MANET Attack Detection - Mobile Adhoc Networks also known as MANETS or Wireless Adhoc Networks is a network that usually has a routable networking environment on top of a Link Layer ad hoc network. They consist of a set of mobile nodes connected wirelessly in a self-configured, self-healing network without having a fixed infrastructure. MANETS, have been predominantly utilized in military or emergency situations however, the prospects of Manets’ usage outside these realms is now being considered for possible public adoption in light of the recent global events such as the pandemic and new emerging infectious diseases. These particular events birthed new challenges, one of which was the considerable strain that was placed on mainstream ISP’s. Whilst there has been a significant amount of research conducted in the sphere Manet Security via various means such as: development of intrusion detection systems, attack classification and prediction systems, etcetera. There still exists prevailing concerns of MANET security and risks. Additionally, recently researched trends within the field has evidenced key disparities in terms of studies related to MANET Risk profiles. This paper seeks to provide an overview of existing studies with respect to MANETS as well as briefly introduces a new method of determining the initial Risk Profile of MANETS via the usage of probabilistic machine learning techniques. It explores new regions of probability-based approaches to further supplement the existing impact-based methodologies for assessing risk within Manets.

MANET Attack Detection - Mobile Ad-hoc network (MANET) has improved to be essential components of our daily lives. Due to its compatibility with multimedia data interchange in a mobile context, MANETs are employed in a variety of applications today, including those for crisis management and the battlefield, The popularity of infrastructure-less networks has grown along with the popularity of ad hoc networks in recent years as a result of the rise in wireless devices and technological developments MANETs have brought about a new type of technologies that allow them to operate without a fixed infrastructure. The dynamic nature of the MANET network makes it susceptible to numerous attacks. One of these is the wormhole, which spreads data from one site to another and can damage the network. If the source node chooses this fictitious route, the attacker has a backup plan to deliver or drop packets. In this paper, we proposed a technique by modifying the Ad-hoc On-demand Distance vector protocol (AODV) in the stage of RREQ and RREP with the sequence number transaction and the detection timer(DT). The proposed method when reached to 100 nodes, achieved the throughput of 95.5kbps, energy consumption of 55.9joule, end to end delay of 0.973sec and Packet Delivery Ratio (PDR) of 96.5%.

MANET Attack Detection - Nodes in a “distributed” Adhoc network do not share a single centralized infrastructure. Hosts and routers can be found on any mobile node. In addition, it sends packets to additional mobile nodes in the network that aren't directly connected to the main network. Network layer assaults such as black hole, wormhole, and denial-of-service (DoS) are all easily carried out on mobile Ad hoc networks (MANETs). Wrong-way attacks, which divert packets from one part of the network and route them through an alternate one, are extremely difficult to detect. Even though the wormhole attack has been countered, the current solutions still suffer from excessive delivery delays, packet delivery ratio issues, and energy consumption. In this paper, a cluster-based algorithm (CBA) detects hybrid wormhole assaults by computing based on sequence number, round-trip time (RTT), which is more optimistic than existing solutions for detecting both in-band and out-of-band connections are possible. RTT thresholds are predicted in this paper using CBA to distinguish between attack and non-attack routes. NS-2 network simulator is used to test the suggested technique. The proposed algorithm's performance was evaluated by looking at its throughput. Results demonstrate that CBA reduced 20% of total energy consumption compared to AODV, the traditional On-Demand Ad-hoc Distance Vector routing protocol.

MANET Attack Detection - One of the most essential self-configuring and independent wireless networks is the MANET. MANET employs a large number of intermediate nodes to exchange information without the need for any centralized infrastructure. However, some nodes act in a selfish manner, utilizing the network's resources solely for their own benefit and refusing to share with the surrounding nodes. Mobile ad hoc network security is a critical factor that is widely accepted. Selfish nodes are the primary problem of MANET. In a MANET, nodes that are only interested in themselves do not involve in the process of packet forwarding. A node can be identified as selfish or malicious due to some misbehavior reasons. Selfishness on the part of network nodes may be a factor in the low delivery ratio of packets and data loss. A high end-to-end delay is caused by node failure in a MANET network. To study the selfish node attack, a malicious selfish node is put into the network, and a trust-based algorithm for the selfish node attack is also suggested. In order to discover a solution to this issue, we have developed an algorithm called SNRM for the detection of selfish nodes. The routing protocol used in this paper for analysis is AODV. Using a simulation tool, PDR and end-to-end delay are evaluated and compared.

