1,721,247 research outputs found
VPNDroid:Malicious Android VPN detection using a CNN-RF method
Get PDFProtecting online privacy using Virtual Private Networks (VPNs) is not as simple as it seems, since many well-known VPNs may not be secure. Despite appearing to be secure on the surface, VPNs can be a complete privacy and security disaster by stealing bandwidth, infecting devices with malware, installing tracking libraries, stealing personal data, and leaving data exposed to third parties. Therefore, Android users must exercise caution when downloading and installing VPN software on their devices. To this end, this paper proposes a neural network combined with a random forest that identifies malicious and malware-infected VPNs based on app permissions, along with a novel dataset of malicious and benign Android VPNs. The experimental results demonstrate that our classifier achieves high accuracy and outperforms other standard classifiers in terms of evaluation metrics such as accuracy, precision, and recall
Anomaly detection in secure cloud environments using a Self-Organizing Feature Map (SOFM) model For clustering sets of R-ordered vector-structured features
No full textCloud computing delivers services over virtualized networks to many end-users. Cloud services are characterized by such attributes as on-demand self-service, broad network access, resource pooling, rapid and elastic resource provisioning and metered services of various qualities. Cloud networks provide data as well as multimedia and video services. Cloud computing for critical structure IT is a relative new area of potential applications. Cloud networks are classified into private cloud networks, public cloud networks and hybrid cloud networks. Anomaly detection systems are defined as a branch of intrusion detection systems that deal with identifying anomalous events with respect to normal system behavior. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate sets of ordered vector structured features that are used for detecting anomalies in the context of secure cloud environments is herein proposed. Multivalue inputs consist of reduced/aggregate ordered sets of vector and binary features. The nodes of the SOFM - after training - are indicative of local distributions of feature measurements during normal cloud operation. Anomalies are detected as outliers of the trained SOFM. Each structured vector consists of binary as well as histogram data. The aggregated Canberra distance is used to order histogram data whereas the Jaccard distance is used for multivalue binary data. The so-called Cross-Order Distance Matrix is defined for both cases. The distance depends upon the selection of a similarity/distance measure and a method for operating upon the elements of the Cross-Order Distance Matrix. Several methods of estimating the distance between two ordered sets of features are investigated in the course of this paper
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Deep Active Learning for Autonomous Navigation
Get PDFImitation learning refers to an agent's ability to mimic a desired behavior by learning from observations. A major challenge facing learning from demonstrations is to represent the demonstrations in a manner that is adequate for learning and efficient for real time decisions. Creating feature representations is especially challenging when extracted from high dimensional visual data. In this paper, we present a method for imitation learning from raw visual data. The proposed method is applied to a popular imitation learning domain that is relevant to a variety of real life applications; namely navigation. To create a training set, a teacher uses an optimal policy to perform a navigation task, and the actions taken are recorded along with visual footage from the first person perspective. Features are automatically extracted and used to learn a policy that mimics the teacher via a deep convolutional neural network. A trained agent can then predict an action to perform based on the scene it finds itself in. This method is generic, and the network is trained without knowledge of the task, targets or environment in which it is acting. Another common challenge in imitation learning is generalizing a policy over unseen situation in training data. To address this challenge, the learned policy is subsequently improved by employing active learning. While the agent is executing a task, it can query the teacher for the correct action to take in situations where it has low confidence. The active samples are added to the training set and used to update the initial policy. The proposed approach is demonstrated on 4 different tasks in a 3D simulated environment. The experiments show that an agent can effectively perform imitation learning from raw visual data for navigation tasks and that active learning can significantly improve the initial policy using a small number of samples. The simulated test bed facilitates reproduction of these results and comparison with other approaches
Intelligent Measurement in Unmanned Aerial Cyber Physical Systems for Traffic Surveillance
Get PDFAn adaptive framework for building intelligent measurement systems has been proposed in the paper and tested on simulated traffic surveillance data. The use of the framework enables making intelligent decisions related to the presence of anomalies in the surveillance data with the help of statistical analysis, computational intelligent and machine learning. Computational intelligence can also be effectively utilised for identifying the main contributing features in detecting anomalous data points within the surveillance data. The experimental results have demonstrated that a reasonable performance is achieved in terms of inferential accuracy and data processing speed
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