Black Hole Attack In Mobile Ad Hoc Network Research Articles

Proposed Naïve Bayes- Genetic algorithm to detect black hole attacks in MANETs.

The threats and attacks on mobile ad hoc networks (MANETs) are significant, making it difficult for traditional security systems to provide complete protection. Therefore, an efficient hybrid clustering approach must be designed to construct an intrusion detection system (IDS) that is suitable for this network. IDSs are crucial in MANETs due to the presence of black hole threats, which are the most significant vulnerabilities in this network.Our suggestion is to use a hybrid classifier to detect black hole attacks in MANETs. This can be achieved by using the Naïve Bayes algorithm for clustering to select the cluster head, and modify the Genetic Algorithm to identify the node responsible for a black hole attack on the optimal path. Finally, the confidence server instructs the destination node. If permitted, it alerts the collection head; otherwise, it identifies the node as a malicious one in the black hole attack within each cluster. The results of our proposed technique show that it has improved package damage rate, quantity, package distribution ratio, whole network interruption, and standardized directing capacity parameters compared to current black hole detection approaches.
 The simulation was done in KDD cup 99 to carry out black hole attack and trace file obtained is used as dataset for training and testing purpose using visual basic.

AODV-based Defense Mechanism for Mitigating Blackhole Attacks in MANET

Mobile Ad hoc Networks (MANETs) are decentralized and self-configuring networks composed of mobile devices that communicate without a fixed infrastructure. However, the open nature of MANETs makes them vulnerable to various security threats, including blackhole attacks, where malicious nodes attract and discard network traffic without forwarding it to its intended destination. Mitigating blackhole attacks is crucial to ensure the reliability and security of communication in MANETs. This paper focuses on the development and evaluation of AODV (Ad hoc On-Demand Distance Vector)-based defence mechanisms for effectively mitigating blackhole attacks in MANETs, while simultaneously addressing energy efficiency and environmental sustainability. AODV is a widely used routing protocol in MANETs due to its on-demand nature and low overhead. However, it lacks built-in security mechanisms, making it susceptible to attacks. We incorporate energyaware route selection, solar-powered routing, collaborative energy sharing, energy-efficient intrusion detection, green routing optimization, and energy harvesting from environmental sources. By considering energy consumption and environmental factors in the route selection process, our defense mechanism not only enhances the security of the network but also contributes to energy conservation and reduced environmental impact. To evaluate the effectiveness of the proposed defence mechanisms, extensive simulations and performance analyses are conducted using network simulation tools. Through simulation-based evaluations, we demonstrate the effectiveness of our approach in achieving robust blackhole attack mitigation while extending the network’s lifetime and minimizing its carbon footprint. Our research offers valuable insights into the development of energy-efficient and environmentally sustainable solutions for securing MANETs in the face of evolving security threats.

Machine Learning and Trust Based AODV Routing Protocol to Mitigate Flooding and Blackhole Attacks in MANET

The latest advancements in wireless communication promotes the researchers to concentrate more in the expansion of Mobile Ad hoc Networks (MANETs), where nodes communicate each other to provide the demanded real time infotainment services. However, due to wireless connectivity and decentralized architecture, constructing secure routing is still a challenging issue in MANETs. Therefore, in this paper, A Machine Learning and Trust Based AODV Routing Protocol (ML-AODV) to mitigate Flooding and Blackhole Attacks in MANET is proposed. Primarily, cooperative intermediate nodes in the network are selected through trust estimation at each node to avoid (flooding attack) unnecessary transmission of huge number of routing packets to nonexistent destinations. Towards this, the node distinct metrics like Hop Count (HC), Residual Energy (RE), and Link Expiration Time (LET) are considered for trust estimation. The nodes with highest trust value are chosen as trusted relay forwarders to find the optimal paths with reduced packet drop rate (Blackhole attack). Further, finds best optimal path to eliminate the energy disparity and delay for packet transmission using machine learning based Artificial Neural Network (ANN) with Support Vector Machine (SVM) classifier. Here, SVM is used to identify the intruder in the selected route to improve efficiency and intrusion detection accuracy. The performance of the proposed ML-AODV is carried out using NS-2 and compared over existing routing schemes under attack. Based on the simulation results, the proposed ML based AODV exhibits improved throughput to 4% and reliability to 44% over existing approaches. In addition, delay, routing overhead, packet loss ratio are reduced to 12%, 15%, and 10% respectively.

