Attacks In Mobile Ad Hoc Networks Research Articles

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

Auto metric graph neural network optimized with woodpecker mating algorithm for detecting network layer attacks in mobile ad hoc networks

ABSTRACT Nowadays, Mobile Ad hoc Network (MANET) gains more attraction due to its seamless and dynamic use. The existing methods in MANET are plagued by some weaknesses, like higher computation time, undermining their real-time responsiveness. Whereas, lower accuracy in intrusion detection compromises the reliability of these systems, potentially leading to missed threats posing a challenge for effective security measures in dynamic MANET environments. To mitigate these weaknesses, an Auto Metric Graph Neural Network optimized with Woodpecker Mating Algorithm is proposed in this paper for detecting Network Layer Attack in MANET (AGNN-WMA-DNLA-MANET). Initially, the data are gathered from CIC-IDS 2019 dataset. The gathered data undergoes pre-processing to eliminate redundancy and missing values replacement using the Local Least Squares method. Afterward, the preprocessing output is fed to AGNN for classification, which classifies the attack as active, passive, or normal. Then, Woodpecker Mating Algorithm (WMA) is proposed to optimize the AGNN weight parameters to ensure the accurate classification. The proposed technique is executed in NS2 tool. The metrics, like accuracy, false positive rate (FPR) and attack detection rate (ADR), computation time, and RoC is analyzed. The proposed AGNN-WMA-DNLA-MANET approach provides 14.68%, 7.142%, and 4.65% higher accuracy for active attack, 15.19%, 2.77%, and 9.85% higher accuracy for passive attack and 38.18%, 12.02%, and 7.59% better accuracy for normal compared with existing methods, such as Dependable intrusion detection scheme under deep convolutional neural network (DCNN-MANET), deep learning-based intrusion detection scheme for MANET (AE-DNN-MANET), and Benchmarking of machine learning for anomaly based intrusion detection scheme (ML-MANET), respectively.

SECURING MOBILE AD-HOC NETWORKS USING ROBUST SECURED IPS ROUTING APPROACH THROUGH ATTACK IDENTIFICATION AND ELIMINATION IN MANETS

Mobile Ad-hoc Networks (MANETs) are susceptible to various security threats due to their decentralized and dynamic nature. Among these threats, Distributed Denial of Service (DDoS) and sibling attacks pose significant challenges to the integrity and availability of network services. This paper presents a novel approach for securing MANETs against DDoS and sibling attacks through a robust Intrusion Prevention System (IPS) integrated with a secured routing mechanism. The proposed methodology leverages residual transfer learning to adapt a pre-trained model for intrusion detection to the MANET environment, enhancing its effectiveness in identifying and mitigating attacks. The problem of securing MANETs against DDoS and sibling attacks is exacerbated by the lack of centralized infrastructure and the dynamic topology of the network. Traditional security mechanisms designed for wired networks are often ineffective in MANETs due to their reliance on centralized control and communication. This research addresses this gap by proposing an IPS solution tailored specifically for MANETs, capable of detecting and preventing attacks without relying on centralized coordination. By utilizing residual transfer learning, the proposed methodology addresses the challenge of limited labeled data in the MANET domain. Transfer learning enables the adaptation of knowledge from a pre-trained model on non-MANET data to improve the performance of intrusion detection in MANET environments. The integration of the IPS with a secured routing approach ensures that detected attacks are efficiently handled within the network, minimizing their impact on performance and ensuring continued operation. Experimental results demonstrate the effectiveness of the proposed approach in mitigating DDoS and sibling attacks in MANETs. The integrated solution achieves high detection rates while minimizing false positives, thereby enhancing the security and resilience of MANETs against evolving threats.

