Security In Mobile Ad Hoc Networks Research Articles

Secure Routing and Hybrid SqueezenetFQuantumNN for Intrusion Detection in MANET

An efficient Intrusion Detection System (IDS) is essential to secure Mobile Ad-hoc Networks (MANETs) against malevolent attacks. The present IDS still faces challenges in enhancing detection accuracy and in decreasing the false alarm rate. To overcome the above issues, a secure routing protocol for intrusion detection in MANET has been proposed here. In the proposed model, initially, MANET is simulated, which is followed by routing based on Dynamic Source Routing (DSR) routing protocol. Then, intrusion detection is done at Base Station (BS). At the BS, the log files are acquired from the dataset at first and it is normalized using Quintile normalization to transform the data into a structured form. Then, the desired features are selected in the feature selection phase using Wave–Hedges metrics for removing redundant and irrelevant data. Finally, intrusion detection is performed using the proposed hybrid SqueezenetFQuantum Neural Network (SqueezenetFQuantumNN) which is devised by the fusion of Squeeze Net and Quantum Neural Network (QuantumNN). The effectiveness of the proposed SqueezenetFQuantumNN is measured depending on evaluation metrics and is found to attain accuracy of 95.72%, True Positive Rate (TPR) of 93.42%, and True Negative Rate (TNR) of 91.39%.

An intelligent security mechanism in mobile Ad-Hoc networks using precision probability genetic algorithms (PPGA) and deep learning technique (Stacked LSTM)

The Mobile Ad-hoc Networks (MANETs) have gained a significant attention in the recent years due to their proliferation and huge application purposes. To defend from many types of modern cyber dangers like Distributed Denial of Service (DDoS) attacks, Advanced Persistent Threats (APTs), Insider Threats, Ransomware, Zero-Day Exploits, Social Engineering tactics, and etc is not easy when it comes to keeping MANETs security. These complex assaults focus on network infrastructure, take advantage of weaknesses in communication protocols and control user actions. Although there have been improvements in intrusion detection systems (IDS), it is still difficult to fully safeguard MANETs. The purpose of this research is to create advanced methods that can accurately find and decrease attacks inside MANETs. Applying Sensor-based Feature Extraction (SFE) to extract useful network features such as Received Signal Strength Indication (RSSI) and Time of Travel (TOT) from datasets NSL-KDD and CICIDS-2017. Utilizing the fresh method of Precise Probability Genetic Algorithm (PPGA) optimization for removing unrelated details, which enhances precision in detecting attacks. Predicting normal and attacking labels by applying Stacked Recurrent Long Short Term Memory (SRLSTM) method, fine-tuning classifier's parameters in every layer to improve outcomes. In order to authenticate and compare the suggested methods with current attack detection tactics, this study will make use of various evaluation measurements. The NSL-KDD, which is a benchmark dataset in network intrusion detection research, has a wide variety of network traffic data with instances that are labeled as normal and different attacks. CICIDS-2017 is similar because it contains an extensive dataset too - this includes real-world traces from network traffic where there's both regular activity and harmful actions. The purpose is to enhance the existing status of MANET security so as it can withstand more strongly against cyber dangers. According to the outcomes, it is analyzed that the attack detection accuracy has improved greatly 99 % when compared to other methods, as shown by the detailed assessment measurements. Better handling of big datasets with top detection accuracy reduces the time needed 8.9 s for training and testing models. Decrease in misclassification results and better ability to differentiate normal network actions from harmful intrusions. Improved resistance of MANETs to different cyber dangers, guaranteeing the safety and dependability of network communication in changing and non-centralized settings.

Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization

ABSTRACT Mobile ad hoc networks (MANETs) facilitate rapid deployment due to independence from established infrastructure, relying instead on wireless technology where each node functions as a source, destination, or intermediary router. However, existing techniques fail to adequately address security and privacy concerns inherent in MANETs, rendering them impractical for real-world deployment. To tackle these challenges, this paper proposes the Blockchain Based Trusted Distributed Routing Scheme for MANET using Latent Encoder Coupled Generative Adversarial Network Optimized with Binary Emperor Penguin Optimizer (LEGAN-BEPO-BCMANET). This scheme leverages blockchain technology to establish a fair proof-of-reputation system, ensuring trusted and decentralized routing based on authenticated blockchain token transactions. By integrating Latent Encoder Coupled Generative Adversarial Network (LEGAN) optimized with Binary Emperor Penguin optimizer (BEPO), the scheme enhances routing efficiency and security. A comprehensive security analysis is performed focusing on aspects such as double-spending prevention, transaction distinguishability, routing information integrity, and self-modification resilience. The proposed approach is implemented in NS3 software, with evaluation metrics including throughput of blockchain token transactions and average energy consumption recorded. Overall, the LEGAN-BEPO-BCMANET scheme offers a robust solution to the security and efficiency challenges faced by MANETs, paving the way for practical deployment in real-world scenarios. The performance of the proposed LEGAN-BEPO-BCMANET technique attains 29.786%, 19.25%, 22.93%, 27.21%, 31.02%, 26.91%, and 25.61% greater throughput, compared to existing methods like Blockchain-based BATMAN protocol utilizing MANET with an ensemble algorithm (BATMAN-MANET), Block chain-based trusted distributed routing scheme with optimized dropout ensemble extreme learning neural network in MANET (DEELNN-MANET), A secured trusted routing utilizing structure of a new directed acyclic graph-blockchain in MANET internet of things environment (DAG-MANET), An Optimized Link State Routing Protocol with Blockchain Framework for Efficient Video-Packet Transmission and Security over MANET (OLSRP-MANET), Auto-metric Graph Neural Network based Blockchain Technology for Protected Dynamic Optimum Routing in MANET (AGNN-MANET) and Data security-based routing in MANETs under key management process (DSR-MANET) respectively.

Enhancing Data Security in Mobile Ad-hoc Network (MANETs) Using Trust-Based Approach with RSSI and Fuzzy Logic

Enhancing Data Security in Mobile Ad-hoc Network (MANETs) Using Trust-Based Approach with RSSI and Fuzzy Logic

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

Performance optimization of multipath K-AOMDV protocol using SVM against blackhole attack

<p>This research focuses on the Ad Hoc On-Demand Multi-Path Routing (AOMDV) protocol, which is preferred for its improved efficiency compared to a single-path routing protocol in mobile ad hoc networks (MANETs). However, identifying attackers in such networks is a complicated task due to malicious nodes providing optimistic, forward-looking optimistic responses. In this study, the author proposes a novel security solution, the K-AOMDV (KNN- Ad Hoc On-Demand Multi-Path Routing protocol) that uses K-means clustering to prevent routing misbehaviour. The efficiency of the proposed K-AOMDV routing protocol is analyzed using supervised machine learning approach to predict optimal routes with minimal packet hops between nodes. The proposed algorithm has a high accuracy rate of 0.99%, 80% true positives, and 80% recall. It communicates the black hole attacker’s node identification (ID) into the network, ensuring that the attacker will not participate in the routing method in the future. The privacy domain in MANET is the main focus of this research, and the proposed solution affiliates an effective approach to enhancing the security of MANETs.</p>

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.

