Biblio

Vehicular Ad hoc Networks commonly abbreviated as VANETs, are an important component of MANET. VANET refers to the group of vehicles that are interlinked to one another through wireless network. Along with technology, comes the threats. Like other wireless networks, VANETs also are vulnerable to various security threats. Security in VANETs is a major issue that attracted many researchers and academicians. One small security breach can cause a big damage in case of VANETs as in this case human lives are involved. Intrusion Detection Systems (IDS) are employed in VANETs in order to detect and identify any malicious activity in the network. The IDS works by analysing the network and detecting any intrusions tried or made in the network so that proper steps could be taken timely to prevent damage from such activities. This paper reviews Intrusion Detection systems, classification of IDS based on various factors and then the architecture of IDS. We then reviewed some of the recent and important intrusion detection research works and then compared them with one another.

This advanced research survey aims to perform intrusion detection and routing in ad hoc networks in wireless MANET networks using machine learning techniques. The MANETs are composed of several ad-hoc nodes that are randomly or deterministically distributed for communication and acquisition and to forward the data to the gateway for enhanced communication securely. MANETs are used in many applications such as in health care for communication; in utilities such as industries to monitor equipment and detect any malfunction during regular production activity. In general, MANETs take measurements of the desired application and send this information to a gateway, whereby the user can interpret the information to achieve the desired purpose. The main importance of MANETs in intrusion detection is that they can be trained to detect intrusion and real-time attacks in the CIC-IDS 2019 dataset. MANETs routing protocols are designed to establish routes between the source and destination nodes. What these routing protocols do is that they decompose the network into more manageable pieces and provide ways of sharing information among its neighbors first and then throughout the whole network. The landscape of exciting libraries and techniques is constantly evolving, and so are the possibilities and options for experiments. Implementing the framework in python helps in reducing syntactic complexity, increases performance compared to implementations in scripting languages, and provides memory safety.

The Intrusion Detection System (IDS) is one of the technologies available to protect mobile ad hoc networks. The system monitors the network and detects intrusion from malicious nodes, aiming at passive (eavesdropping) or positive attack to disrupt the network. This paper proposes a new Intrusion detection system using three Machine Learning (ML) techniques. The ML techniques were Random Forest (RF), support vector machines (SVM), and Naïve Bayes(NB) were used to classify nodes in MANET. The data set was generated by the simulator network simulator-2 (NS-2). The routing protocol was used is Dynamic Source Routing (DSR). The type of IDS used is a Network Intrusion Detection System (NIDS). The dataset was pre-processed, then split into two subsets, 67% for training and 33% for testing employing Python Version 3.8.8. Obtaining good results for RF, SVM and NB when applied randomly selected features in the trial and error method from the dataset to improve the performance of the IDS and reduce time spent for training and testing. The system showed promising results, especially with RF, where the accuracy rate reached 100%.

A wireless technology Mobile Ad hoc Network (MANET) that connects a group of mobile devices such as phones, laptops, and tablets suffers from critical security problems, so the traditional defense mechanism Intrusion Detection System (IDS) techniques are not sufficient to safeguard and protect MANET from malicious actions performed by intruders. Due to the MANET dynamic decentralized structure, distributed architecture, and rapid growing of MANET over years, vulnerable MANET does not need to change its infrastructure rather than using intelligent and advance methods to secure them and prevent intrusions. This paper focuses essentially on machine learning methodologies and algorithms to solve the shortage of the first line defense IDS to overcome the security issues MANET experience. Threads such as black hole, routing loops, network partition, selfishness, sleep deprivation, and denial of service (DoS), may be easily classified and recognized using machine learning methodologies and algorithms. Also, machine learning methodologies and algorithms help find ways to reduce and solve mischievous and harmful attacks against intimidation and prying. The paper describes few machine learning algorithms in detail such as Neural Networks, Support vector machine (SVM) algorithm and K-nearest neighbors, and how these methodologies help MANET to resolve their security problems.