A fuzzy based Q – learning approach to prevent sinkhole attacks in MANET (FQ – SPM)

Mobile Ad-Hoc Networks (MANET) are considered one of the significant and growing areas in today’s scenario of technological advancement. It is an infrastructure-less and dynamic ad-hoc network that requires a connection between nodes to deliver packets and data. However, its design adopts a connection-less approach, at the helm of which no monitoring node exists. Hence, the threat of maintaining the network’s security remains an uphill task. Many attacks have been attempted to breach the protection of the MANET. This paper discusses one of the most potent attacks in a MANET infrastructure, the Sinkhole Attack. We try to minimize the possibility of a sinkhole attack using a Fuzzy Q-learning-based approach, a reinforcement learning technique. The results are encouraging, suggesting that sinkhole attacks can be minimized to a great extent after the adaption of the proposed approach.

Developing a reliable route protocol for mobile self-organization networks

Mobile ad hoc networks (MANETs), which correspond to a novel wireless technology, are widely used in Internet of Things (IoT) systems such as drones, wireless sensor networks, and military or disaster relief communication. From the perspective of communication and data collection, the success rate of collaborations between nodes in mobile ad hoc networks and reliability of data collection mainly depend on whether the nodes in the network operate normally, namely, according to the established network rules. However, mobile ad hoc networks are vulnerable to attacks targeting transmission channels and nodes owing to their dynamic evolution, openness, and distributed characteristics. Therefore, during the network operation, it is necessary to classify and detect the behavior and characteristics of each node. However, most existing research only analyzes and considers responses against a single or small number of attacks. To address these issues, this article first systematically analyzed and classified common active attacks in MANETs. Then, a node trust model was proposed based on the characteristics of various attacks; subsequently, a new secure routing protocol, namely, TC-AODV, was proposed. This protocol has minimal effect on the original communication dynamics and can effectively deal with Packet drop, wormhole, Session hijacking, and other main attacks in MANETs. The NS3 simulation results show that the proposed routing protocol attains good transmission performance, can effectively identify various attacks and bypass malicious nodes, and securely complete the communication process.

An Empirical Study of Destructive Nodes Characterization in Wireless Networks

Mobile Ad hoc Networks (MANETs) are deployed in various new public and domestic environments, going up to new needs in terms of concert and effectiveness. According to the wireless dynamic nature, some needed services like security for network maintenance, trust-based routing and resource management among network nodes are not carried out as good as expected. Also, the ad-hoc networks are vulnerable to secure communications and multiple attacks can participate in various layers of a network stack. Destructive nodes have chances to change or discard routing specifications, sometimes it can send false routes to capture source data packets to pass through themselves. Some protocols have been designed to address the complication from secure data communication. Even though, a secure protocol cannot handle all kinds of attack detection and elimination in all situations. New secure data communication wireless protocols need to focus these challenges, because security care is not natively built in MANET. Therefore, in this research paper, analysis of destructive nodes characterization and impacts on wireless networks investigated the multiple attacks behaviour, activities of the attacks all through neighbour selection, path establishment from source to destination, creating awareness of attack presence detection knowledge to the normal devices during path discovery and data transmission mechanisms. Legitimate nodes need to be building with secure transmission knowledge to make sure trusted communication, ensure validation, honesty, and privacy to classify the attacks in MANETs.

Efficient security framework against sybil attack in mobile adhoc network using EE-OLSR protocol scheme

Efficient security framework against sybil attack in mobile adhoc network using EE-OLSR protocol scheme

Proposed Naïve Bayes- Genetic algorithm to detect black hole attacks in MANETs.

The threats and attacks on mobile ad hoc networks (MANETs) are significant, making it difficult for traditional security systems to provide complete protection. Therefore, an efficient hybrid clustering approach must be designed to construct an intrusion detection system (IDS) that is suitable for this network. IDSs are crucial in MANETs due to the presence of black hole threats, which are the most significant vulnerabilities in this network.Our suggestion is to use a hybrid classifier to detect black hole attacks in MANETs. This can be achieved by using the Naïve Bayes algorithm for clustering to select the cluster head, and modify the Genetic Algorithm to identify the node responsible for a black hole attack on the optimal path. Finally, the confidence server instructs the destination node. If permitted, it alerts the collection head; otherwise, it identifies the node as a malicious one in the black hole attack within each cluster. The results of our proposed technique show that it has improved package damage rate, quantity, package distribution ratio, whole network interruption, and standardized directing capacity parameters compared to current black hole detection approaches.
 The simulation was done in KDD cup 99 to carry out black hole attack and trace file obtained is used as dataset for training and testing purpose using visual basic.

