Node activity based trust and reputation estimation approach for secure and QoS routing in MANET

Achieving safe and secure communication in MANETs is a key challenge due to its dynamic nature. A number of security studies disclose that reputation management systems are able to be effectual with less overhead. The reputation of a node is calculated by using automated assessment algorithms depend on predefined trust scheme. This paper proposes a Node Activity-based Trust and Reputation estimation (NA-TRE) approach for the security and QoS routing in MANET. NA-TRE aims to find trust estimation and reputation of a node. The NA-TRE approach monitors the activity changes, packet forwarding or dropping in a node to find the status of the node. The various activities of a node like Normal State (NS), Resource Limitation State (RS) and Malicious State (MS) are monitored. This status of a node is helpful in computing trust and reputation. In this paper NA-TRE approach compared with existing protocols AODV, FACE and TMS to evaluate the efficiency of MANET. The experiment results show that 20% increasing of throughput, 10% decrease of overhead and end to end delay

Analysis of Behavioral Characteristics of Jammers to Detect Malicious Nodes in Mobile ADHOC Networks

Wireless ADHOC Networks are used to establish a wireless connection between two computing devices without the need for a Wi-Fi access point or router. This network is decentralized and uses omnidirectional communication media, which makes it more vulnerable to certain types of attacks compared to wired networks. Jamming attacks, a subset of denial-of-service (DoS) attacks, involve malicious nodes that intentionally interfere with the network, blocking legitimate communication. To address this issue, the proposed method analyzes various characteristics of nodes, such as packets sent, received, and dropped, at each node. Using the packet delivery ratio and packet drop ratio, the method detects jamming nodes from normal nodes, improving network performance. The network is simulated in NS2 environment

A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks

Mobile ad hoc networks (MANETs) have experienced rapid growth in their use for various military, medical, and commercial scenarios. This is due to their dynamic nature that enables the deployment of such networks, in any target environment, without the need for a pre-existing infrastructure. On the other hand, the unique characteristics of MANETs, such as the lack of central networking points, limited wireless range, and constrained resources, have made the quest for securing such networks a challenging task. A large number of studies have focused on intrusion detection systems (IDSs) as a solid line of defense against various attacks targeting the vulnerable nature of MANETs. Since cooperation between nodes is mandatory to detect complex attacks in real time, various solutions have been proposed to provide cooperative IDSs (CIDSs) in efforts to improve detection efficiency. However, all of these solutions suffer from high rates of false alarms, and they violate the constrained-bandwidth nature of MANETs. To overcome these two problems, this research presented a novel CIDS utilizing the concept of social communities and the Dempster-Shafer theory (DST) of evidence. The concept of social communities was intended to establish reliable cooperative detection reporting while consuming minimal bandwidth. On the other hand, DST targeted decreasing false accusations through honoring partial/lack of evidence obtained solely from reliable sources. Experimental evaluation of the proposed CIDS resulted in consistently high detection rates, low false alarms rates, and low bandwidth consumption. The results of this research demonstrated the viability of applying the social communities concept combined with DST in achieving high detection accuracy and minimized bandwidth consumption throughout the detection process

Efficiency and Accuracy Enhancement of Intrusion Detection System Using Feature Selection and Cross-layer Mechanism

The dramatic increase in the number of connected devices and the significant growth of the network traffic data have led to many security vulnerabilities and cyber-attacks. Hence, developing new methods to secure the network infrastructure and protect data from malicious and unauthorized access becomes a vital aspect of communication network design. Intrusion Detection Systems (IDSs), as common widely used security techniques, are critical to detect network attacks and unauthorized network access and thus minimize further cyber-attack damages. However, there are a number of weaknesses that need to be addressed to make reliable IDS for real-world applications. One of the fundamental challenges is the large number of redundant and non-relevant data. Feature selection emerges as a necessary step in efficient IDS design to overcome high dimensionality problem and enhance the performance of IDS through the reduction of its complexity and the acceleration of the detection process. Moreover, detection algorithm has significant impact on the performance of IDS. Machine learning techniques are widely used in such systems which is studied in details in this dissertation. One of the most destructive activities in wireless networks such as MANET is packet dropping. The existence of the intrusive attackers in the network is not the only cause of packet loss. In fact, packet drop can occur because of faulty network. Hence, in order detect the packet dropping caused by a malicious activity of an attacker, information from various layers of the protocol is needed to detect malicious packet loss effectively. To this end, a novel cross-layer design for malicious packet loss detection in MANET is proposed using features from physical layer, network layer and MAC layer to make a better detection decision. Trust-based mechanism is adopted in this design and a packet loss free routing algorithm is presented accordingly

