Because of the characteristic of openness and flexibility, Android has become the most popular mobile platform. However, it has also become the most targeted system by mobile malware. It is necessary for the users to have a fast and reliable detection method. In this paper, a two-layer method is proposed to detect malware in Android APPs. The first layer is permission, intent and component information based static malware detection model. It combines the static features with fully connected neural network to detect the malware and test its effectiveness through experiment, the detection rate of the first layer is 95.22%. Then the result (benign APPs from the first layer) is input into the second layer. In the second layer, a new method CACNN which cascades CNN and AutoEncoder, is used to detect malware through network traffic features of APPs. The detection rate of the second layer is 99.3% in binary classification (2-classifier). Moreover, the new two-layer model can also detect malware by its category (4-classifier) and malicious family (40-classifier). The detection rates are 98.2% and 71.48% respectively. The experimental results show that our two-layer method not only can achieve semi-supervise learning, but also can effectively improve the detection rate of malicious Android APPs.

Multi-label streaming feature selection has attracted extensive attention in diverse big data applications. However, most existing works focused on the scenarios where labels are independent, while ignoring the real scenarios that they may be interdependent and correlated with each other. This paper aims to fill this gap by developing a novel online multi-label streaming feature selection scheme by taking into account the existence of label correlation, known as (OMSFSLC). In our design, we first calculate the correlation degree between labels to obtain the label weight. Then, we integrate the mutual information and the label weight to evaluate the correlation between features and labels. In particular, it consists of three stages: 1) online significance analysis, which can determine the significant features via the correlation degree between the newly arriving features and labels; 2) online relevance analysis, which can obtain relevant features via the mutual information; and 3) online redundancy analysis, which can filter the redundant features for removal via pairwise comparison. We implement our solution and conduct extensive experiments on benchmark datasets for performance evaluations. The experimental results exhibit that OMSFSLC significantly outperforms the state-of-the-art methods in terms of effectiveness and efficiency.

Learning with streaming data has attracted extensive research interest in recent years. Existing online learning approaches have specific assumptions regarding data streams, such as requiring fixed or varying feature spaces with explicit patterns and balanced class distributions. While the data streams generated in many real scenarios commonly have arbitrarily incomplete feature spaces and dynamic imbalanced class distributions, making existing approaches be unsuitable for real applications. To address this issue, this paper proposes a novel O nline L earning from I ncomplete and I mbalanced D ata S treams (OLI $^{2}$ DS) algorithm. OLI $^{2}$ DS has a two-fold main idea: 1) it follows the empirical risk minimization principle to identify the most informative features of incomplete feature spaces, and 2) it develops a dynamic cost strategy to handle imbalanced class distributions in real-time by transforming F-measure optimization into a weighted surrogate loss minimization. To evaluate OLI $^{2}$ DS, we compare it with state-of-the-art related algorithms in three kinds of experiments. First, we adopt 14 real datasets to simulate three scenarios of incomplete feature spaces, i.e., trapezoidal, feature evolvable, and capricious data streams. Second, based on a benchmark online analyzer, we generate 13 datasets to simulate incomplete data streams with different imbalance ratios. Third, we analyze concept drift in two simulated scenes, i.e., online learning and data stream mining, and verify the adaption of OLI $^{2}$ DS on repeated concept drifts and variable imbalance ratios. The results demonstrate that OLI $^{2}$ DS achieves a significantly better performance than its rivals. Besides, a real-world case study on movie review classification is conducted to elaborate on our OLI $^{2}$ DS algorithm's effectiveness. Code is released at https://github.com/youdianlong/OLI2DS .

Online learning is critical for handling complex data streams in Big Data-related applications. This study explores a new online learning problem where both the features and labels are incomplete. Such incompleteness poses a critical challenge in determining the latent relationship between incomplete features and labels. Unfortunately, existing online learning methods only consider a few cases of incomplete feature spaces, such as trapezoidal, evolvable, and capricious data streams, limiting their applicability to this problem. To bridge this gap, this study proposes a novel algorithm of O nline L earning for Data Streams with I ncomplete F eatures and L abels (OLIFL). OLIFL imposes no constraints on changing patterns of feature space and does not require all instances to be labeled with two-fold ideas. First, OLIFL explores the informativeness of individual features to update the classifier by dynamically maintaining global feature space and updating the informativeness matrix. Second, it estimates the label confidence of unlabeled instances to control their negative effects by limiting the error upper bound. Extensive experiments on benchmark datasets are conducted in five scenarios: three incomplete feature (trapezoidal, evolvable, and capricious) spaces, and two incomplete labels (only missing labels and missing both features and labels). In addition, we explore the sensitivity of the model to parameters, and its usability and response efficiency in handling concept drifts. The results show that OLIFL significantly outperforms its rivals. Moreover, we use OLIFL to classify a movie review task as real application verification.

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 the large number of selected features. SAOLA sometimes offers outstanding efficiency in running time and prediction accuracy but possesses the 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., NIPS 2003 and Causality Workbench. Through 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.