A Reputation-Based Clustering Mechanism for MANET Routing Security

Mobile Ad Hoc Networks (MANETs) are infrastructure-less networks that are mainly used for establishing communication during the situation where wired network fails. Security related information collection is a fundamental part of the identification of attacks in Mobile Ad Hoc Networks (MANETs). A node should find accessible routes to remaining nodes for information assortment and gather security related information during route discovery for choosing secured routes. During data communication, malicious nodes enter the network and cause disturbances during data transmission and reduce the performance of the system. In this manuscript, a Time Interval Based Blockchain Model (TIBBM) for security related information assortment that identifies malicious nodes in the MANET is proposed. The proposed model builds the Blockchain information structure which is utilized to distinguish malicious nodes at specified time intervals. To perform a malicious node identification process, a Network Block Monitoring Node (NBMN) is selected after route selection and this node will monitor the blocks created by the nodes in the routing table. At long last, NBMN node understands the location of malicious nodes by utilizing the Blocks created. The proposed model is compared with the traditional malicious node identification model and the results show that the proposed model exhibits better performance in malicious node detection.

Several research effort in recent years has shown its significant interest in the development of security measure in the routing of Mobile Ad-hoc Network (MANET). Ad-hoc On-demand Distance Vector (AODV) routing protocol has been one of the basic protocol that is modified to cope with security demands. This paper present an analytical model to evaluate the performance of AODV variant that is developed to be secure with malicious node detection and trust mechanism. Since the performance of MANET is tightly depends on the underlying topology of the nodes, route establishment may fail due to node's movement or malicious activity. The analytical model aims to provide the basic performance insight in the form of the route establishment probability of the MANET with and without malicious nodes. The model is important for MANET designer to optimize the connectivity for all nodes.

A Review on Cooperative Bait Based Intrusion Detection in MANET

We propose a Calculi in process algebraic framework to formally model Intrusion Detection System (IDS) for secure routing in Mobile Ad-hoc Networks. The proposed calculi, named as dRi, is basically an extension of Distributed pi calculus (Dpi). The calculi models unicast, multicast & broadcast communication, node mobility, energy conservation at node and detection of malicious node(s) in Mobile Ad-hoc Networks. The Calculi has two syntactic categories: one for describing nodes and another for processes which reside in nodes. We also present two views of semantic reductions; one as reduction on configurations whereas another as LTSs (Labelled Transition Systems), behavioural semantics, where reduction on configurations are described on various actions. We present an example described using LTSs to show the capability of the proposed calculi. We define a bisimulation based equivalence between configurations. Further we define a touch-stone equivalence on its reduction semantics & also present prove outline for bisimulation based equivalence that can be recovered from its touch-stone equivalence and vice-versa.

Mobile ad-hoc networks (MANETs) are decentralized and self-organizing communication systems. They have become pervasive in the current technological framework. MANETs have become a vital solution to the services that need flexible establishments, dynamic and wireless connections such as military operations, healthcare systems, vehicular networks, mobile conferences, etc. Hence it is more important to estimate the trustworthiness of moving devices. In this research, we have proposed a model to improve a trusted routing in mobile ad-hoc networks by identifying malicious nodes. The proposed system uses Reinforcement Learning (RL) agent that learns to detect malicious nodes. The work focuses on a MANET with Ad-hoc On-demand Distance Vector (AODV) Protocol. Most of the systems were developed with the assumption of a small network with limited number of neighbours. But with the introduction of reinforcement learning concepts this work tries to minimize those limitations. The main objective of the research is to introduce a new model which has the capability to detect malicious nodes that decrease the performance of a MANET significantly. The malicious behaviour is simulated with black holes that move randomly across the network. After identifying the technology stack and concepts of RL, system design was designed and the implementation was carried out. Then tests were performed and defects and further improvements were identified. The research deliverables concluded that the proposed model arranges for highly accurate and reliable trust improvement by detecting malicious nodes in a dynamic MANET environment.

Most existing trust-based security schemes for mobile ad-hoc networks (MANETs) consider packet loss an indicator of possible attacks by malicious nodes. There may be several reasons for packet losses, such as interference, queue overflow, and node mobility. Identifying the real underlying cause of a packet loss event is important for any security solution. To detect truly malicious nodes, it is necessary to carry out a fine-grained analysis (FGA) to determine the underlying cause of such loss. Without such analysis, the performance of any security solution may degrade, due to the punishment of innocent nodes while actual malicious nodes may remain undetected. Therefore, approaches are required that can correctly identify the reason for packet losses and can react accordingly. In this paper, we present a scheme that is able to correctly identify malicious nodes, using network parameters to determine whether packet losses are due to queue overflows or node mobility in MANETs. The contributions of this paper include the FGA scheme for packet loss and the development of a comprehensive trust model for malicious node identification and isolation. Our proposed FGA scheme is evaluated in terms of effectiveness and performance metrics under different network parameters and configurations. The experimental results show that our proposed trust model achieves a significant reduction in false positives rate and an increase in the rate of detection of truly malicious nodes compared with traditional non-FGA schemes.

Since last decades, mobile ad hoc network has emerged drastically as a new generation networking technology. Nowadays, computers are used as necessary gadgets and its derived gadgets such as PDA, notebook, mobile, tablet. Their utility increases, when they are connected to the data networking medium. Wireless network is one of the popular network media that connect the mobile devices. Mobile ad hoc network (MANET) is widely accepted wireless network, as it proves the best fit for various applications such as military operations, natural disasters, and remote areas, and at the same time, MANET is more vulnerable to various attacks on the different networking layers. The working mechanism of MANET is based on the mobile nodes. All the nodes are assumed as trusted nodes, but some malicious nodes get affected in MANET working approach. This paper presents a review study of different types of vulnerable attacks on different network layers of MANET. MANET attacks are broadly classified according to the TCP/IP layer architecture. This study gives us the scope of developing secure IDPS for the MANET.

The 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.

ABSTRACTQuality of service (QoS) provisioning in mobile ad hoc networks (MANETs) is one of the fundamental concerns in this area. Autonomous mobility of nodes is one of the main factors affecting the network QoS. A lack of fixed infrastructure and central control on the nodes complicate guaranteeing the QoS in MANETs. Security is a major factor affecting the QoS in such networks. This paper proposes a new routing method based on trust among the nodes and applies a clustering approach to MANETs. This routing scheme can calculate the trustworthiness of the route and the shortest routes to the destination by avoiding identified malicious nodes. The effects of node mobility and number of malicious nodes on routing performances are investigated in the reported research. Simulation is used to show the effectiveness of the proposed approach in comparison with a categorized trust method, Ad hoc On‐demand Distance Vector, trust vector‐based Ad hoc On‐demand Distance Vector, and trust vector‐based dynamic source routing in terms of end‐to‐end delay, network throughput, and packet delivery ratio at the expense of a moderate increase in the routing overhead. The proposed trust‐based routing method finds the trustworthy route by applying end‐to‐end trust calculation and finds the optimal route by performing clustering scheme from the source node to the destination node. In the extended proposed routing method, adding a nonce‐based encryption scheme results in improved packet delivery ratio, network throughput, and malicious node detection rate. Copyright © 2013 John Wiley & Sons, Ltd.

Mobile Ad-hoc networks (MANETs) are self-organizing multi-hop wireless networks composed of independent mobile nodes. Due to the characteristics of dynamic topology and limited resources, MANETs are vulnerable to attacks from malicious nodes. However, traditional security policies with encryption and authentication cannot be well suited for MANETs, because of absence of trusted third party. In order to decrease hazards from malicious nodes, the concept of trust has been introduced to MANETs. In this paper, we first propose a novel trust model, in which nodes calculate trust value according to multiple events including historical interactions, interactions context factors and recommendations from neighbor nodes. Then the model is introduced into AOMDV, forming a novel trusted multipath routing protocol, termed as Trust-based Ad Hoc On-demand Multipath Distance Vector protocol (TAOMDV). The TAOMDV provides a flexible way to select the shortest path or multiple shortest paths from all trust satisfied paths to balance load. Experiments show that TAOMDV improves significantly in network loss ratio and average end-to-end latency.

Mobile Ad-hoc Networks (MANETs) features such as open medium, dynamic topology, lack of centralised management and lack of infrastructure expose them to a number of security attacks. Black hole attack is one type of attack that is more common in MANET reactive routing protocols such as Ad-hoc On-demand Distance Vector (AODV) and Dynamic Source Routing (DSR). Black hole attack takes advantage of route discovery process in reactive routing protocols. In this type of attack, a malicious node misleads other nodes in the network by pretending to have the shortest and updated route to a target node whose packets it wants to interrupt. It then redirects all packets destined to a target node to itself and discards them instead of forwarding. This paper analyses the performance of AODV and DSR when attacked by black hole, by varying the mobility of the nodes in the network. The analysis is carried out by simulating scenarios of AODV based MANET and DSR based MANET using Network Simulator 2 (NS-2) and introducing the black hole attack in each of the scenarios. The different scenarios are generated by changing the mobility of the nodes. The performance metrics that are used to do the analysis are throughput, packet delivery ratio and end-to-end delay. The simulation results show that the performance of both AODV and DSR degrades in the presence of black hole attack. Throughput and packet delivery ratio decrease when the network is attacked by black hole because the malicious node absorbs or discards some of the packets. End-to-end delay is also reduced in the presence of a black hole attack because a malicious node pretends to have a valid route to destination without checking the routing table, and therefore shortens the route discovery process. The results also show that throughput decreases slightly when mobility of the nodes is increased in the network. The increase in the speed of the nodes decreases both end-to-end delay and packet delivery ratio.