Dipper Throated Optimization for Detecting Black-Hole Attacks in MANETs

In terms of security and privacy, mobile ad-hoc network (MANET) continues to be in demand for additional debate and development. As more MANET applications become data-oriented, implementing a secure and reliable data transfer protocol becomes a major concern in the architecture. However, MANET’s lack of infrastructure, unpredictable topology, and restricted resources, as well as the lack of a previously permitted trust relationship among connected nodes, contribute to the attack detection burden. A novel detection approach is presented in this paper to classify passive and active black-hole attacks. The proposed approach is based on the dipper throated optimization (DTO) algorithm, which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’ quality of service. A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron (DTO-MLP), and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical (LEACH) clustering technique. MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights of minor features. This hybrid method is primarily designed to combat active black-hole assaults. Using the LEACH clustering phase, however, can also detect passive black-hole attacks. The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach. For diverse mobility situations, the results demonstrate up to 97% detection accuracy and faster execution time. Furthermore, the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

Prevention of Blackhole Attack in MANET using Certificate less Signature Scheme

Mobile AdHoc Networks (MANETs) are the network of self-configuring nodes. Such nodes communicate through single as well as multi-hop modes without the aid of any centralized administrator or pre-existing network infrastructure. Due to this reason, MANETs have gained a highly significance in modern wireless networking technologies. Such networks are extremely vulnerable to one of the security attack i.e. blackhole attack. It is a malicious node when an attacker is able to send a fake route reply to the originator of a route request packet. Such attackers discard the legitimate packets and replay packets in the whole network thereby adversely affecting network performance. Most of the security protocols for MANET are using bilinear pairing methods to provide security against security attacks and it takes high computing cost for the computation of pairing operations. Nowadays, researchers are using certificate-less signature schemes in distributed environments to provide efficient security. This signature scheme is very popular because it does not use any certificate authority for the management of security certificates. In this paper, we proposed an efficient technique to prevent blackhole attack in MANET using RSA-based certificateless signature scheme without using any bilinear pairing operations. Our scheme provides security against forgery and blackhole attacks and is evaluated under a discrete logarithm problem. Proposed scheme outperforms existing schemes in terms of these metrics viz., throughput, packet delivery ratio, routing overhead and end-to-end delay when we are varying mobility and fixed percentage of malicious nodes. Our proposed scheme not only detects or prevents the blackhole attack but it is also capable to provide important security services viz., integrity, authentication and non-repudiation.

Countering of Black Hole Attack on Manet with AODV Protocol

Abstract: Security in mobile ad-hoc network (MANET) is the most serious issue impacting performance of network. In general, routing methods is one of the complicated and exciting analysis places. In black hole attack, a harmful node uses its routing technique to be able to promote itself for having the quickest direction to the place node or to the bundle it wants to identify. In this research, performance of one of the most efficient solutions for preventing single black hole attack in MANET using AODV routing protocol will be investigated in terms of packet delivery ratio, packet loss percentage, average end-to-end delay, and route request overhead. This chapter describes the introduction, background of the study, research objectives and questions, the scope of the study and its primary objectives.

Mitigation of black hole attack in MANETs: A sequence no-based approach

MANET (Mobile Ad-hoc network) is type of network which is comprised of moveableness vertices which are connected to form a wireless network. The connected vertices can move very freely as there is no fixed infrastructure for the network. Black hole attack is a DOS attack where one of the vertices in the network is mischievous which tries to capture all the datagrams passing through it by sending a false reply to the origin vertex. In this paper an improvement is made to the existing AODV protocol to mitigate the black hole attack in Manets. The proposed method finds the mischievous vertex and avoids the delivery route through the mischievous vertex. The simulations were experimented in ns2 and the observations prove that the implemented approach works well for various performance metrices like Packet delivery ratio, End to End delay and throughput.