APPROACHES FOR SOLVING ROUTING AND SECURITY ISSUES IN MOBILE AD-HOC NETWORKS (MANETs): A REVIEW

Mobile Ad hoc Networks (MANETs) have been very popular for some years now owing to their ability to allow communication in dynamic and infrastructure-less environments. However, the unique characteristics of MANETs, such as node mobility, limited power resources, and absence of centralized infrastructure, pose challenges in ensuring efficient routing and robust security. This paper presents a review of the existing techniques aimed at improving routing protocols and security in MANETs. Scholarly articles, conference papers, and technical reports published in notable research outlets were sourced. Then, the papers were categorized into two main areas: routing techniques and security mechanisms based on the target of this work. Regarding routing techniques, this review discusses the evolution of traditional routing protocols, including proactive, reactive, and hybrid approaches, and highlights their strengths and limitations. Moreover, the review presents some recent advancement such as location-based, Quality of Service (QoS)-aware, and energy-efficient routing protocols, which address specific challenges in MANETs. In terms of security mechanisms, this review provides an overview of the various threats and attacks that MANETs are susceptible to, including black hole, wormhole, and Sybil attacks. The review then examines the countermeasures proposed in the literature to tackle the security challenges. This article further highlights the emerging trends and research directions in the field of MANETs which include blockchain-based security, machine learning-assisted routing, and Internet of Things (IoT) integration. It is believed that this review can provide further insights to researchers in this domain.

Trust-Based Routing Selection Policy on Mobile Ad-Hoc Network Using Aodv Routing Protocol

This study presents an enhanced Ad-hoc On-demand Distance Vector (AODV) routing protocol, termed Proposed_TAODV, designed to improve security in Mobile Ad-Hoc Networks (MANETs) of 150 nodes by incorporating trust-based mechanisms. Through a comprehensive simulation, the Proposed_TAODV is evaluated against existing AODV and Dynamic Source Routing (DSR) protocols under conditions of increasing malicious node presence. The results reveal that Proposed_TAODV maintains a higher Packet Delivery Ratio, experiences lower Average End-to-End Delay, and achieves greater Throughput compared to the benchmarks, indicating its superior performance and robustness. The integration of Direct Trust Evaluation, Indirect Trust Evaluation, and Trust Aggregation methods into the AODV protocol clearly enhances the MANET's resilience to security threats, establishing the Proposed_TAODV as a promising approach for securing MANETs against various forms of attacks and network disruptions.

A Trust and Node Capability Model for Reliable and Secure MANET Communication

The Mobile Ad-hoc Network (MANET) is a rapidly deployable network. That is valuable for industrial and domestic applications due to flexible, mobile, and wireless communication. But the network is constrained with resources and security. In this paper, we are presenting a node capability based trusted routing named TNC-AODV for MANET. It is a hybrid approach for maintaining route reliability and security. The model is composed of the property of node capability and Trust. The Node capability is defined by the quality of service parameters like remaining energy, available bandwidth, buffer length, and mobility pattern. The aim is to ensure the discovery of reliable routes. Additionally, the trust is implemented by using a local and global trust for securing the network. The TNC-AODV is implemented through modification of AODV routing. That routing technique has been tested on three security threats namely Black-hole, wormhole, and DOS flooding attack. The simulation has been carried out using the NS2 simulator. The experimental results demonstrate that TNC-AODV provides security against attacks. Additionally, improve the packet delivery ratio, and throughput. Finally, the possible and feasible future extension of the work has also been proposed.

Fuzzy based inference system with ensemble classification based intrusion detection system in MANET

Mobile ad hoc networks (MANET) have become one of the hottest research areas in computer science, including in military and civilian applications. Such applications have formed a variety of security threats, particularly in unattended environments. An Intrusion detection system (IDS) must be in place to ensure the security and reliability of MANET services. These IDS must be compatible with the characteristics of MANETs and competent in discovering the biggest number of potential security threats. In this work, a specialized dataset for MANET is implemented to identify and classify three types of Denial of Service (DoS) attacks: Blackhole, Grayhole and Flooding Attack. This work utilized a cluster-based routing algorithm (CBRA) in MANET.A simulation to gather data, then processed to create eight attributes for creating a specialized dataset using Java. Mamdani fuzzy-based inference system (MFIS) is used to create dataset labelling. Furthermore, an ensemble classification technique is trained on the dataset to discover and classify three types of attacks. The proposed ensemble classification has six base classifiers, namely, C4.5, Fuzzy Unordered Rule Induction Algorithm (FURIA), Multilayer Perceptron (MLP), Multinomial Logistic Regression (MLR), Naive Bayes (NB) and Support Vector Machine (SVM). The experimental results demonstrate that MFIS with the Ensemble classification technique enables an enhancing security in MANET’s by modeling the interactions among a malicious node with number of legitimate nodes. This is suitable for future works on multilayer security problem in MANET.