Detection and Prevention System towards the Truth of Convergence on Decision Using Aumann Agreement Theorem

AbstractThe Detection and Prevention system against many attacks has been formulated in Mobile ad hoc networks to secure the data and to provide the uninterrupted service to the legitimate clients. The formulation of opinion of neighbors or belief value or Trust value plays vital role in the detection system to avoid attacks. The attack detection system always extracts the behaviors of nodes to identify the attack patterns and prediction of future attacks. The False positives and false negatives plays vital role on identification of attackers accurately without any false positives and negatives .Our system uses the Aumann agreement theorem for convergence of Truth on opinion based on the bound of confidence value, such that truth consensus will maintained, The accuracy of system will be enhanced through this methodolog

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

Spectrum sharing security and attacks in CRNs: a review

Cognitive Radio plays a major part in communication technology by resolving the shortage of the spectrum through usage of dynamic spectrum access and artificial intelligence characteristics. The element of spectrum sharing in cognitive radio is a fundament al approach in utilising free channels. Cooperatively communicating cognitive radio devices use the common control channel of the cognitive radio medium access control to achieve spectrum sharing. Thus, the common control channel and consequently spectrum sharing security are vital to ensuring security in the subsequent data communication among cognitive radio nodes. In addition to well known security problems in wireless networks, cognitive radio networks introduce new classes of security threats and challenges, such as licensed user emulation attacks in spectrum sensing and misbehaviours in the common control channel transactions, which degrade the overall network operation and performance. This review paper briefly presents the known threats and attacks in wireless networks before it looks into the concept of cognitive radio and its main functionality. The paper then mainly focuses on spectrum sharing security and its related challenges. Since spectrum sharing is enabled through usage of the common control channel, more attention is paid to the security of the common control channel by looking into its security threats as well as protection and detection mechanisms. Finally, the pros and cons as well as the comparisons of different CR - specific security mechanisms are presented with some open research issues and challenges

Reactive protocols for unified user profiling for anomaly detection in mobile Ad Hoc networks

The Next Generation mobile network expected to be fully automated to meet the growing need for data rates and quality in communication. These prodigious demands have also increased the amount of data being handled in these wireless networks. The cellular networks can leverage vital data about the user and the network conditions providing all-inclusive visibility and intelligence in communication. Emerging analytic technologies such as big data and neural networks have been used to unearth vital insight from network traffic to assist intelligent models in routing packets. Reactive protocols are an emerging model in the intelligent routing of traffic in ad-hoc networks. In this paper, we first utilize the reactive protocols to route traffic in a wireless network while analyzing anomalous behavior. In the case of anomaly detection in wireless communication, combined performance indicators to identify outliers. The detected outliers been compared with the ground data and routes created using the reactive protocols. The combination of reactive protocols and the key performance indicators in network performance uncovered anomalies leading to segregation of these traffic in routing. From the results, it is evident that an abrupt surge in the traffic indicated an anomaly and identify the areas of interest in a network especially for resource and path allocation and fault avoidance. A MATLAB GUI was used to simulate the reactive protocols for routing of traffic and generation of data sets that analyze in Microsoft Excel to characterize the key performance indicators of the network

Emergence in the security of protocols for mobile ad-hoc networks

This thesis is concerned with the study of secure wireless routing protocols, which have been deployed for the purpose of exchanging information in an adhoc networking enviromnent. A discrete event simulator is developed, utilising an adaptive systems modelling approach and emergence that aims to assess networking protocols in the presence of adversarial behaviour. The model is used in conjunction with the characteristics that routing protocols have and also a number of cryptographic primitives that can be deployed in order to safeguard the information being exchanged. It is shown that both adversarial behaviour, as well as protocol descriptions can be described in a way that allows for them to be treated as input on the machine level. Within the system, the output generated selects the fittest protocol design capable of withstanding one or more particular type of attacks. As a result, a number of new and improved protocol specifications are presented and benchmarked against conventional metrics, such as throughput, latency and delivery criteria. From this process, an architecture for designing wireless routing protocols based on a number of security criteria is presented, whereupon the decision of using particular characteristics in a specification has been passed onto the machine level