Mobile Ad hoc Networks ‘MANETs’ are still defenseless against peripheral threats due to the fact that this network has vulnerable access and also the absence of significant fact of administration. The black hole attack is a kind of some routing attack, in this type of attack the attacker node answers to the Route Requests (RREQs) thru faking and playing itself as an adjacent node of the destination node in order to get through the data packets transported from the source node. To counter this situation, we propose to deploy some nodes (exhibiting some distinctive functionality) in the network called DPS (Detection and Prevention System) nodes that uninterruptedly monitor the RREQs advertised by all other nodes in the networks. DPS nodes target to satisfy the set objectives in which it has to sense the mischievous nodes by detecting the activities of their immediate neighbor. In the case, when a node demonstrates some peculiar manners, which estimates according to the experimental data, DPS node states that particular distrustful node as black hole node by propagation of a threat message to all the remaining nodes in the network. A protocol with a clustering approach in AODV routing protocol is used to sense and avert the black hole attack in the mentioned network. Consequently, empirical evaluation shows that the black hole node is secluded and prohibited from the whole system and is not allowed any data transfer from any node thereafter.

In the recent years, mobile ad hoc wireless networks (MANETs) have experienced a tremendous rise in popularity and usage due to their flexibility and ability to provide connectivity from anywhere at any time. In general, MANETs provide mobile communication to participating nodes in situation where nodes do not need access to an existing network infrastructure. MANETs have a network topology that changes over time due to lack of infrastructure and mobility of nodes. Detection of a malicious node in MANETs is hard to achieve due to the dynamic nature of the relationships between moving node and the nature of the wireless channel. Most traditional Intrusion Detection System (IDS) are designed to operate in a centralized manner; and do not operate properly in MANET because data in MANETs is distributed in different network devices. In this paper, we present an Hierarchical Cooperative Intrusion Detection Method (HCIDM) to secure packets routing in MANETs. HCIDM is a distributed intrusion detection mechanism that uses collaboration between nodes to detect active attacks against the routing table of a mobile ad hoc network. HCIDM reduces the effectiveness of the attack by informing other nodes about the existence of a malicious node to keep the performance of the network within an acceptable level. The novelty of the mechanism lies in the way the responsibility to protect the networks is distributed among nodes, the trust level is computed and the information about the presence of a malicious is communicated to potential victim. HCIDM is coded using the Network Simulator (NS-2) in an ad hoc on demand distance vector enable MANET during a black hole attack. It is found that the HCIDM works efficiently in comparison with an existing Collaborative Clustering Intrusion Detection Mechanism (CCIDM), in terms of delivery ratio, delay and throughput.

Mobile Ad Hoc Network (MANET) is one of the types of Ad-hoc Network which is comprised of wireless in a network. The main problem in this research is the vulnerability of the protocol routing Dymo against jellyfish attack, so it needs detection from a jellyfish attack. This research implements the DELPHI method to detect jellyfish attacks on a DYMO protocol which has better performance because the Delay Per-Hop Indicator (DELPHI) gathers the amount of hop and information delay from the disjoint path and calculates the delays per-hop as an indicator of a jellyfish attack. The evaluation results indicate an increase in the end-to-end delay average, start from 112.59s in 10 nodes increased to 143.732s in 30 nodes but reduced to 84,2142s in 50 nodes. But when the DYMO routing did not experience any jellyfish attacks both the delivery ratio and throughput are decreased. The delivery ratio, where decreased from 10.09% to 8.19% in 10 nodes, decreased from 20.35% to 16.85%, and decreased from 93.5644% to 82.825% in 50 nodes. As for the throughput, for 10 nodes decreased from 76.7677kbps to 68.689kbps, for 30 nodes decreased from 100kbps to 83.5821kbps and for 50 nodes decreased from 18.94kbps to 15.94kbps.