Lightweight blockchain-assisted intrusion detection system in energy efficient MANETs

This paper focuses on achieving high-level security in Mobile Adhoc Networks (MANET) by incorporating Blockchain technology-based Intrusion Detection systems (IDS). The existing works on MANET security focus on either security prevention or detection. Thus, the security level attained by the prior works is unable to cope with the increasing attacks. To resolve this main issue, this research paper introduces Lightweight Blockchain assisted Intrusion Detection System (LB-IDS) which jointly prevents and detects the attacks held on mobile networks. Initially, the network nodes are authenticated by a lightweight Blockchain-based Multi-Factor Authentication (LBMFA) scheme. This procedure prevents the malicious nodes entry to the network. Then, data packets are transmitted through the optimal route which is selected by Multi-Objective Strawberry Optimization (MOSO) algorithm. The collected data packets are fed into IDS which classifies the data into normal and malicious packets. For IDS, we proposed Deep Q-Learning (DQL) algorithm which takes actions by learning the environment. As the mitigation step, the Blockchain is updated with the trust value according to the data packet classification. For such continuous monitoring, K-Mode Clustering (KMC) algorithm is proposed. On the whole, the proposed work improves the network security in MANET through Prevention, Detection, and Mitigation. The results of the presented work attains better security level, packet delivery ratio (PDR), energy efficiency, delay, and detection accuracy.

Anti-intrusion strategy MANET inspired by the bacterial foraging optimization algorithm

Mobile ad hoc networks (MANET) are reflexivity, fast, versatile wireless networks that are particularly useful when traditional radio infrastructure is unavailable, such as during outdoor events, natural disasters, and military operations. Security may be the weakest link in a network due to its dynamic topology, which leaves it susceptible to eavesdropping, rerouting, and application modifications. More security problems with MANET exist than with its service Quality of Service (QoS). Therefore, intrusion detection, which controls the system to find further security issues, is strongly suggested. Keeping an eye out for intrusions is essential to forestall future attacks and beef up security. If a mobile node loses its power supply, it may be unable to continue forwarding packets, which depends on the structure's condition. The proposed study presents a security of trust and optimization that conserves energy methods for MANETs based on integrating the K means algorithm and Bacteria Foraging Optimization Algorithm (KBFOA). This work proposes a method for quickly and accurately determining which nodes should serve as Cluster Heads (CHs) by K means algorithm. This approach aims to choose a node with a high Sustainable Cell (SC) rate as the Header of a Cluster (HC). Each node will calculate its SC according to its unique factors, such as energy consumption, degree, remaining energy, mobility, and distance from the HC and base stations. The security of MANETs is improved by the inclusion of an algorithm in the proposed approach that detects and eliminates rogue nodes. This suggested approach will increase network stability and performance using a high-sustainable cluster head. The proposed approach will be achieved the highest reliability of clustering rate of 97%. The intended research will be accomplished Maximum security measures rate will be accomplished by 96%, the maximum rate of data transmission will be obtained of 96%, and greater efficient detection of malicious nodes ratio will be enhanced by 95%, lower energy consumption rate will be achieved by 0.09 m joules.