DIWGAN Optimized with Namib Beetle Optimization Algorithm for Intrusion Detection in Mobile Ad Hoc Networks

ABSTRACT Mobile ad hoc network (MANET) plays a major role in wireless devices such as defense and flooding. Despite their smart applications, MANET faces more security issues than traditional wired and wireless networks on account of their distinct features, such as no central coordination, dynamic topology, temporal network life, and the nature of wireless communication. To overcome these issues, this manuscript proposes a Dual Interactive Wasserstein Generative Adversarial Network optimized with Namib Beetle Optimization Algorithm is proposed for intrusion detection and preventing attacks in MANET. By utilizing the One Way Hash Chain Function, mobile users first register with the Trusted Authority. Each mobile user sends a finger vein biometric along with their user id, latitude, and longitude for authentication verification. The packet analyzer, feature extraction, preprocessing, and classification are the four parts that make up intrusion detection. To determine if any attack patterns have been identified, the packet analyzer is examined. This is executed using a Type 2 Fuzzy Controller that deems packet header information. Anisotropic diffusion Kuwahara filtering techniques is time series is taken into consideration in the preprocessing unit. The battle royal optimization algorithm is utilized in the feature extraction unit to acquire a better collection of features for packet categorization. The classification unit classifies the packets into five categories: DoS, Probe, U2R, R2L, and Anomaly using the proposed technique. Finally, the proposed method provides 26.26%, 15.57%, 32.9% higher accuracy, 33.06%, 23.82%, and 38.84% lesser delay analysed to the existing models.

AODV-based Defense Mechanism for Mitigating Blackhole Attacks in MANET

Mobile Ad hoc Networks (MANETs) are decentralized and self-configuring networks composed of mobile devices that communicate without a fixed infrastructure. However, the open nature of MANETs makes them vulnerable to various security threats, including blackhole attacks, where malicious nodes attract and discard network traffic without forwarding it to its intended destination. Mitigating blackhole attacks is crucial to ensure the reliability and security of communication in MANETs. This paper focuses on the development and evaluation of AODV (Ad hoc On-Demand Distance Vector)-based defence mechanisms for effectively mitigating blackhole attacks in MANETs, while simultaneously addressing energy efficiency and environmental sustainability. AODV is a widely used routing protocol in MANETs due to its on-demand nature and low overhead. However, it lacks built-in security mechanisms, making it susceptible to attacks. We incorporate energyaware route selection, solar-powered routing, collaborative energy sharing, energy-efficient intrusion detection, green routing optimization, and energy harvesting from environmental sources. By considering energy consumption and environmental factors in the route selection process, our defense mechanism not only enhances the security of the network but also contributes to energy conservation and reduced environmental impact. To evaluate the effectiveness of the proposed defence mechanisms, extensive simulations and performance analyses are conducted using network simulation tools. Through simulation-based evaluations, we demonstrate the effectiveness of our approach in achieving robust blackhole attack mitigation while extending the network’s lifetime and minimizing its carbon footprint. Our research offers valuable insights into the development of energy-efficient and environmentally sustainable solutions for securing MANETs in the face of evolving security threats.