One of the most dynamic network is the Mobile Adhoc (MANET) network. It is a list of numerous mobile nodes. Dynamic topology and lack of centralization are the basic characteristics of MANET. MANETs are prone to many attacks due to these characteristics. One of the attacks carried out on the network layer is the blackhole attack. In a black-hole attack, by sending false routing information, malicious nodes interrupt data transmission. There are two kinds of attacks involving a black-hole, single and co-operative. There is one malicious node in a single black-hole attack that can act as the node with the highest sequence number. The node source would follow the direction of the malicious node by taking the right direction. There is more than one malicious node in the collaborative black-hole attack. One node receives a packet and sends it to another malicious node in this attack. It is very difficult to detect and avoid black-hole attacks. Many researchers have invented black-hole attack detection and prevention systems. In this paper, We find a problem in the existing solution, in which validity bit is used. This paper also provides a comparative study of many scholars. The source node is used to detect and prevent black hole attacks by using a binary partition clustering based algorithm. We compared the performance of the proposed solution with existing solution and shown that our solution outperforms the existing one.

Mobile Adhoc networks (MANETs) comprises of mobile devices or nodes that are connected wirelessly and have no infrastructure. Detecting malicious activities in MANETs is a challenging task as they are vulnerable to attacks where the performance of the entire network degrades. Hence it is necessary to provide security to the network so that the nodes are prone to attack. Selecting a good routing protocol in MANET is also important as frequent change of topology causes the route reply to not arrive at the source node. In this paper, R-AODV (Reverse Adhoc On-Demand Distance Vector) protocol along with ECC (Elliptic Key Cryptography) algorithm is designed and implemented to detect and to prevent the malicious node and to secure data transmission against blackhole attack. The main objective is to keep the data packets secure. ECC provides a smaller key size compared to other public-key encryption and eliminates the requirement of pre-distributed keys also makes the path more secure against blackhole attacks in a MANET. The performance of this proposed system is simulated by using the NS-2.35 network simulator. Simulation results show that the proposed protocol provides good experimental results on various metrics like throughput, end-to-end delay, and PDR. Analysis of the results points to an improvement in the overall network performance.

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.

This paper presents Forensic methods for detection and prevention of multiple attacks along with neural networks like Denial-of-Service (DoS), probe, vampire, and User-to-Root (U2R) attacks, in a Mobile Ad hoc Network (MANET). We accomplish attacker(s) detection and prevention percentage upto 99% in varied node density scenarios 50/100/150.

The dynamic and adaptable characteristics of mobile ad hoc networks have made it a significant field for deploying various applications in wireless sensor networks. Increasing popularity of the portable devices is the main reason for the development of mobile ad hoc networks. Furthermore, the network does not require a fixed architecture and it is easy to deploy. This type of network is highly vulnerable to cyber-attacks as the nodes communicate with each other through a Wireless medium. The most critical attack in ad hoc network is jellyfish attack. In this research we have proposed a Direct Trust-based Detection Algorithm to detect and prevent jellyfish attack in MANET.

MANETs are wireless networks, providing properties such as self-configuration, mobility, and flexibility to the network, which make them a popular and widely used technique. As the usage and popularity of the networks increases, security becomes the most important factor to be concerned. For the sake of security, several protocols and methodologies have been developed for the networks. Along with the increase in security mechanisms, the number of attacks and attackers also increases and hence the threat to the network and secure communication within it increases as well. Some of the attacks have been resolved by the proposed methodologies but some are still a severe threat to the framework, one such attack is Black Hole Attack. The proposed work integrates the SUPERMAN (Security Using Pre-Existing Routing for Mobile Ad-hoc Networks) framework with appropriate methodology to detect and prevent the network from the Black Hole Attack. The mechanism is based on the AODV (Ad-hoc On-demand Distance Vector) routing protocol. In the methodology, the source node uses two network routes, from the source to the destination, one for sending the data packet and another for observing the intermediate nodes of the initial route. If any node is found to be a Black Hole node, then the route is dropped and the node is added to the Black Hole list and a new route to send the data packet to the destination is discovered.

