Malicious Nodes In Mobile Ad Hoc Networks Research Articles

An artificial immune system‐based algorithm for selfish node detection in Mobile Ad Hoc Networks (MANETs)

AbstractThe Mobile Ad Hoc Network (MANET) depending on dynamic distributed topology presents several challenges such as decentralized infrastructure. In MANET, each node act as a host as well as a router to exchange packet. From Source to destination, the MANET nodes offer the ability of transmission to send, receive, and route traffic. Moreover, the presence of selfish nodes or malicious nodes in MANETs significantly degrades network performance. Identifying and isolating such nodes poses a formidable challenge. Consequently, this research introduces a security model founded on the principles of an Artificial Immune System (AIS) to detect and defend against Selfish Node (SN) attacks. This research work employs the principles of AIS to categorize a node's behavioral state based on the Mature Context Immune Antigen Rate (MCIAR). Subsequently, a hybrid protocol named Reliable History‐dependent Resource Conscious Clustered and Defensive AODV (RRCC‐DAODV) is introduced to detect Selfish Nodes (SNs) and counteract their attacks. Within the RRCC algorithm, the identification of selfish nodes relies on both Trust Value (TV) and Residual Energy (RE). Additionally, the proposed DAODV protocol incorporates the V‐detector algorithm to enhance defense mechanisms against potential attacks. The simulation of the proposed model is carried out in NS3. The proposed mechanism achieves a detection ratio of 94.05%. The simulation outcomes demonstrate the excellence of the implemented system. This superiority is observed when compared to other standard protocols, as indicated by the parameters of throughput, residual energy, packet delivery ratio, and delay.

A Review on Cooperative Bait Based Intrusion Detection in MANET

Mobile Ad Hoc Network (MANET) is a kind of wireless network where a number of mobile nodes are wirelessly connected without any fixed infrastructure. Several attacks or intrusions may occur because of the presence of selfish or malicious nodes in MANETs while performing data transmission from one mobile node to another. Vulnerability occurs in the mobile nodes of the MANETs, when an intruder node easily captures the traffic pattern of the network. A Cooperative Bait Detection Scheme is introduced for detecting the selfish node and malicious node in MANET. CBDS contains the more benefit for proactive and reactive protection constructions using routing scheme to identify the black hole attacks in MANETs. With the design of CBDS, the source node selects the adjacent node address which is taken as bait destination address for transmitting bait malicious node to reply message. Then the malicious nodes are detected from participating the routing operation based on reverse tracing technique for providing better security.

A Cross-Layer, Anomaly-Based IDS for WSN and MANET.

Intrusion detection system (IDS) design for mobile adhoc networks (MANET) is a crucial component for maintaining the integrity of the network. The need for rapid deployment of IDS capability with minimal data availability for training and testing is an important requirement of such systems, especially for MANETs deployed in highly dynamic scenarios, such as battlefields. This work proposes a two-level detection scheme for detecting malicious nodes in MANETs. The first level deploys dedicated sniffers working in promiscuous mode. Each sniffer utilizes a decision-tree-based classifier that generates quantities which we refer to as correctly classified instances (CCIs) every reporting time. In the second level, the CCIs are sent to an algorithmically run supernode that calculates quantities, which we refer to as the accumulated measure of fluctuation (AMoF) of the received CCIs for each node under test (NUT). A key concept that is used in this work is that the variability of the smaller size population which represents the number of malicious nodes in the network is greater than the variance of the larger size population which represents the number of normal nodes in the network. A linear regression process is then performed in parallel with the calculation of the AMoF for fitting purposes and to set a proper threshold based on the slope of the fitted lines. As a result, the malicious nodes are efficiently and effectively separated from the normal nodes. The proposed scheme is tested for various node velocities and power levels and shows promising detection performance even at low-power levels. The results presented also apply to wireless sensor networks (WSN) and represent a novel IDS scheme for such networks.

