A Lightweight and Attack Resistant Authenticated Routing Protocol for Mobile Adhoc Networks

In mobile ad hoc networks, by attacking the corresponding routing protocol, an attacker can easily disturb the operations of the network. For ad hoc networks, till now many secured routing protocols have been proposed which contains some disadvantages. Therefore security in ad hoc networks is a controversial area till now. In this paper, we proposed a Lightweight and Attack Resistant Authenticated Routing Protocol (LARARP) for mobile ad hoc networks. For the route discovery attacks in MANET routing protocols, our protocol gives an effective security. It supports the node to drop the invalid packets earlier by detecting the malicious nodes quickly by verifying the digital signatures of all the intermediate nodes. It punishes the misbehaving nodes by decrementing a credit counter and rewards the well behaving nodes by incrementing the credit counter. Thus it prevents uncompromised nodes from attacking the routes with malicious or compromised nodes. It is also used to prevent the denial-of-service (DoS) attacks. The efficiency and effectiveness of LARARP are verified through the detailed simulation studies.Comment: 14 Pages, IJWM

A new procedure for misbehavior detection in vehicular ad-hoc networks using machine learning

Misbehavior detection in vehicular ad hoc networks (VANETs) is performed to improve the traffic safety and driving accuracy. All the nodes in the VANETs communicate to each other through message logs. Malicious nodes in the VANETs can cause inevitable situation by sending message logs with tampered values. In this work, various machine learning algorithms are used to detect the primarily five types of attacks namely, constant attack, constant offset attack, random attack, random offset attack, and eventual attack. Firstly, each attack is detected by different machine learning algorithms using binary classification. Then, the new procedure is created to do the multi classification of the attacks on best chosen algorithm from different machine learning techniques. The highest accuracy in case of binary classification is obtained with Naïve Bayes (100%), decision tree (100%), and random forest (100%) in type1 attack, decision tree (100%) in type2 attack, and random forest (98.03%, 95.56%, and 95.55%) in Type4, Type8 and Type16 attack respectively. In case of new procedure for multi-classification, the highest accuracy is obtained with random forest (97.62%) technique. For this work, VeReMi dataset (a public repository for the malicious node detection in VANETs) is used

A comprehensive survey of V2X cybersecurity mechanisms and future research paths

Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version

LD: Identifying Misbehaving Nodes in MANET

A mobile ad-hoc network is a collection of mobile nodes connected together over a wireless medium without any fixed infrastructure. Unique characteristics of mobile ad-hoc networks such as open peer-to-peer network architecture, shared wireless medium and highly dynamic topology, pose various challenges to the security design. Mobile ad-hoc networks lack central administration or control, making them very vulnerable to attacks or disruption by faulty nodes in the absence of any security mechanisms. Also, the wireless channel in a mobile ad-hoc network is accessible to both legitimate network users and malicious attackers. So, the task of finding good solutions for these challenges plays a critical role in achieving the eventual success of mobile ad-hoc networks. However, the open medium and wide distribution of nodes make MANET vulnerable to malicious attackers. In this case, it is crucial to develop efficient intrusion-detection mechanisms to protect MANET from attacks. Secure routing protocols and mechanisms to detect routing misbehavior in the direct neighborhood exist; however, collusion of misbehaving nodes has not been adequately addressed yet. We present LeakDetector, a mechanism to detect colluding malicious nodes in wireless multihop networks A mobile ad-hoc network is a collection of mobile nodes connected together over a wireless medium without any fixed infrastructure. Unique characteristics of mobile ad-hoc networks such as open peer-to-peer network architecture, shared wireless medium and highly dynamic topology, pose various challenges to the security design. Mobile ad-hoc networks lack central administration or control, making them very vulnerable to attacks or disruption by faulty nodes in the absence of any security mechanisms. Also, the wireless channel in a mobile ad-hoc network is accessible to both legitimate network users and malicious attackers. So, the task of finding good solutions for these challenges plays a critical role in achieving the eventual success of mobile ad-hoc networks. However,the. LeakDetector enables the calculation of the packet-loss ratio for the individual nodes