Machine Learning and Trust Based AODV Routing Protocol to Mitigate Flooding and Blackhole Attacks in MANET

The latest advancements in wireless communication promotes the researchers to concentrate more in the expansion of Mobile Ad hoc Networks (MANETs), where nodes communicate each other to provide the demanded real time infotainment services. However, due to wireless connectivity and decentralized architecture, constructing secure routing is still a challenging issue in MANETs. Therefore, in this paper, A Machine Learning and Trust Based AODV Routing Protocol (ML-AODV) to mitigate Flooding and Blackhole Attacks in MANET is proposed. Primarily, cooperative intermediate nodes in the network are selected through trust estimation at each node to avoid (flooding attack) unnecessary transmission of huge number of routing packets to nonexistent destinations. Towards this, the node distinct metrics like Hop Count (HC), Residual Energy (RE), and Link Expiration Time (LET) are considered for trust estimation. The nodes with highest trust value are chosen as trusted relay forwarders to find the optimal paths with reduced packet drop rate (Blackhole attack). Further, finds best optimal path to eliminate the energy disparity and delay for packet transmission using machine learning based Artificial Neural Network (ANN) with Support Vector Machine (SVM) classifier. Here, SVM is used to identify the intruder in the selected route to improve efficiency and intrusion detection accuracy. The performance of the proposed ML-AODV is carried out using NS-2 and compared over existing routing schemes under attack. Based on the simulation results, the proposed ML based AODV exhibits improved throughput to 4% and reliability to 44% over existing approaches. In addition, delay, routing overhead, packet loss ratio are reduced to 12%, 15%, and 10% respectively.

Timer Entrenched Baited Scheme to Locate and Remove Attacks in MANET

The Mobile Ad-hoc Network (MANET) is a dynamic topology that provides a variety of executions in various disciplines. The most sticky topic in organizational fields was MANET protection. MANET is helpless against various threats that affect its usability and accessibility. The dark opening assault is considered one of the most far-reaching dynamic assaults that deteriorate the organization's execution and reliability by dropping all approaching packages via the noxious node. The Dark Opening Node aims to deceive any node in the company that wishes to connect to another node by pretending to get the most delicate ability to support the target node. Ad-hoc On-demand Distance Vector (AODV) is a responsive steering convention with no corporate techniques to locate and destroy the dark opening center. We improved AODV by incorporating a novel compact method for detecting and isolating lonely and collaborative black-hole threats that utilize clocks and baits. The recommended method allows MANET nodes to discover and segregate black-hole network nodes over dynamic changes in the network topology. We implement the suggested method's performance with the help of Network Simulator (NS)-3 simulation models. Furthermore, the proposed approach comes exceptionally near to the original AODV, absent black holes in terms of bandwidth, end-to-end latency, error rate, and delivery ratio.

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

Mobile Ad-hoc Networks (MANET) usage across the globe is increasing by the day. Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes. The trust values are estimated based on the reputation values of each node in the network by using different mechanisms. However, these mechanisms have various challenging issues which degrade the network performance. Hence, a novel Quality of Service (QoS) Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work. Initially, the QoS-based trust estimation is proposed by using a Fuzzy logic scheme. The trust value of each node is estimated by using each node’s reputation values which are determined based on the fuzzy membership function values and utilizing QoS parameters such as residual energy, bandwidth, node mobility, and reliability. This mechanism prevents only the black hole attack in the network during transmission. But, the gray hole attacks are not identified which in turn increases the packet drop rate significantly. Hence, the gray hole attack is also detected based on the Kullback-Leibler (KL) divergence method used for estimating the statistical measures. Additional QoS metrics are considered to prevent the gray hole attack, such as packet loss, packet delivery ratio, and delay for each node. Thus, the proposed mechanism prevents both black hole and gray hole attacks simultaneously. Finally, the simulation results show that the effectiveness of the proposed mechanism compared with the other trust-aware routing protocols in MANET.

