In recent years huge potential benefits from novel applications in mobile ad hoc networks (MANET) have been discussed extensively. However, without robust security mechanisms and systems to provide safety shell through the MANET infrastructure, MANET applications can be vulnerable and hammered by malicious attackers easily. In order to detect misbehaved message routing and identify malicious attackers in MANET, schemes based on reputation concept have shown their advantages in this area in terms of good scalability and simple threshold-based detection strategy. We observed that previous reputation schemes generally use predefined thresholds which do not take into account the effect of behavior dynamics between nodes in a period of time. In this paper, we propose a Threshold-Adaptive Reputation System (TARS) to overcome the shortcomings of static threshold strategy and improve the overall MANET performance under misbehaved routing attack. A fuzzy-based inference engine is introduced to evaluate the trustiness of a node's one-hop neighbors. Malicious nodes whose trust values are lower than the adaptive threshold, will be detected and filtered out by their honest neighbors during trustiness evaluation process. The results of network simulation show that the TARS outperforms other compared schemes under security attacks in most cases and at the same time reduces the decrease of total packet delivery ratio by 67% in comparison with MANET without reputation system.

With the successful development and rapid advancement of social networking technology, people tend to exchange and share information via online social networks, such as Facebook and LINE.Massive amounts of information are aggregated promptly and circulated quickly among people. However, with the enormous volume of human-interactions, various types of swindles via online social networks have been launched in recent years. Effectively detecting fraudulent activities on social networks has taken on increased importance, and is a topic of ongoing interest. In this paper, we develop a fraud analysis and detection system based on real-time messaging communications, which constitute one of the most common human-interacted services of online social networks. An integrated platform consisting of various text-mining techniques, such as natural language processing, matrix processing and content analysis via a latent semantic model, is proposed. In the system implementation, we first collect a series of fraud events, all of which happened in Taiwan, to construct analysis modules for detecting such fraud events. An Android-based application is then built for alert notification when dubious logs and fraud events happen.