain scalability problem. However, most current payment channel schemes suffer from overly ideal routing assumptions or fail to provide complete path privacy protection. To address the above problems, the multi‐hop anonymous and privacy‐preserving PCN scheme are analyzed and a multi‐hop anonymous PCN based on onion routing is proposed based on it. The scheme removes the routing assumptions of the original scheme, designs an onion routing‐based payment channel network, and modifies the way relay nodes construct elliptic curve discrete logarithmic problems to resist wormhole attacks. Finally, the security analysis shows that the scheme in this paper has atomicity, relational anonymity and resistance to wormhole attacks. The latest bilinear pairing anonymous multi‐hop payment (EAMHL+) scheme is used to compare communication and computing overhead. The results show that the scheme in this paper requires less communication overhead and is more efficient in terms of computational overhead when the average hop number of the payment channel network is 3 hops and the network diameter is 6 hops.

Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors

Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors