Biblio

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.

Mobile Ad hoc Network (MANET) routing is basic and route selection ought to be made faster before the node leaves the system. MANET routing Methods are intended to work in a friendly and satisfying condition which makes them helpless against different attacks. MANET is one of the most encouraging fields for innovative work of remote system. MANET has now turned out to be one of the most lively and dynamic field of communication among systems. A MANET is a self-sufficient gathering of mobile nodes that speak with one another over remote connections and coordinate in an appropriated way so as to give the fundamental system convenience without a fixed framework. MANET has transfer speed limitations yet it permits self-ruling communication of versatile clients over it. Because of regular node mobility, and along these lines change in route topology, the architecture of the system goes unpredicted after some time. In such a decentralized situation, secured route identification is a key task for communication among nodes. Trust calculation among nodes is done for involving trusted nodes in route discovery process. In this manuscript, a novel secure routing method is proposed which identifies route among trusted nodes and update the routing table info frequently because of dynamic topology of the network. The outcomes demonstrate that the proposed method takes better routing technique when compared with existing methods.

The massive growth of the global routing tables is one of the biggest problems that still face internet nowadays. This problem is mainly caused by the random distribution of IPv4 addresses. With the immigration to IPv6 and the large ranges of addresses provided by this protocol, it is crucial to wisely manage the assignment of IPv6 prefixes. In this paper, we propose a process to generate a logical topology of IPv6 networks. This topology uses perfectly the summarization technique and consists in representing the summary routes in hierarchical manner such that large range of addresses represents several smaller ranges. The proposed aggregation process optimizes and divides up the routing tables which may help resolve the problem of the explosive growth of internet routing tables. Furthermore, the logical topology can be easly customized to fit the features of the routers that are used in the network.

Mobile Ad Hoc Network (MANET) is pretty vulnerable to attacks because of its broad distribution and open nodes. Hence, an effective Intrusion Detection System (IDS) is vital in MANET to deter unwanted malicious attacks. An IDS has been proposed in this paper based on watchdog and pathrater method as well as evaluation of its performance has been presented using Dynamic Source Routing (DSR) and Ad-hoc On-demand Distance Vector (AODV) routing protocols with and without considering the effect of the sinkhole attack. The results obtained justify that the proposed IDS is capable of detecting suspicious activities and identifying the malicious nodes. Moreover, it replaces the fake route with a real one in the routing table in order to mitigate the security risks. The performance appraisal also suggests that the AODV protocol has a capacity of sending more packets than DSR and yields more throughput.

A mobile ad hoc network is a type of ad hoc network in which node changes it locations and configures them. It uses wireless medium to communicate with other networks. It also does not possess centralized authority and each node has the ability to perform some tasks. Nodes in this type of network has a routing table depending on which it finds the optimal way to send packets in forward direction but link failure should be updated in node table to encompass that. In civilian environment like meeting rooms, cab networking etc, in military search and rescue operations it has huge application.

Mobile Ad hoc Network (MANET) is one of the most popular dynamic topology reconfigurable local wireless network standards. Distributed Denial of Services is one of the most challenging threats in such a network. Flooding attack is one of the forms of DDoS attack whereby certain nodes in the network miss-utilizes the allocated channel by flooding packets with very high packet rate to it's neighbors, causing a fast energy loss to the neighbors and causing other legitimate nodes a denial of routing and transmission services from these nodes. In this work we propose a novel link layer assessment based flooding attack detection and prevention method. MAC layer of the nodes analyzes the signal properties and incorporated into the routing table by a cross layer MAC/Network interface. Once a node is marked as a flooding node, it is blacklisted in the routing table and is communicated to MAC through Network/MAC cross layer interface. Results shows that the proposed technique produces more accurate flooding attack detection in comparison to current state of art statistical analysis based flooding attack detection by network layer.

Software Defined Networks (SDNs) have gained prominence recently due to their flexible management and superior configuration functionality of the underlying network. SDNs, with OpenFlow as their primary implementation, allow for the use of a centralised controller to drive the decision making for all the supported devices in the network and manage traffic through routing table changes for incoming flows. In conventional networks, machine learning has been shown to detect malicious intrusion, and classify attacks such as DoS, user to root, and probe attacks. In this work, we extend the use of machine learning to improve traffic tolerance for SDNs. To achieve this, we extend the functionality of the controller to include a resilience framework, ReSDN, that incorporates machine learning to be able to distinguish DoS attacks, focussing on a neptune attack for our experiments. Our model is trained using the MIT KDD 1999 dataset. The system is developed as a module on top of the POX controller platform and evaluated using the Mininet simulator.