Robotics Mobile Ad-hoc Networks (MANETs) are comprised of stations having mobility with no central authority and control. The stations having mobility in Robotics MANETs work as a host as well as a router. Due to the unique characteristics of Robotics MANETs such type of networks are vulnerable to different security attacks. Ad-hoc On-demand Distance Vector (AODV) is a routing protocol that belongs to the reactive category of routing protocols in Robotics MANETs. However, it is more vulnerable to the Black hole (BH) attack that is one of the most common attacks in the Robotics MANETs environment. In this attack during the route disclosure procedure a malicious station promotes itself as a most brief path to the destination as well as after that drop every one of the data gotten by the malicious station. Meanwhile the packets don't reach to its ideal goal, the BH attack turns out to be progressively escalated when a heap of malicious stations attack the system as a gathering. This research analyzed different BH finding as well as removal mechanisms for AODV routing protocol.

The wireless technology has knocked the door of tremendous usage and popularity in the last few years along with a high growth rate for new applications in the networking domain. Mobile Ad hoc Networks (MANETs) is solitary most appealing, alluring and challenging field where in the participating nodes do not require any active, existing and centralized system or rigid infrastructure for execution purpose and thus nodes have the moving capability on arbitrary basis. Radio range nodes directly communicate with each other through the wireless links whereas outside range nodes uses relay principle for communication. Though it is a rigid infrastructure less environment and has high growth rate but security is a major concern and becomes vital part of providing hostile free environment for communication. The MANET imposes several prominent challenges such as limited energy reserve, resource constraints, highly dynamic topology, sharing of wireless medium, energy inefficiency, recharging of the batteries etc. These challenges bound to make MANET more susceptible, more close to attacks and weak unlike the wired line networks. Theresearch paperismainly focused on two aspects, one is computation termination of cluster head algorithm and another is use of finite state machine for attacks identification.

In the communication model of wired and wireless Adhoc networks, the most needed requirement is the integration of security. Mobile Adhoc networks are more aroused with the attacks compared to the wired environment. Subsequently, the characteristics of Mobile Adhoc networks are also influenced by the vulnerability. The pre-existing unfolding solutions are been obtained for infrastructure-less networks. However, these solutions are not always necessarily suitable for wireless networks. Further, the framework of wireless Adhoc networks has uncommon vulnerabilities and due to this behavior it is not protected by the same solutions, therefore the detection mechanism of intrusion is combinedly used to protect the Manets. Several intrusion detection techniques that have been developed for a fixed wired network cannot be applied in this new environment. Furthermore, The issue of intensity in terms of energy is of a major kind due to which the life of the working battery is very limited. The objective this research work is to detect the Anomalous behavior of nodes in Manet's and Experimental analysis is done by making use of Network Simulator-2 to do the comparative analysis for the existing algorithm, we enhanced the previous algorithm in order to improve the Energy efficiency and results shown the improvement of energy of battery life and Throughput is checked with respect to simulation of test case analysis. In this paper, the proposed algorithm is compared with the existing approach.

Mobile Ad-hoc Network (MANET) consists of different configurations, where it deals with the dynamic nature of its creation and also it is a self-configurable type of a network. The primary task in this type of networks is to develop a mechanism for routing that gives a high QoS parameter because of the nature of ad-hoc network. The Ad-hoc-on-Demand Distance Vector (AODV) used here is the on-demand routing mechanism for the computation of the trust. The proposed approach uses the Artificial neural network (ANN) and the Support Vector Machine (SVM) for the discovery of the black hole attacks in the network. The results are carried out between the black hole AODV and the security mechanism provided by us as the Secure AODV (SAODV). The results were tested on different number of nodes, at last, it has been experimented for 100 nodes which provide an improvement in energy consumption of 54.72%, the throughput is 88.68kbps, packet delivery ratio is 92.91% and the E to E delay is of about 37.27ms.