A detection and prevention system against collaborative attacks in Mobile Ad hoc Networks

Mobile Ad hoc Networks (MANETs) are vulnerable to various kinds of threats due to their dynamic nature and lack of a central point of control. Collaborative attacks occur when multiple attackers synchronize their actions to disrupt a target network. A wormhole attack is one of the most severe collaborative attacks, as it can harm a network in several ways. Wormhole attacks are very difficult to detect, as they are launched by two or more nodes in collaboration with each other and they work in two phases. In the first phase, malicious nodes try to convince legitimate nodes to transfer data through them in order to get acces to more routes. In the second phase, malicious nodes exploit the received data in a variety of ways. In this paper, we propose a Detection and Prevention System (DPS) to detect and block malicious nodes in MANETs. For this purpose, special nodes called DPS nodes, which continuously monitor the behavior of other nodes, are deployed in a network. When a DPS node finds a node exhibiting pre-defined suspicious behavior, it declares that suspicious node as a wormhole node by broadcasting a message. All data and control messages from a node that has been declared as wormhole node are discarded by the network. NS-2 simulations show that the proposed DPS considerably reduces the number of packets dropped by malicious nodes with very low false positive rate.

An EDRI-based approach for detecting and eliminating cooperative black hole nodes in MANET

Mobile Ad hoc Network (MANET) is a self-configurable, self-maintenance network with wireless, mobile nodes. Special features of MANET like dynamic topology, hop-by-hop communications and open network boundary, made security highly challengeable in this network. From security aspect, routing protocols are highly vulnerable against a wide range of attacks like black hole. In black hole attack malicious node injects fault routing information to the network and leads all data packets toward it-self. In this paper, we proposed an approach to detect and eliminate cooperative malicious nodes in MANET with AODV routing protocol. A data control packet is used in order to check the nodes in selected path; also, by using an Extended Data Routing Information table, all malicious nodes in selected path are detected, then, eliminated from network. For evaluation, our approach and a previous work have been implemented using Opnet 14 in different scenarios. Referring to simulation results, the proposed approach decreases packet overhead and delay of security mechanism with no false positive detection. In addition, network throughput is improved by using the proposed approach.

AN OCTOPUS-INSPIRED INTRUSION DETERRENCE MODEL IN DISTRIBUTED COMPUTING SYSTEM

The study formulated and evaluated a model for effective management of maliciousnodes in mobile Ad-hoc network based on Ad-Hoc on- demand distancevector routing protocol. A collaborative injection model called CollaborativeInjection Deterrence Model (CIDM) was formulated using stochastic theory.The definition of the model was presented using graph theory. CIDM wassimulated using three different scenarios. The three scenarios were then comparedusing packets delivery ratio (PDR), routing load, throughput and delayas performance metrics. The simulation result showed that CIDM reduce considerablythe rate of packets dropped caused by malicious nodes in MANETnetwork. CIDM did not introduce additional load to the network and yet withproduce higher throughput. Lastly, the access delay with CIDM is minimalcompared with convectional OADV. The study developed a model to mete outa punitive measure to rogue nodes as a form of intrusion deterrence withoutdegrading the overall performance of the network. The well known CRAWDADdataset was used in the simulation.

Defending Against Collaborative Attacks by Malicious Nodes in MANETs: A Cooperative Bait Detection Approach

In mobile ad hoc networks (MANETs), a primary requirement for the establishment of communication among nodes is that nodes should cooperate with each other. In the presence of malevolent nodes, this requirement may lead to serious security concerns; for instance, such nodes may disrupt the routing process. In this context, preventing or detecting malicious nodes launching grayhole or collaborative blackhole attacks is a challenge. This paper attempts to resolve this issue by designing a dynamic source routing (DSR)-based routing mechanism, which is referred to as the cooperative bait detection scheme (CBDS), that integrates the advantages of both proactive and reactive defense architectures. Our CBDS method implements a reverse tracing technique to help in achieving the stated goal. Simulation results are provided, showing that in the presence of malicious-node attacks, the CBDS outperforms the DSR, 2ACK, and best-effort fault-tolerant routing (BFTR) protocols (chosen as benchmarks) in terms of packet delivery ratio and routing overhead (chosen as performance metrics).

Gray and Black Hole Attack Identification Using Control Packets in MANETs

A Mobile Ad hoc Network (MANET) is a group of mobile nodes which cooperate in forwarding packets in a multi-hop fashion without any centralized administration. One of its key challenges is finding the malicious node in MANETs. In the literature many techniques have been proposed by researchers. In this paper we have proposed a scheme in which we are sending a control sequence to the neighbour nodes and we are expecting the nodes response. Based on the node response we can identify the malicious node.