Number Of Attack Nodes Research Articles

k , l -Anonymity in Wheel-Related Social Graphs Measured on the Base of k -Metric Antidimension

For the study and valuation of social graphs, which affect an extensive range of applications such as community decision-making support and recommender systems, it is highly recommended to sustain the resistance of a social graph G to active attacks. In this regard, a novel privacy measure, called the k , l -anonymity, is used since the last few years on the base of k -metric antidimension of G in which l is the maximum number of attacker nodes defining the k -metric antidimension of G for the smallest positive integer k . The k -metric antidimension of G is the smallest number of attacker nodes less than or equal to l such that other k nodes in G cannot be uniquely identified by the attacker nodes. In this paper, we consider four families of wheel-related social graphs, namely, Jahangir graphs, helm graphs, flower graphs, and sunflower graphs. By determining their k -metric antidimension, we prove that each social graph of these families is the maximum degree metric antidimensional, where the degree of a vertex is the number of vertices linked with that vertex.

Random-Session and K-Neighbour Based Suspected Node Analysis Approach for Cooperative Blackhole Detection in MANET

Cooperative Blackhole is generated by two or more attackers with mutual handshaking. The nodes behave normally in reciprocal interaction and communication. In this paper, a session based suspected node evaluation model is presented to detect and prevent the cooperative blackhole attacks. A session is established for prior communication estimation as the communication begins. The parameter specific evaluation is performed over the session to identify the distrusted nodes. After this session, the continuous observation on behaviour of suspected nodes is performed throughout the communication. K-neighbour evaluation method is applied to recognize the cooperative attack behaviour of the suspected nodes. The evaluation is performed randomly on smaller separate sessions during the communication. The status update on nodes is also done based to recognize the attacker or safe node. The degree of conjunctive interaction in a flow is observed to recognize the cooperative attacker nodes. The proposed model is simulated on the multiple networks with variations in terms of the number of nodes, node speed and the number of attacker nodes. The evaluations are taken against the independent blackhole and cooperative blackhole nodes. The comparative evaluation against AODV, Probabilistic Blackhole Detection methods verified the reliability and effectiveness of the proposed model. The qualified evaluation performed on PDR and lossrate parameters verified that the investigated model has improved the PDR ratio significantly for highly infected, scattered and dense networks.

Impact of selective forwarding attack on AODV protocol in mobile wireless senor networks

wireless sensor network is the growing field of research having capacity to help the mankind with its usage in varied areas like military surveillance, medical etc. However these networks are vulnerable to certain attacks like black hole, wormhole and selective forwarding. In this paper selective forwarding attack is analyzed on varying number of attacker nodes and its impact on different performance parameters. It is concluded that the performance of the entire network is degraded with the increase in the number of malicious nodes.

Performance analysis of mobile ad hoc networks under flooding attacks

Due to their characteristics of dynamic topology, wireless channels and limited resources, mobile ad hoc networks are particularly vulnerable to a denial of service (DoS) attacks launched by intruders. The effects of flooding attacks in network simulation 2 (NS2) and measured performance parameters are investigated, including packet loss ratio, average delay, throughput and average number of hops under different numbers of attack nodes, flooding frequency, network bandwidth and network size. Simulation results show that with the increase of the flooding frequency and the number of attack nodes, network performance sharply drops. But when the frequency of flooding attacks or the number of attack nodes is greater than a certain value, performance degradation tends to a stable value.