Vehicular Ad-hoc Networks (VANETs) are generally acknowledged as an extraordinary sort of Mobile Ad hoc Network (MANET). VANETs have seen enormous development in a decade ago, giving a tremendous scope of employments in both military and in addition non-military personnel exercises. The temporary network in the vehicles can likewise build the driver's capability on the road. In this paper, an effective information dispersal approach is proposed which enhances the vehicle-to-vehicle availability as well as enhances the QoS between the source and the goal. The viability of the proposed approach is shown with regards to the noteworthy gets accomplished in the parameters in particular, end to end delay, packet drop ratio, average download delay and throughput in comparison with the existing approaches.

Vehicular Adhoc Network (VANET), a specialized form of MANET in which safety is the major concern as critical information related to driver's safety and assistance need to be disseminated between the vehicle nodes. The security of the nodes can be increased, if the network availability is increased. The availability of the network is decreased, if there is Denial of Service Attacks (DoS) in the network. In this paper, a packet detection algorithm for the prevention of DoS attacks is proposed. This algorithm will be able to detect the multiple malicious nodes in the network which are sending irrelevant packets to jam the network and that will eventually stop the network to send the safety messages. The proposed algorithm was simulated in NS-2 and the quantitative values of packet delivery ratio, packet loss ratio, network throughput proves that the proposed algorithm enhance the security of the network by detecting the DoS attack well in time.

Ad hoc network is sensitive to attacks because it has temporary nature and frequently recognized insecure environment. Both Ad hoc On-demand Distance Vector (AODV) and Ad hoc On-demand Multipath Distance vector (AOMDV) routing protocols have the strategy to take help from Wireless and mobile ad hoc networks. A mobile ad hoc network (MANET) is recognized as an useful internet protocol and where the mobile nodes are self-configuring and self-organizing in character. This research paper has focused on the detection and influence of black hole attack on the execution of AODV and AOMDV routing protocols and has also evaluated the performance of those two on-demand routing protocols in MANETs. AODV has the characteristics for discovering a single path in single route discovery and AOMDV has the characteristics for discovering multiple paths in single route discovery. Here a proposed method for both AODV and AOMDV routing protocol, has been applied for the detection of the black hole attack, which is the merge of both SHA-3 and Diffie-Hellman algorithm. This merge technique has been applied to detect black hole attack in MANET. This technique has been applied to measure the performance matrices for both AODV and AOMDV and those performance matrices are Average Throughput, Average End to End delay and Normalized Routing Load. Both AODV and AOMDV routing protocol have been compared with each other to show that under black hole attack, AOMDV protocol always has better execution than AODV protocol. Here, NS-2.35 has been used as the Network Simulator tool for the simulation of these particular three types of performance metrics stated above.

Mobile ad-hoc network (MANETS) is generally appropriate in different territories like military tactical network, educational, home and entertainment and emergency operations etc. The MANETSs are simply the disintegration and designing kind of system in this portable hubs coming up and out the system whenever. Because of decentralized creation of the network, security, routing and Standard of service are the three noteworthy issues. MANETSs are helpless against security attack in light of the decentralized validation. The mobile hubs can enter or out the system and at some point malicious hubs enter the system, which are capable to trigger different dynamic and inactive attack. The flooding attack is the dynamic sort of attack in which malicious hubs transfers flooding packets on the medium. Because of this, medium gets over-burden and packets drop may happen inside the system. This decreases the throughput and increased packet loss. In this paper we illustrated different techniques and proposed various methods responsible for flooding attack. Our commitment in this paper is that we have investigated various flooding attacks in MANETs, their detection techniques with performance measure parameters.

The mobile ad-hoc networks comprised of nodes that are communicated through dynamic request and also by static table driven technique. The dynamic route discovery in AODV routing creates an unsecure transmission as well as reception. The reason for insecurity is the route request is given to all the nodes in the network communication. The possibility of the intruder nodes are more in the case of dynamic route request. Wormhole attacks in MANETs are creating challenges in the field of network analysis. In this paper the wormhole scenario is realized using high power transmission. This is implemented using energy model of ns2 simulator. The Apptool simulator identifies the energy level of each node and track the node of high transmission power. The performance curves for throughput, node energy for different encrypted values, packet drop ratio, and end to end delay are plotted.