Anomaly Recognition in Wireless Ad-hoc Network by using Ant Colony Optimization and Deep Learning

As a result of lower initial investment, greater portability, and lower operational expenses, wireless networks are rapidly replacing their wired counterparts. The new technology that is on the rise is the Mobile Ad-Hoc Network (MANET), which operates without a fixed network infrastructure, can change its topology on the fly, and requires no centralised administration to manage its individual nodes. As a result, MANETs must focus on network efficiency and safety. It is crucial in MANET to pay attention to outliers that may affect QoS settings. Nonetheless, despite the numerous studies devoted to anomaly detection in MANET, security breaches and performance difficulties keep coming back. There is an increased need to provide strategies and approaches that help networks be more safe and robust due to the wide variety of security and performance challenges in MANET. This study presents outlier detection strategies for addressing security and performance challenges in MANET, with a special focus on network anomaly identification. The suggested work utilises a dynamic threshold and outlier detection to tackle the security and performance challenges in MANETs, taking into account metrics such as end-to-end delay, jitter, throughput, packet drop, and energy usage.

Ensemble-based cryptography for soldiers’ health monitoring using mobile ad hoc networks

Information technology acts an important role in gathering, transmitting with executing data from areas of disaster-prone such as the battlefield and international borders. In addition to the country’s security, the soldier needs protection by defending himself with advanced weapons such as a bomb detector. This paper provides the capability to track the whereabouts and health of soldiers who have been lost or injured on the battlefield. It assists in reducing the time, searching and rescuing operation efforts of the military control room. This paper implements a system for health-condition monitoring that sends soldiers’ health parameters, such as the electrocardiogram (ECG), blood oxygen level, pulse rate, and temperature, to the control room via a Mobile Ad hoc Network (MANET). Body parameters are sensed utilizing various body sensors fixed to the bodies of soldiers. The body parameters are broadcasted to the control room via MANET devices at the path. To preserve the health parameters data of soldiers from enemies while data transmission, this paper also proposes a cryptographic ensemble approach. This approach combines Symmetric Key Encryption, and Identity Based Encryption (IBE) with Identity Based Signature (IBS). The experimental result shows proposed cryptographic ensemble provides high security compared with existing MANET security algorithms.

Secure and efficient data transfer in AODV routing protocol using RSA encryption

Mobile Ad-Hoc Networks (MANETs) are recognised as a key emerging technology for use whenever cellular communications are either infeasible, inefficient, or cost-ineffective. MANETS are composed of nodes that are arranged in a self-organizing manner, without a central device like a switch or router. The nodes communicate with each other over wireless links and cooperate in a distributed fashion. Smartphones are the most ideal network nodes in many scenarios where traditional networks are unavailable, but building a secure network with them is not easy. This paper extensively uses the existing possibilities to build a secure MANET. The designed MANET has been built on an NS3 simulator, with the base protocol being Ad-hoc On-demand Distance Vector (AODV) and message encryption is done by using RSA Algorithm. The Secure AODV protocol extension is implemented using the RSA extension.

A Neighbor Trust Weight Based Cryptography for Multi Key Distribution for Improving Quality of Service in MANETS

A Mobile Ad-Hoc Network (MANET) is a self-configuring network that provides temporary connections to several wireless nodes. Trust mechanisms are employed in routing protocols to quickly locate a safe path. Because of its openness and complexity, MANET can be attacked in a number of ways. To begin mitigating potential security risks, a number of different cryptographic key generation strategies are explored. A key management system for MANET security is available with different encryption techniques. Identity with Trust Level based Cryptography Model (ITLCM) is used to generate multiple keys and distribute these to particular targets. At this stage, key management protocols are essential to any secure group architecture of communication. Because of its dynamic topology which extensively affects its application, the multi key management is an essential task. When compared to more conventional methods of protecting a network, MANET security is entirely novel. Security routing protocol implementation is difficult since it requires the production and distribution of multiple keys. To provide both connection and message protection without relying on third parties, the Neighbor Trust Weight based Routing Model (NTWRM) is designed. In the proposed model, a trusted node is selected to monitor all of the nodes in the routing process to create a stable multi-key distribution environment that enhances MANET performance. In comparison with traditional methods, the proposed model shows that its findings are better than the existing ones.