DETECTING AND ISOLATING BLACK-HOLE ATTACKS IN MANET USING COUNTER BASED TROLLING TECHNIQUE

Mobile Ad hoc Network (MANET) is a part of wireless networks that gives different applications in various fields. MANET's security had gotten perhaps the greatest issue in fields of networks. MANET is powerless against various kinds of attacks that influence its usefulness and availability. The black hole attack is viewed as one of the most perilous dynamic attacks which impedes the presentation and dependability of the network because of the dropping of all approaching data packets by the malicious node. The black hole attack intends to deceive each node in the network that needs to speak with another node by guaranteeing that it generally has the best path to the objective node. AODV is a responsive routing protocol which has no method to identify and forestall black hole attack in to the network. In this examination work, we improved the AODV routing protocol utilizing another lightweight method that utilizes hop count and trolling to recognize and detect single and multiple black hole attack. In this research work we provide the security scheme against single and cooperative black hole attack in MANET. The blackhole attack is packet dropping attack behaves like normal node at the time of connection establishment and after forward false reply of destination to sender drops all the data packets. In this attack one or more than one malicious nodes create a secure environment with the presence of other normal nodes. The proposed IDS (Intrusion Detection System) is identified the nodes those are not forwarded the data packets continuously abut node exist in network and provides the secure communication in dynamic network.

Trusted Sensing Model for Mobile Ad HoC Network Using Differential Evolution Algorithm

Mobile Ad Hoc Network (MANET) has set of mobile nodes that are allowed to communicate with each other through wireless links. The nodes are deployed spontaneously without any infrastructure in a geographical area. Due to the lack of centralized administration and prior organization, MANETs are vulnerable to different attacks of malicious nodes. To overcome the problem of black hole attack in MANETs, a trust model using Differential Evolution (DE) algorithm has been proposed. It identifies the malicious node and inhibits them to become the member of data transmission path. The proposed work consists of two phases; one is to obtain the optimized path and the other deals with the penalty factor for malicious nodes. Moreover, the Differential Evolution is one of the most promising optimization to enhance security with increased network density. The proposed algorithm is compared with AOMDV, DSR, Genetic algorithm and ACO.

Reliability Integrated Intrusion Detection System for Isolating Black Hole Attack in MANET

Mobile ad hoc network (MANET) is a temporary network which can be utilized for emergency applications. It is easy to deploy the attackers in the network. The network performance may get degraded due to the presence of attackers. Black hole attack is the major attack which will totally violate the network rules and degrade the routing process. In this research, the Reliability Integrated Intrusion Detection System (RIIDS) is used for isolating the black hole attacks. It contains three phases. In first phase, the node forwarding ration is estimated to provide node reliability. In second phase, route reliability metric is evaluated to obtain the effective routes which can withstand the attackers. In third phase, objective function with effective routing strategy is adopted to detect attackers and isolate them by discovering alternate routes. The simulation results are analyzed using AODV protocol in terms of various performance metrics i.e. attacker detection ratio, queuing delay, packet delivery ratio and confidentiality.

Node Disjoint Multiple Paths Routing Technique for Secure, Reliable and Confidential Data Transmission against Black Hole Attacks in MANET

The Ad-hoc On-Demand Multipath Distance Vector (AOMDV) is an extension of AODV protocol in the mobile ad-hoc network (MANET). It utilizes the features of DSDV, DSR, and AODV Routing protocols for Secure, Reliable and confidential data transmission against the attacks in the MANET, but in black hole attack malicious nodes utilize the behavior of AODV and show its malicious behavior at the data transmission, it causes the loss of data packets. So to avoid this loss more researchers have been conducted several techniques for detection and put several efforts to prevent the Blackhole attacks. But still, it is not prevented completely, so this paper proposes a Node Disjoint Multiple paths routing algorithm technique on AOMDV against blackhole attacks in MANET and shows its better performance in Network Simulator (NS2).

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.