VANET a thrilling function via mobile ad-hoc network (MANETs). VANET is a dominant invention which can supply practical vehicle to roadside infrastructure (V2I) communique and vehicle to vehicle (V2V). VANETs are a natural configure scheme wherein node are automobile and WIFI technology are used to shape networks. VANETs is a accredited construct intelligent transportation system (ITS) which emphasis on road protection, visitor well being and traffic effectiveness. In existing work, the supply node use extra data well identified as pseudo reply packet (PRREP). Supply node saves all details regarding entire arriving package in look-up chart chosen as RREP_T. The chart save series of PRREP set in ascending way via using POP and PUSHES processes. If there is any kind of deviation in chart order will be measured to PRREP series got via malicious node and will be rejected via source. Shortest series digits gives peak priority and it is measured via series digits. Nodes that have irregular series digits are measured as malicious node and supply transmit message via network. But depending on sequence number is not useful concept for the detection of malicious node in the network. In the proposed work, we overcome this problem via usage of genetic algorithm to recover performance in network. If the node has less fitness value than the threshold value than it is considered as a misbehaviour node. By this technique we can improve the performance of network by removing malicious nodes. We used NS2 in the simulation to provide the implementation work to provide the efficiency in the network.

Irrespective of whether the environment is wired or wireless, routing is an important challenge in networks. Since mobile ad hoc networks (MANETs) are flexible and decentralized wireless networks, routing is very difficult. Furthermore, malicious nodes existing in the MANET can damage the routing performance of the network. Recently, reinforcement learning has been proposed to address these problems. Being a reinforcement learning algorithm, the Q-learning mechanism is suitable for an opportunistic routing approach because it not only adapts to changing networks, but also mitigates the effect of malicious nodes on packet transmission. In this study, we propose a new reinforcement learning routing protocol for MANETs called reputation opportunistic routing based on Q-learning (RORQ). Using this protocol, which works based on game theory, a reputation system can detect and exclude malicious nodes in a network for efficient routing. Thus, our method can find a routing path more effectively in an environment attacked by malicious nodes. The simulation results showed that the proposed method could achieve superior routing performance compared with other state-of-the-art routing protocols. In addition, compared to other algorithms, the proposed method demonstrated gains of up to 55% in terms of packet loss, up to 82% in terms of average end-to-end delay, and up to 28% in terms of energy efficiency in the blackhole attack scenario and up to 73% in terms of packet loss, up to 35% in terms of average end-to-end delay, and up to 12% in terms of energy efficiency in the gray hole attack scenario.

Wormhole attack is a severe attack in mobile ad hoc networks, which is particularly challenging to defend against. In this paper, a new method defined as time ruler using distance measurement for detecting wormhole attack is proposed including its hypothetical model, the concept of time ruler and the process of its establishment, storage and calibration. The feasibility of the method on theory, thus respectively deriving two discriminating inequalities with the use of sending time ruler and receiving time ruler are analyzed in detail. The performances of the method are also analyzed and its validity is evaluated with Omnet++ developing tools. Mobile Ad Hoc networks (MANETs) are self-organized, multi-hop wireless networks which are independent of fixed infrastructure, with the advantages of easy networking and not being limited with time and space. Unlike the conventional network, MANETs are characterized by numerous constraints such as lack of infrastructure, lack of resources on nodes, dynamic topology, no centralized management and control, and lack of pre-established trust relationships between nodes. Due to these, MANETs are very likely to often be run in untrusted environments and make themselves vulnerable to various security attacks, such as eavesdropping, tamper, replay, and denial-of-service. Wormhole attack is also called Tunnel attack. In MANETs, a malicious node records a packet, at one location in the network, tunnels the packet to another location, and replays it there. If the tunneled distance is longer than the normal wireless transmission range of a single hop, it takes less time or less hops to make the tunneled packet arrive sooner than other packets transmitted over a normal multi- hop route. In this case, it creates the illusion that two remote regions of a MANET are directly connected through nodes that appear to be neighbors. Since it costs less hops to travel through tunnel than by a normal route, for shortest path routing protocol, the malicious node increases its attraction to network flow, and thereby providing advantageous conditions for itself to launch further attack such as tamper or packet-loss.

Mobile Ad-hoc Networks (MANETs) allow mobile hosts to initiate communications with each other over a network without an established infrastructure or a central network authority. Because of this, MANETs have dynamic topologies because nodes can easily join or leave the network at any time. From a security design perspective, MANETs are vulnerable to various types of malicious attacks. As are result, Ad-hoc On-demand Distance Vector (AODV), which is one of the standard MANET protocols, can be attacked by malicious nodes. A black hole attack is one type of malicious attack that can be easily employed against data routing in MANETs. A black hole node replies to route requests rapidly with the shortest path and the highest destination sequence number. The black hole node does not have an active route to a specified destination associated with it and it drops all of the data packets that it receives. This paper proposes a mechanism that provides Secure Route Discovery for the AODV protocol (SRD-AODV) in order to prevent black hole attacks. This mechanism requires the source node and the destination node to verify the sequence numbers in the Route Request (RREQ) and Route Reply (RREP) messages, respectively, based on defined thresholds before establishing a connection with a destination node for sending the data. The simulation results using the Network Simulator 2 (NS2) demonstrate an improvement in the ratio of packet delivery for three different environments using our mechanism as compared to the standard AODV protocol.