Detection and Isolating Flood Rushing Attacks in WSN Using Timer Based Trolling Technique

Abstract: The Mobile Ad hoc Network (MANET) is a wireless network component that offers a variety of applications in numerous industries. Perhaps the biggest problem with networks was the security of MANET. MANET is defenceless against many attacks that affect its availability and functionality. The black hole attack is one of the most dangerous dynamic assaults since it prevents the network from being presented and reliable because the malicious node drops all incoming data packets. By ensuring that it always has the best path to the target node, the black hole attack aims to trick every node in the network that wants to communicate with another node. A black hole attack into the network cannot be detected or prevented by the responsive routing protocol AODV. In this study, we improved the AODV routing protocol by using a different, more lightweight technique that recognises and detects single and multiple black hole attacks using hop count and trolling. In this study, we present a security method for MANET against solitary and group black hole attacks. The blackhole attack is a packet-dropping attack that acts like a regular node during connection formation and drops all data packets after forwarding a bogus reply from the destination to the sender. With the help of other regular nodes, one or more malicious nodes in this assault establish a safe environment. The proposed IDS (Intrusion Detection System) locates nodes that are not continually forwarding data packets but are still present in the network and offers secure communication in a dynamic network.

MACO-DHKE Based Secure Data Transmission in MANETs

Mobile ad hoc networks (MANETs) are the self-sufficient nodes with their unique characteristics, such as open wireless mediums and self-motivated topology, which suffers from security weaknesses. Due to the complexity of the MANET security architecture, it is difficult to detect and prevent security issues in the wired networks. Hence, this paper proposes a secure and QoS-aware data transmission in the MANET that improves the efficiency of the transmission. The optimal route, which is the shortest possible path, is chosen using the modified ant colony optimization algorithm (MACO algorithm) and the secure transmission in MANET is ensured using the Diffie-Hellman key-exchange algorithm (DHKE strategy). The key exchange method is used to improve the security of the transmission of the MANET. This paper shows a high transmission rate on a secure path with a higher PDR and throughput.

Intrusion Detection in Manet Through Machine Learning Approach

Mobile ad hoc networks (MANETs) have evolved into a leading multi-hop infrastructure less wireless communication technology where every node performs the function of a router. Ad- hoc networks have been spontaneously and specifically designed for the nodes to communicate with each other in locations where it is either complex or impractical to set up an infrastructure. The overwhelming truth is that with IoT emergence, the number of devices being connected every single second keeps increasing tremendously on account of factors like scalability, cost factor and scalability which are beneficial to several sectors like education, disaster management, healthcare, espionage etc., where the identification and allocation of resources as well as services is a major constraint. Nevertheless, this infrastructure with dynamic mobile nodes makes it more susceptible to diverse attack scenarios especially in critical circumstances like combat zone communications where security is inevitable and vulnerabilities in the MANET could be an ideal choice to breach the security. Therefore, it is crucial to select a robust and reliable system that could filter malicious activities and safeguard the network. Network topology and mobility constraints poses difficulty in identifying malicious nodes that can infuse false routes or packets could be lost due to certain attacks like black hole or worm hole. Hence our objective is to propose a security solution to above mentioned issue through ML based anomaly detection and which detects and isolates the attacks in MANETs. Most of the existing technologies detect the anomalies by utilizing static behavior; this may not prove effective as MANET portrays dynamic behavior. Machine learning in MANETs helps in constructing an analytical model for predicting security threats that could pose enormous challenges in future. Machine learning techniques through its statistical and logical methods offers MANETs the learning potential and encourages towards adaptation to different environments. The major objective of our study is to identify the intricate patterns and construct a secure mobile ad-hoc network by focusing on security aspects by identifying malicious nodes and mitigate attacks. Simulation-oriented results establish that the proposed technique has better PDR and EED in comparison to the other existing techniques.