SDN-Based Double Hopping Communication against Sniffer Attack

Sniffer attack has been a severe threat to network communication security. Traditional network usually uses static network configuration, which provides convenience to sniffer attack. In this paper, an SDN-based double hopping communication (DHC) approach is proposed to solve this problem. In DHC, ends in communication packets as well as the routing paths are changed dynamically. Therefore, the traffic will be distributed to multiple flows and transmitted along different paths. Moreover, the data from multiple users will be mixed, bringing difficulty for attackers in obtaining and recovering the communication data, so that sniffer attack will be prevented effectively. It is concluded that DHC is able to increase the overhead of sniffer attack, as well as the difficulty of communication data recovery

An SDN-Based Fingerprint Hopping Method to Prevent Fingerprinting Attacks

Fingerprinting attacks are one of the most severe threats to the security of networks. Fingerprinting attack aims to obtain the operating system information of target hosts to make preparations for future attacks. In this paper, a fingerprint hopping method (FPH) is proposed based on software-defined networks to defend against fingerprinting attacks. FPH introduces the idea of moving target defense to show a hopping fingerprint toward the fingerprinting attackers. The interaction of the fingerprinting attack and its defense is modeled as a signal game, and the equilibriums of the game are analyzed to develop an optimal defense strategy. Experiments show that FPH can resist fingerprinting attacks effectively

An algorithm for discovering vital nodes in regional networks based on stable path analysis

Abstract Vital node discovery is a hotspot in network topology research. The key is using the Internet’s routing characteristics to remove noisy paths and accurately describe the network topology. In this manuscript, a vital regional routing nodes discovery algorithm based on routing characteristics is proposed. We analyze the stability of multiple rounds of measurement results to overcome the single vantage point’s path deviation. The unstable paths are eliminated from the regional network which is constructed through probing for target area, and the pruned topology is more in line with real routing rules. Finally, we weight the edge based on the actual network’s routing characteristics and discover vital nodes in combination with the weighting degree. Unlike existing algorithms, the proposed algorithm reconstructs the network topology based on communication and transforms unweighted network connections into weighted connections. We can evaluate the node importance in a more realistic network structure. Experiments on the Internet measurement data (275 million probing results collected in 107 days) demonstrate that: the proposed algorithm outperforms four existing typical algorithms. Among 15 groups of comparison in 3 cities, our algorithm found more (or the same number) backbone nodes in 10 groups and found more (or the same number) national backbone nodes in 13 groups

SybilHP: Sybil Detection in Directed Social Networks with Adaptive Homophily Prediction

Worries about the increasing number of Sybils in online social networks (OSNs) are amplified by a range of security issues; thus, Sybil detection has become an urgent real-world problem. Lightweight and limited data-friendly, LBP (Loopy Belief Propagation)-based Sybil-detection methods on the social graph are extensively adopted. However, existing LBP-based methods that do not utilize node attributes often assume a global or predefined homophily strength of edges in the social graph, while different user’s discrimination and preferences may vary, resulting in local homogeneity differences. Another issue is that the existing message-passing paradigm uses the same edge potential when propagating belief to both sides of a directed edge, which does not agree with the trust interaction in one-way social relationships. To bridge these gaps, we present SybilHP, a Sybil-detection method optimized for directed social networks with adaptive homophily prediction. Specifically, we incorporate an iteratively updated edge homophily estimation into the belief propagation to better adapt to the personal preferences of real-world social network users. Moreover, we endow message passing on edges with directionality by a direction-sensitive potential function design. As a result, SybilHP can better capture the local homophily and direction pattern in real-world social networks. Experiments show that SybilHP works with high detection accuracy on synthesized and real-world social graphs. Compared with various state-of-the-art graph-based methods on a large-scale Twitter dataset, SybilHP substantially outperforms existing methods