Recently, in IoT, wireless sensor network has become a critical technology with which many applications in industries and human life can achieve smart IoT control. However, for those daily applications using WSN technology, malicious users can capture the sensor nodes much easily since these wireless sensor nodes are usually deployed in easily touched places. Once this node captured attack occurs, wireless sensor network soon faces various security risks. In this paper, in order to resist node captured attack, we propose a novel authentication information exchange scheme in WSN, which is very different from the previous authentication researches. Our idea is to add an authentication information exchange scheme in previous authentication scheme but not propose new one. We develop this scheme based on the idea of the association scheme of Home GWN and local sensor nodes. In this study, HGWN should contact all local sensor nodes and meanwhile is responsible for performing an authentication information exchange scheme for resisting security risk. In order to prevent the attacker from guessing communication period between HGWN and the sensor, we also design a dynamic contacting mechanism. We give a detail discussion of this scheme and validate it by three ways, security evaluation, BAN logic and performance evaluation, which proves that our authentication information exchange scheme can achieve security features and goals.

SUMMARYThe current network-based intrusion detection systems have a very high rate of false alarms, and this phenomena results in significant efforts to gauge the threat level of the anomalous traffic. In this paper, we propose an intrusion detection mechanism based on honeypot log similarity analysis and data mining techniques to predict and block suspicious flows before attacks occur. With honeypot logs and association rule mining, our approach can reduce the false alarm problem of intrusion detection because only suspicious traffic would be present in the honeypots. The proposed mechanism can reduce human effort, and the entire system can operate automatically. The results of our experiments indicate that the honeypot prediction system is practical for protecting assets from attacks or misuse.

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Nowadays, with the advance of Internet technology, social network is getting popular, which combines the virtual network and the real world. People employ this network to communicate with all social things of their interest, including shopping, making friends, sharing experiences of life, and so on. In social networks, the social data expands rapidly and the malicious users can easily get the social data. With the social information, they could conjecture the relationship among social network data via the systematical analysis tool. Hence, the personal privacy data in social network may be exposed to some unknown risks, and recently, these issues arising in such a network catch much attention. The protection of personal privacy social data becomes an important and urgent research in social networks. One of personal privacy social information is the relations between the individuals and their social groups, namely human relationships.Neighborhood attacks are incident to the exposure of human relationships. Previous studies try to conceal human relationships information with well-known k-anonymity protection to resist this attack in social networks. However, those researches do not take care of those attacks in weighted social network, which does not make sense due to the fact that people should have different relationships with different persons. In such weighted social network, the edge denotes the human relationship and the weight denotes the degree of this human relationship in social networks. Our study focuses on the k-anonymity protection scheme in weighted social networks, and our scheme can achieve k-anonymity protection under the expected conditions, less virtual edges added and fewer weights changed. Through the analysis with MATLAB tool, we show that our k-anonymity algorithm can achieve high anonymity protection rate under various k-anonymity policies.