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Serious concerns have been raised about stealthy leakage of users privacy in mobile apps, and many recent approaches are also proposed to detect privacy leak in these apps. However, more and more benign mobile apps have to send out user's privacy for legitimate functions or user intention. To evade detection, new mobile malware starts to mimic privacy-related behaviors of benign apps that provide...
As Android becoming the most popular smart phone operating system, malicious applications running on the Android platform appears very frequently and poses the major threat to the security of Android. Considering the resources of smart phone are severely limited, a stable, simple and quick malware detection method for Android is indispensable. In this paper, we propose an ultra-lightweight malware...
P2P technology has been widely applied in many areas due to its excellent properties. Some botnets also shift towards the decentralized architectures, since they provide a better resiliency against detection and takedown efforts. Besides, modern P2P bots tend to run on compromised hosts in a stealthy way, which renders most existing approaches ineffective. In addition, few approaches address the problem...
The rapid development of Peer-to-Peer (P2P) technology brings challenges to quality of service (QoS), network planning and access control. An accurate classification of P2P traffic is vital for addressing those challenges. Traditional port-based and payload-based methods fail to cope with emerging port disguise and payload encryption techniques. In this paper, we present Peer Sorter, a system for...
To develop Human-centric Driver Assistance Systems (HDAS) for automatic understanding and charactering of driver behaviors, an efficient feature extraction of driving postures based on Geronimo–Hardin–Massopust (GHM) multiwavelet transform is proposed, and Multilayer Perceptron (MLP) classifiers with three layers are then exploited in order to recognize four pre-defined classes of driving postures...
With the continuous development of Internet technology, accurate classification of network traffic becomes more and more important. Statistics-based traffic classification with extremely accuracy and high expansibility has become the mainstream of this domain. However, this method also has some shortcomings, such as, overabundance of statistical features, and insufficient flexibility of feature vector...
The classification of unstructured P2P multicast video streaming is the premise for playing online linkage and real-time evidence in the process of network monitoring management. Based on the classification method in the preliminary research, an improved classification method is proposed. the method uses an optimal feature vector extraction algorithm to filter the proposed behavior features in the...
A novel feature extraction approach for fatigue expressions of vehicle drivers, which consists of Viola-Jones face detection algorithm and Gabor wavelet transform, was proposed. With features extracted from the fatigue expressions dataset created at Southeast University, holdout experiments on fatigue expressions classification are created using Multilayer Perceptron (MLP) classifier, compared with...
The objective of this study is to investigate different pattern classification paradigms in the automatically understanding and characterizing driver behaviors. With features extracted from a driving posture dataset consisting of grasping the steering wheel, operating the shift lever, eating a cake and talking on a cellular phone, created at Southeast University, holdout and cross-validation experiments...
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