Enhanced Cooperative Tamper Evident Agent Based Anomaly Intrusion Detection in Ad Hoc Networks

2018 ◽  
Vol 6 (2) ◽  
pp. 7-12
Author(s):  
P. Sreenivsulu ◽  
◽  
Dr. K. Ramesh Reddy ◽  

In recent years with increasing number of wireless devices Ad Hoc Networks become a vital technology. But these networks are highly vulnerable to attacks due to several reasons such as changing topology, open medium and lack of centralized monitoring. Current intrusion detection systems are based on either rule based or behavior model. The efficiency of such IDS is based on how accurate they identify the attacks. In clustering a cluster head is selected as coordinator for performing transmissions in both inter and intra cluster environment. There are many models for choosing a cluster head in Ad Hoc environment. However if the cluster head itself is a compromised node then the cluster head can launch attacks without being detected since its IDS is already malfunctioned. In this paper we propose an “Enhanced Cooperative Tamper Evident Agent Based Anomaly Intrusion Detection System”, which helps in identifying the attacks more accurately even if cluster head is compromised.

Author(s):  
John Felix Charles Joseph ◽  
Amitabha Das ◽  
Boon-Chong Seet ◽  
Bu-Sung Lee

Intrusion detection in ad hoc networks is a challenge because of the inherent characteristics of these networks, such as, the absence of centralized nodes, the lack of infrastructure, and so forth. Furthermore, in addition to application-based attacks, ad hoc networks are prone to attacks targeting routing protocols. Issues in intrusion detection in ad hoc networks are addressed by numerous research proposals in literature. In this chapter, we first enumerate the properties of ad hoc networks which hinder intrusion detection systems. After that, significant intrusion detection system (IDS) architectures and methodologies proposed in the literature are elucidated. Strengths and weaknesses of these works are studied and are explained. Finally, the future directions which will lead to the successful deployment of intrusion detection in ad hoc networks are discussed.


Author(s):  
Chong Han ◽  
Sami Muhaidat ◽  
Ibrahim Abualhaol ◽  
Mehrdad Dianati ◽  
Rahim Tafazolli

Vehicular Ad-Hoc Networks (VANETs) are a critical component of the Intelligent Transportation Systems (ITS), which involve the applications of advanced information processing, communications, sensing, and controlling technologies in an integrated manner to improve the functionality and the safety of transportation systems, providing drivers with timely information on road and traffic conditions, and achieving smooth traffic flow on the roads. Recently, the security of VANETs has attracted major attention for the possible presence of malicious elements, and the presence of altered messages due to channel errors in transmissions. In order to provide reliable and secure communications, Intrusion Detection Systems (IDSs) can serve as a second defense wall after prevention-based approaches, such as encryption. This chapter first presents the state-of-the-art literature on intrusion detection in VANETs. Next, the detection of illicit wireless transmissions from the physical layer perspective is investigated, assuming the presence of regular ongoing legitimate transmissions. Finally, a novel cooperative intrusion detection scheme from the MAC sub-layer perspective is discussed.


Author(s):  
Mannat Jot Singh Aneja ◽  
Tarunpreet Bhatia ◽  
Gaurav Sharma ◽  
Gulshan Shrivastava

This chapter describes how Vehicular Ad hoc Networks (VANETs) are classes of ad hoc networks that provides communication among various vehicles and roadside units. VANETs being decentralized are susceptible to many security attacks. A flooding attack is one of the major security threats to the VANET environment. This chapter proposes a hybrid Intrusion Detection System which improves accuracy and other performance metrics using Artificial Neural Networks as a classification engine and a genetic algorithm as an optimization engine for feature subset selection. These performance metrics have been calculated in two scenarios, namely misuse and anomaly. Various performance metrics are calculated and compared with other researchers' work. The results obtained indicate a high accuracy and precision and negligible false alarm rate. These performance metrics are used to evaluate the intrusion system and compare with other existing algorithms. The classifier works well for multiple malicious nodes. Apart from machine learning techniques, the effect of the network parameters like throughput and packet delivery ratio is observed.


2016 ◽  
Vol 10 (4) ◽  
pp. 1-32 ◽  
Author(s):  
Abdelaziz Amara Korba ◽  
Mehdi Nafaa ◽  
Salim Ghanemi

In this paper, a cluster-based hybrid security framework called HSFA for ad hoc networks is proposed and evaluated. The proposed security framework combines both specification and anomaly detection techniques to efficiently detect and prevent wide range of routing attacks. In the proposed hierarchical architecture, cluster nodes run a host specification-based intrusion detection system to detect specification violations attacks such as fabrication, replay, etc. While the cluster heads run an anomaly-based intrusion detection system to detect wormhole and rushing attacks. The proposed specification-based detection approach relies on a set of specifications automatically generated, while anomaly-detection uses statistical techniques. The proposed security framework provides an adaptive response against attacks to prevent damage to the network. The security framework is evaluated by simulation in presence of malicious nodes that can launch different attacks. Simulation results show that the proposed hybrid security framework performs significantly better than other existing mechanisms.


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