Mobile Ad-hoc network is decentralized and composed of various individual devices for communicating with each other. Its distributed nature and infrastructure deficiency are the way for various attacks in the network. On implementing Intrusion detection systems (IDS) in ad-hoc node securities were enhanced by means of auditing and monitoring process. This system is composed with clustering protocols which are highly effective in finding the intrusions with minimal computation cost on power and overhead. The existing protocols were linked with the routes, which are not prominent in detecting intrusions. The poor route structure and route renewal affect the cluster hardly. By which the cluster are unstable and results in maximization processing along with network traffics. Generally, the ad hoc networks are structured with battery and rely on power limitation. It needs an active monitoring node for detecting and responding quickly against the intrusions. It can be attained only if the clusters are strong with extensive sustaining capability. Whenever the cluster changes the routes also change and the prominent processing of achieving intrusion detection will not be possible. This raises the need of enhanced clustering algorithm which solved these drawbacks and ensures the network securities in all manner. We proposed CBIDP (cluster based Intrusion detection planning) an effective clustering algorithm which is ahead of the existing routing protocol. It is persistently irrespective of routes which monitor the intrusion perfectly. This simplified clustering methodology achieves high detecting rates on intrusion with low processing as well as memory overhead. As it is irrespective of the routes, it also overcomes the other drawbacks like traffics, connections and node mobility on the network. The individual nodes in the network are not operative on finding the intrusion or malicious node, it can be achieved by collaborating the clustering with the system.

This research proposes an inspection on Trust Based Routing protocols to protect Internet of Things directing to authorize dependability and privacy amid to direction-finding procedure in inaccessible systems. There are number of Internet of Things (IOT) gadgets are interrelated all inclusive, the main issue is the means by which to protect the routing of information in the important systems from different types of stabbings. Clients won't feel secure on the off chance that they know their private evidence could without much of a stretch be gotten to and traded off by unapproved people or machines over the system. Trust is an imperative part of Internet of Things (IOT). It empowers elements to adapt to vulnerability and roughness caused by the through and through freedom of other devices. In Mobile Ad-hoc Network (MANET) host moves frequently in any bearing, so that the topology of the network also changes frequently. No specific algorithm is used for routing the packets. Packets/data must be routed by intermediate nodes. It is procumbent to different occurrences ease. There are various approaches to compute trust for a node such as fuzzy trust approach, trust administration approach, hybrid approach, etc. Adaptive Information Dissemination (AID) is a mechanism which ensures the packets in a specific transmission and it analysis of is there any attacks by hackers.It encompasses of ensuring the packet count and route detection between source and destination with trusted path.Trust estimation dependent on the specific condition or setting of a hub, by sharing the setting information onto alternate hubs in the framework would give a superior answer for this issue.Here we present a survey on various trust organization approaches in MANETs. We bring out instantaneous of these approaches for establishing trust of the partaking hubs in a dynamic and unverifiable MANET atmosphere.

For the security of mobile ad-hoc networks (MANETs), a group of wireless mobile nodes needs to cooperate by forwarding packets, to implement an intrusion detection system (IDS). Some of the current IDS implementations in a clustered MANET have designed mobile nodes to wait until the cluster head is elected before scanning the network and thus nodes may be, unfortunately, exposed to several control packet attacks by which nodes identify falsified routes to reach other nodes. In order to detect control packet attacks such as route falsification, we design a route cache sharing mechanism for a non-clustered network where all one-hop routing data are collected by each node for a cooperative host-based detection. The cooperative host-based detection system uses a Support Vector Machine classifier and achieves a detection rate of around 95%. By successfully detecting the route falsification attacks, nodes are given the capability to avoid other attacks such as black-hole and gray-hole, which are in many cases a result of a successful route falsification attack.