Random ID based Secure AODV to prevent Black Hole Attack in MANET

Mobile Ad Hoc Networks (MANET) are typically wireless networks that do not have any fixed network architecture. This makes the task of routing layers difficult. A popular reactive routing protocol – Ad hoc On-demand Distance Vector (AODV) used in MANETs has certain vulnerabilities which make it susceptible to black hole attacks. This paper proposes an innovative technique to detect and prevent the possibility of a black hole attack in AODV based MANETs. Using the approach of detecting multiple Route Reply (RREP) messages, authentication of nodes is done by the use of a randomly generated id unique to each route discovery process. This method was tested using simulations in NS2 software and compared with previous attempts. The proposed method showed marked improvement in performance in the presence of malicious nodes.

Cross centric intrusion detection system for secure routing over black hole attacks in MANETs

Due to the constraint of resources in Mobile Adhoc Network (MANET), usually mobile nodes do not gain perfect routing efficiency. This routing efficiency will be reduced by several numbers of data blockages and the communication time in the routing path. There are many data blockages arising, especially Black Hole Attack (BHA) is a massive anomalous attack in routing. This may cause the efficiency of communication time between nodes. In this juncture, the availability of Intrusion Detection System with Cross centric network which is overloaded with routing path packets, so it is easily affected by the BHA. To decrease the impact of black hole attack using Cross Centric Network Intrusion Detection System, this research proposes the CniDsor framework. CniDsor framework is a diminutive form of the “Cross Centric Intrusion Detection System for Secure Routing over Black Hole Attacks in MANETs”. This system is used to prevent the Black Hole Attacks over the Secure Routing. CniDsor works based on the Path Origin Selection, Priority Portion Assignment (PPA) and IDS attack reduction. In this study, path origin selection utilizes secure routing by “PIHNSPRA Routing Algorithm” and it selects the path from end to end nodes with secure manner and reduces the black-hole attack. Meanwhile, priority portion assignment is used to manage the node position and network monitoring by the “Past Interaction History”. Finally, CniDsor framework provides secure IDS communication efficiency in MANET which is used to accomplish the efficient routing path. Moreover, the proposed CniDsor framework works better and minimizes the communication overhead, packet delay and end to end delay, thus increases the network lifetime, packet delivery ratio (PDR).

Survey: Black Hole Attack Detection in MANET

Mobile Ad-hoc network (MANET) is dynamic in nature consists infrastructure less topology and de centralized behavior of nodes. Due to dynamic in nature each node is free to move anywhere so network become more vulnerable to attacks like black hole, gray hole any many more types of attacks. Infrastructure less mean each node some time act like host and sometimes like router. De centralized means it does not fallow client server model of communication. AODV is a routing protocol for data and control packet far wording between nodes during the communication between nodes black hole attacks are major issue in MANETs. In this paper discus some important technique used to detect the black hole attacks in MANETs using AODV routing protocol and their merits and demerits which play major role for future research.

Mitigate black hole attack using bee optimised weighted trust with AODV in MANET

Mobile ad hoc network (MANET) is a decentralised network that can be used without any fixed setup or infrastructure and offers unlimited opportunities in all fields. Both the advantage and disadvantage of this mediums is the wide transmission range of the network that exceeds the area where the network is deployed, giving great opportunities for intruders to hack the network thus making it an unsafe mode of transmission. The conventional approaches for security attacks require high memory and high power consumption and so they cannot be used to tackle the security attacks for ad hoc routing. In this work, trust is used for addressing the maliciousness in the network. In this paper, the proposition is to reduce black hole attack in MANET using artificial bee colony optimisation for finding optimal secure routes. Proposed method outperforms the performance metrics like packet delivery ratio (PDR) and number of hops to sink. Also performs as normal effect for end-to-end delay.