MANET Attack Detection - Recently, the mobile ad hoc network (MANET) has enjoyed a great reputation thanks to its advantages such as: high performance, no expensive infrastructure to install, use of unlicensed frequency spectrum, and fast distribution of information around the transmitter. But the topology of MANETs attracts the attention of several attacks. Although authentication and encryption techniques can provide some protection, especially by minimizing the number of intrusions, such cryptographic techniques do not work effectively in the case of unseen or unknown attacks. In this case, the machine learning approach is successful to detect unfamiliar intrusive behavior. Security methodologies in MANETs mainly focus on eliminating malicious attacks, misbehaving nodes, and providing secure routing. In this paper we present to most recent works that propose or apply the concept of Machine Learning (ML) to secure the MANET environment.

MANET Attack Detection - The current stady is confined in proposing a reputation based approach for detecting malicious activity where past activities of each node is recorded for future reference. It has been regarded that the Mobile ad-hoc network commonly called as (MANET) is stated as the critical wireless network on the mobile devices using self related assets. Security considered as the main challenge in MANET. Many existing work has done on the basis of detecting attacks by using various approaches like Intrusion Detection, Bait detection, Cooperative malicious detection and so on. In this paper some approaches for identifying malicious nodes has been discussed. But this Reputation based approach mainly focuses on sleuthing the critcal nodes on the trusted path than the shortest path. Each node will record the activity of its own like data received from and Transferred to information. As soon as a node update its activity it is verified and a trust factor is assigned. By comparing the assigned trust factor a list of suspicious or malicious node is created.

MANET Attack Detection - The MANET architecture's future growth will make extensive use of encryption and encryption to keep network participants safe. Using a digital signature node id, we illustrate how we may stimulate the safe growth of subjective clusters while simultaneously addressing security and energy efficiency concerns. The dynamic topology of MANET allows nodes to join and exit at any time. A form of attack known as a black hole assault was used to accomplish this. To demonstrate that he had the shortest path with the least amount of energy consumption, an attacker in MATLAB R2012a used a digital signature ID to authenticate the node from which he wished to intercept messages (DSEP). “Digital Signature”, “MANET,” and “AODV” are all terms used to describe various types of digital signatures. Black Hole Attack, Single Black Hole Attack, Digital Signature, and DSEP are just a few of the many terms associated with MANET.

MANET Attack Prevention - Wireless ad hoc networks are characterized by dynamic topology and high node mobility. Network attacks on wireless ad hoc networks can significantly reduce performance metrics, such as the packet delivery ratio from the source to the destination node, overhead, throughput, etc. The article presents an experimental study of an intrusion detection system prototype in mobile ad hoc networks based on machine learning. The experiment is carried out in a MANET segment of 50 nodes, the detection and prevention of DDoS and cooperative blackhole attacks are investigated. The dependencies of features on the type of network traffic and the dependence of performance metrics on the speed of mobile nodes in the network are investigated. The conducted experimental studies show the effectiveness of an intrusion detection system prototype on simulated data.

MANET Attack Prevention - All across the world, majority of humans rely upon wireless ADHOC network. So, it turns into the maximum priority to lessen the vulnerability of wireless network. Wireless networks are exposed to many distinct varieties of attacks out of which wormhole attack is most dangerous. Unlike many different attacks on ad hoc routing, wormhole attack could be very effective and cannot be avoided with cryptographic approach due to the fact intruders do now no longer modify the packet data, it replays the packets. An intentionally positioned wormhole can cause a significant breakdown in communication. An analysis was performed in this study that removed wormhole attacks from MANET using changes to the AODV routing protocol. We have used Smart Packet Detection and Prevention Technique (SPDPT) to remove Wormhole. We have examined simulation parameters such as packet delivery ratio, end-to-end delay, energy consumption, and throughput.