Dipper Throated Optimization for Detecting Black-Hole Attacks in MANETs

In terms of security and privacy, mobile ad-hoc network (MANET) continues to be in demand for additional debate and development. As more MANET applications become data-oriented, implementing a secure and reliable data transfer protocol becomes a major concern in the architecture. However, MANET’s lack of infrastructure, unpredictable topology, and restricted resources, as well as the lack of a previously permitted trust relationship among connected nodes, contribute to the attack detection burden. A novel detection approach is presented in this paper to classify passive and active black-hole attacks. The proposed approach is based on the dipper throated optimization (DTO) algorithm, which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’ quality of service. A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron (DTO-MLP), and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical (LEACH) clustering technique. MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights of minor features. This hybrid method is primarily designed to combat active black-hole assaults. Using the LEACH clustering phase, however, can also detect passive black-hole attacks. The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach. For diverse mobility situations, the results demonstrate up to 97% detection accuracy and faster execution time. Furthermore, the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

Relaxed hybrid routing to prevent consecutive attacks in mobile ad-hoc networks

Relaxed hybrid routing to prevent consecutive attacks in mobile ad-hoc networks

A Model to Prevent Gray Hole Attack in Mobile Ad-Hoc Networks

A Model to Prevent Gray Hole Attack in Mobile Ad-Hoc Networks

Relaxed hybrid routing to prevent consecutive attacks in mobile ad-hoc networks

Relaxed hybrid routing to prevent consecutive attacks in mobile ad-hoc networks

EBR: Routing Protocol to Detect Blackhole Attacks in Mobile Ad Hoc Networks

The presence of malevolent nodes in mobile ad hoc networks (MANETs) would lead to genuine security concerns. These nodes may disturb the routing process or deform the pattern of the data packets passing through the network. The MANET is extremely liable to attacks, owing to its characteristics of the network framework, such as the absence of infrastructure, moveable topology, and a centralized control unit. One of the most common attacks in MANETs is the blackhole attack. MANET nodes are susceptible to spectacular degradation of network performance in the presence of such attacks. In this regard, detecting or preventing deceitful nodes that will launch blackhole attacks is a challenge in MANETs. In this paper, we propose an Enhanced Blackhole Resistance (EBR) protocol to identify and resist nodes that are responsible for blackhole attacks. EBR can avoid congested traffic by passing the data packets through a safe route with the minimum RTT. The EBR protocol uses a combination of time to live (TTL) and round trip time (RTT), which is also called a TR mechanism, to detect the blackhole attacks. Our algorithm does not require any cryptographic or authentication mechanisms. Simulation results prove that EBR behaves better than other protocols in terms of throughput, end-to-end delay, packet delivery ratio, energy, and routing overhead.

Prevention of Blackhole Attack in MANET using Certificate less Signature Scheme

Mobile AdHoc Networks (MANETs) are the network of self-configuring nodes. Such nodes communicate through single as well as multi-hop modes without the aid of any centralized administrator or pre-existing network infrastructure. Due to this reason, MANETs have gained a highly significance in modern wireless networking technologies. Such networks are extremely vulnerable to one of the security attack i.e. blackhole attack. It is a malicious node when an attacker is able to send a fake route reply to the originator of a route request packet. Such attackers discard the legitimate packets and replay packets in the whole network thereby adversely affecting network performance. Most of the security protocols for MANET are using bilinear pairing methods to provide security against security attacks and it takes high computing cost for the computation of pairing operations. Nowadays, researchers are using certificate-less signature schemes in distributed environments to provide efficient security. This signature scheme is very popular because it does not use any certificate authority for the management of security certificates. In this paper, we proposed an efficient technique to prevent blackhole attack in MANET using RSA-based certificateless signature scheme without using any bilinear pairing operations. Our scheme provides security against forgery and blackhole attacks and is evaluated under a discrete logarithm problem. Proposed scheme outperforms existing schemes in terms of these metrics viz., throughput, packet delivery ratio, routing overhead and end-to-end delay when we are varying mobility and fixed percentage of malicious nodes. Our proposed scheme not only detects or prevents the blackhole attack but it is also capable to provide important security services viz., integrity, authentication and non-repudiation.

FSSAM: A Five Stage Security Analysis Model for Detecting and Preventing Wormhole Attack in Mobile Ad-Hoc Networks Using Adaptive Atom Search Algorithm

FSSAM: A Five Stage Security Analysis Model for Detecting and Preventing Wormhole Attack in Mobile Ad-Hoc Networks Using Adaptive Atom Search Algorithm