Detecting and Isolating Black-Hole Attacks in MANET Using Timer Based Baited Technique

Mobile Ad hoc Network (MANET) is a type of wireless networks that provides numerous applications in different areas. Security of MANET had become one of the hottest topics in networks fields. MANET is vulnerable to different types of attacks that affect its functionality and connectivity. The black-hole attack is considered one of the most widespread active attacks that degrade the performance and reliability of the network as a result of dropping all incoming packets by the malicious node. Black-hole node aims to fool every node in the network that wants to communicate with another node by pretending that it always has the best path to the destination node. AODV is a reactive routing protocol that has no techniques to detect and neutralize the black-hole node in the network. In this research, we enhanced AODV by integrating a new lightweight technique that uses timers and baiting in order to detect and isolate single and cooperative black-hole attacks. During the dynamic topology changing the suggested technique enables the MANET nodes to detect and isolate the black-hole nodes in the network. The implementation of the proposed technique is performed by using NS-2.35 simulation tools. The results of the suggested technique in terms of Throughput, End-to-End Delay, and Packet Delivery Ratio are very close to the native AODV without black holes.

An Efficient multi stage security model based on cross layer mechanism for identifying and preventing black hole attack in wireless ad-hoc networks

Mobile Adhoc Network (MANET) is one of the popular and highly demanded networks functioning under wireless adhoc network. It has high demand by various emerging applications in the entire world. MANET and its necessities are increasing day by day. MANET and its various components like node, route and etc., are too dynamic regarding creating, deployment, mobility, speed, join and disconnect from the network. MANET does not require any existing infrastructure whereas it starts functions in any place at any time. Due these reasons various malicious activities are created dynamically and it degrades the performance of the network. This paper focused on detecting and identifying malicious activities one who affects the data directly. Such kind of malicious activity is blackhole attack where it destroys the data during transmission in a route. In order to detect and eliminated blackhole attack this paper focused on monitoring and investigating various functionalities of the network using a Multi Stage Security Model (MSSM). The MSSM is used for detecting and preventing blackhole attacks in MANET which involves investigating cross layer functionalities during data transmission. And it also investigates and analyzes the entire information that is available in the network. This proposed MSSM is simulated in Network Simulation-2 software and the results are verified and the performance is evaluated.

Energy aware detection and prevention of black hole attack in MANET

Energy and security are the two vital components of Mobile Ad-hoc Networks (MANETs). During routing finding an optimal path from sender to receiver sense of path length (number of hops), longevity (battery life) and security becomes an essential requirement. Variety of schemes are proposed by the researchers for finding the shortest path along with energy saving and protecting from attacks Black-hole and gray-hole attacks are some of the most harmful attacks against MANET communication and needs attention. These attacks may cause by insider or outsider malicious node(s) who may drop packets or misroute the information during communication from sender node to receiver node. In our study we proposed an intelligent routing protocol based on Ant Colony Optimization (ACO) technique that finds shortest path from source to destination, applies the concept of power aware techniques to save energy increasing the longevity of the link avoiding link failure and also uses the concept of digital signatures, watchdog and path rater for detection and avoidance ofblack-hole and gray-hole attacks. Simulation study of the proposed scheme is made over some network parameters and found to be efficient in comparison to the basic AODV routing protocol.

Black hole detection using evolutionary algorithm for IDS/IPS in MANETs

Mobile ad hoc network (MANET) is a self organized structure of mobile nodes which are distributed unevenly in the environment. There are various MANET routing protocols exist which cannot stand against the security issues which might occur inside or outside of the MANET environment. Black hole attack is most popular insider attack happening on the MANET environment which needs to be concerned more to avoid the unwanted packet loss. These problems are resolved in the proposed research method by introducing new method namely hybridization of particle swarm optimization with genetic algorithm (HPSO-GA) routing system. The main contribution of this research method is to detect the black hole attack (BHA) by utilizing ad hoc on demand distance vector (AODV) routing protocol. This method utilizes the data routing information (DRI) of the neighbouring nodes instead of considering source node information. DRI information are gathered from every neighbouring nodes which increases the probability of attack detection accuracy. The suggested HPSO-GA routing mechanism researches the black hole attack in MANET, at that point, the outputs were measured utilizing the parameters namely detection rate, false positive alarm (FPA), packet delivery ratio (PDR), routing overhead, average end to end delay and throughput. The outcomes are contrasted with existing techniques, like, AODV, local intrusion detection (LID) with AODV. Simulation output utilizing the network simulator demonstrates that the improvement ratio of the throughput picked up by HPSO-GA security routing mechanism and general improvement reduction at end-to-end delay and routing overhead.