In recent years, online feature selection has been a research topic on streaming feature mining, as it can reduce the dimensionality of the streaming features by removing the irrelevant and redundant features in real time. There are many representative research efforts on the online feature selection with streaming features, i.e., alpha − investing, online streaming feature selection (OSFS), and scalable and accurate online approach (SAOLA) for feature selection. In these studies, alpha-investing has limited prediction accuracy and a large number of selected features. SAOLA sometimes offers outstanding efficiency in running time and prediction accuracy but possesses a large number of selected features. OSFS offers high prediction accuracy in many datasets, but its running time increases exponentially with an increasing number of features with low redundancy and high relevance. To address the limitations of the above-mentioned works, we propose an online learning algorithm named OSFASW , which samples streaming features in real-time by a self-adaption sliding-window and discards the irrelevant and redundant features by conditional independence. The OSFASW obtains an approximate Markov blanket with high prediction accuracy, meanwhile reducing the number of selected features. The efficiency of the proposed OSFASW algorithm was validated in a performance test on widely used datasets, e.g., NIPS2003 and causalityworkbench. Through the extensive experimental results, we demonstrate that OSFASW significantly improves the prediction accuracy and requires a smaller number of selected features than alpha − investing, OSFS, and SAOLA.

Online feature selection is a challenging topic in data mining. It aims to reduce the dimensionality of streaming features by removing irrelevant and redundant features in real time. Existing works, such as Alpha-investing and Online Streaming Feature Selection (OSFS), have been proposed to serve this purpose, but they have drawbacks, including low prediction accuracy and high running time if the streaming features exhibit characteristics such as low redundancy and high relevance. In this paper, we propose a novel algorithm about online streaming feature selection, named ConInd that uses a three-layer filtering strategy to process streaming features with the aim of overcoming such drawbacks. Through three-layer filtering, i.e., null-conditional independence, single-conditional independence, and multi-conditional independence, we can obtain an approximate Markov blanket with high accuracy and low running time. To validate the efficiency, we implemented the proposed algorithm and tested its performance on a prevalent dataset, i.e., NIPS 2003 and Causality Workbench. Through extensive experimental results, we demonstrated that ConInd offers significant performance improvements in prediction accuracy and running time compared to Alpha-investing and OSFS. ConInd offers 5.62% higher average prediction accuracy than Alpha-investing, with a 53.56% lower average running time compared to that for OSFS when the dataset is lowly redundant and highly relevant. In addition, the ratio of the average number of features for ConInd is 242% less than that for Alpha-investing.

The Android operating system provides a rich Inter-Component Communication (ICC) method that brings enormous convenience. However, the Android ICC also increases security risks. To address this problem, a formal method is proposed to model and detect inter-component communication behavior in Android applications. Firstly, we generate data flow graphs and data facts for each component through component-level data flow analysis.Secondly, our approach treats ICC just like method calls. After analyzing the fields and data dependencies of the intent, we identify the ICC caller and callee, track the data flow between them, and construct the ICC model. Thirdly, the behavior model of Android applications is constructed by a formal mapping method for component data flow graph based on Pi calculus. The runtime sensitive path trigger detection algorithm is then given. Communicationbased attacks are detected by analyzing intent abnormity. Finally, we analyze the modeling and detection efficiency, and compare it with relevant methods. Analysis of 57 real-world applications partly verifies the effectiveness of the proposed method.

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