MANET Attack Prevention - Recently, the rising use of portable devices with advanced wireless communication gives Mobile ad-hoc networks more significance with the expanding number of widespread applications. This infrastructure uses a link-to-link wireless connection to transfer the data called route, which uses a routing protocol. AODV is a reactive protocol that uses control packets to discover a route toward the destination node in the network. Since MANET is an open infrastructure without a centralized controller, it is at risk of security assaults that are generated through the malicious node at the time of route discovery and data transmission. For example, the Blackhole attack in which the offender node retains and drops few or all data/control packets by using vulnerabilities of the on-demand routing protocols. This paper proposed a trust-based method to prevent the network against blackhole attack. This paper modeled the behavior of blackhole node and proposes a trust-based security technique. Further suggested technique is analyzed and evaluated against various evaluation metrics like PDR, throughput, end-to-end delay, attack percentage, etc. The proposed security technique is also compared with three different scenarios, namely attack, watchdog, and IDS scenarios, using the above evaluation metrics. The comparison shows that the proposed trust-based security ensures the detection and prevention against blackhole nodes not only at the time of route discovery but also at the time of real-time data transmission.

MANET Attack Prevention - Since the mid-1990s, the growth of laptops and Wi-Fi networks has led to a great increase in the use of MANET (Mobile ad hoc network) in wireless communication. MANET is a group of mobile devices for example mobile phones, computers, laptops, radios, sensors, etc., that communicate with each other wirelessly without any support from existing internet infrastructure or any other kind of fixed stations. As MANET is an infrastructure-less network it is prone to various attacks, which can lead to loss of information during communication, security breaches or other unauthentic malpractices. Various types of attacks to which MANET can be vulnerable are denial of service (DOS) and packet dropping attacks such as Gray hole, Blackhole, Wormhole, etc. In this research, we are particularly focusing on the detection and prevention of Gray hole attack. Gray hole node drops selective data packets, while participating in the routing process like other nodes, and advertises itself as a genuine node. The Intrusion Detection System (IDS) technique is used for identification and aversion of the Gray hole attack. Use of AODV routing protocol is made in the network. The network is incorporated and simulation parameters such as PDR (Packet Delivery Ratio), Energy Consumption, End-to-end delay, and Throughput are analyzed using simulation software.

MANET Attack Prevention - The MANET architecture's future growth will make extensive use of encryption and encryption to keep network participants safe. Using a digital signature node id, we illustrate how we may stimulate the safe growth of subjective clusters while simultaneously addressing security and energy efficiency concerns. The dynamic topology of MANET allows nodes to join and exit at any time. A form of attack known as a black hole assault was used to accomplish this. To demonstrate that he had the shortest path with the least amount of energy consumption, an attacker in MATLAB R2012a used a digital signature ID to authenticate the node from which he wished to intercept messages (DSEP). “Digital Signature”, “MANET,” and “AODV” are all terms used to describe various types of digital signatures. Black Hole Attack, Single Black Hole Attack, Digital Signature, and DSEP are just a few of the many terms associated with MANET.

Wireless ad hoc networks are characterized by dynamic topology and high node mobility. Network attacks on wireless ad hoc networks can significantly reduce performance metrics, such as the packet delivery ratio from the source to the destination node, overhead, throughput, etc. The article presents an experimental study of an intrusion detection system prototype in mobile ad hoc networks based on machine learning. The experiment is carried out in a MANET segment of 50 nodes, the detection and prevention of DDoS and cooperative blackhole attacks are investigated. The dependencies of features on the type of network traffic and the dependence of performance metrics on the speed of mobile nodes in the network are investigated. The conducted experimental studies show the effectiveness of an intrusion detection system prototype on simulated data.

The MANET architecture's future growth will make extensive use of encryption and encryption to keep network participants safe. Using a digital signature node id, we illustrate how we may stimulate the safe growth of subjective clusters while simultaneously addressing security and energy efficiency concerns. The dynamic topology of MANET allows nodes to join and exit at any time. A form of attack known as a black hole assault was used to accomplish this. To demonstrate that he had the shortest path with the least amount of energy consumption, an attacker in MATLAB R2012a used a digital signature ID to authenticate the node from which he wished to intercept messages (DSEP). “Digital Signature”, “MANET,” and “AODV” are all terms used to describe various types of digital signatures. Black Hole Attack, Single Black Hole Attack, Digital Signature, and DSEP are just a few of the many terms associated with MANET.