The Experts below are selected from a list of 11451 Experts worldwide ranked by ideXlab platform

Liang Xiao - One of the best experts on this subject based on the ideXlab platform.

  • Reinforcement Learning Based Mobile Offloading for Cloud-Based Malware Detection
    2017 IEEE Global Communications Conference GLOBECOM 2017 - Proceedings, 2018
    Co-Authors: Xiaoyue Wan, Geyi Sheng, Liang Xiao
    Abstract:

    Cloud-based Malware Detection improves the Detection performance for mobile devices that offload their Malware Detection tasks to security servers with much larger Malware database and powerful computational resources. In this paper, we investigate the competition of the radio transmission bandwidths and the data sharing of the security server in the dynamic Malware Detection game, in which each mobile device chooses its offloading rate of the application traces to the security server. As the Q-learning technique has a slow learning rate in the game with high dimension, we have designed a mobile Malware Detection based on hotbooting-Q techniques, which initiates the quality values based on the Malware Detection experience. We propose an offloading strategy based on deep Q-network technique with a deep convolutional neural network to further improve the Detection speed, the Detection accuracy, and the utility. Preliminary simulation results verify the Detection gain of the scheme compared with the Q-learning based strategy.

  • Cloud-based Malware Detection game for mobile devices with offloading
    IEEE Transactions on Mobile Computing, 2017
    Co-Authors: Liang Xiao, Xueli Huang
    Abstract:

    As accurate Malware Detection on mobile devices requires fast process of a large number of application traces, cloud-based Malware Detection can utilize the data sharing and powerful computational resources of security servers to improve the Detection performance. In this paper, we investigate the cloud-based Malware Detection game, in which mobile devices offload their application traces to security servers via base stations or access points in dynamic networks. We derive the Nash equilibrium (NE) of the static Malware Detection game and present the existence condition of the NE, showing how mobile devices share their application traces at the security server to improve the Detection accuracy, and compete for the limited radio bandwidth, the computational and communication resources of the server. We design a Malware Detection scheme with Q-learning for a mobile device to derive the optimal offloading rate without knowing the trace generation and the radio bandwidth model of other mobile devices. The Detection performance is further improved with the Dyna architecture, in which a mobile device learns from the hypothetical experience to increase its convergence rate. We also design a post-decision state learning-based scheme that utilizes the known radio channel model to accelerate the reinforcement learning process in the Malware Detection. Simulation results show that the proposed schemes improve the Detection accuracy, reduce the Detection delay, and increase the utility of a mobile device in the dynamic Malware Detection game, compared with the benchmark strategy.

Baojiang Cui - One of the best experts on this subject based on the ideXlab platform.

  • A Malware Detection Method Based on Sandbox, Binary Instrumentation and Multidimensional Feature Extraction
    Advances on Broad-Band Wireless Computing Communication and Applications, 2018
    Co-Authors: Chong Wang, Jianwei Ding, Tao Guo, Baojiang Cui
    Abstract:

    With the development of software security technology, more and more malicious programs constantly uses new confusion and feature hiding techniques, the Malware Detection technology need to upgrade urgently. This paper presents a Malware Detection method based on sandbox, binary instrumentation and multidimensional feature extraction. We introduced the design and implementation of sandbox, feature extractor and the classifier. Finally, we merged multiple models and get a pretty well classifier for the Malware Detection.

  • BWCCA - A Malware Detection Method Based on Sandbox, Binary Instrumentation and Multidimensional Feature Extraction
    Advances on Broad-Band Wireless Computing Communication and Applications, 2017
    Co-Authors: Chong Wang, Jianwei Ding, Tao Guo, Baojiang Cui
    Abstract:

    With the development of software security technology, more and more malicious programs constantly uses new confusion and feature hiding techniques, the Malware Detection technology need to upgrade urgently. This paper presents a Malware Detection method based on sandbox, binary instrumentation and multidimensional feature extraction. We introduced the design and implementation of sandbox, feature extractor and the classifier. Finally, we merged multiple models and get a pretty well classifier for the Malware Detection.

Eul Gyu Im - One of the best experts on this subject based on the ideXlab platform.

  • A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
    IEEE Transactions on Information Forensics and Security, 2019
    Co-Authors: Taeguen Kim, Boojoong Kang, Mina Rho, Sakir Sezer, Eul Gyu Im
    Abstract:

    With the widespread use of smartphones, the number of Malware has been increasing exponentially. Among smart devices, android devices are the most targeted devices by Malware because of their high popularity. This paper proposes a novel framework for android Malware Detection. Our framework uses various kinds of features to reflect the properties of android applications from various aspects, and the features are refined using our existence-based or similarity-based feature extraction method for effective feature representation on Malware Detection. Besides, a multimodal deep learning method is proposed to be used as a Malware Detection model. This paper is the first study of the multimodal deep learning to be used in the android Malware Detection. With our Detection model, it was possible to maximize the benefits of encompassing multiple feature types. To evaluate the performance, we carried out various experiments with a total of 41 260 samples. We compared the accuracy of our model with that of other deep neural network models. Furthermore, we evaluated our framework in various aspects including the efficiency in model updates, the usefulness of diverse features, and our feature representation method. In addition, we compared the performance of our framework with those of other existing methods including deep learning-based methods.

Zheng Yan - One of the best experts on this subject based on the ideXlab platform.

  • A survey on dynamic mobile Malware Detection
    Software Quality Journal, 2017
    Co-Authors: Ping Yan, Zheng Yan
    Abstract:

    The outstanding advances of mobile devices stimulate their wide usage. Since mobile devices are coupled with third-party applications, lots of security and privacy problems are induced. However, current mobile Malware Detection and analysis technologies are still imperfect, ineffective, and incomprehensive. Due to the specific characteristics of mobile devices such as limited resources, constant network connectivity, user activities and location sensing, and local communication capability, mobile Malware Detection faces new challenges, especially on dynamic runtime Malware Detection. Many intrusions or attacks could happen after a mobile app is installed or executed. The literature still expects practical and effective dynamic Malware Detection approaches. In this paper, we give a thorough survey on dynamic mobile Malware Detection. We first introduce the definition, evolution, classification, and security threats of mobile Malware. Then, we summarize a number of criteria and performance evaluation measures of mobile Malware Detection. Furthermore, we compare, analyze, and comment on existing mobile Malware Detection methods proposed in recent years based on evaluation criteria and measures. Finally, we figure out open issues in this research field and motivate future research directions.

Hongnian Yu - One of the best experts on this subject based on the ideXlab platform.

  • SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System
    IEEE Access, 2018
    Co-Authors: Saba Arshad, Munam A. Shah, Abdul Wahid, Amjad Mehmood, Houbing Song, Hongnian Yu
    Abstract:

    For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the Malware developer's attention. Android allows downloading and installation of apps from other unofficial market places. This gives Malware developers an opportunity to put repackaged malicious applications in third-party app-stores and attack the Android devices. A large number of Malware analysis and Detection systems have been developed which uses static analysis, dynamic analysis, or hybrid analysis to keep Android devices secure from Malware. However, the existing research clearly lags in detecting Malware efficiently and accurately. For accurate Malware Detection, multilayer analysis is required which consumes large amount of hardware resources of resource constrained mobile devices. This research proposes an efficient and accurate solution to this problem, named SAMADroid, which is a novel 3-level hybrid Malware Detection model for Android operating systems. The research contribution includes multiple folds. First, many of the existing Android Malware Detection techniques are thoroughly investigated and categorized on the basis of their Detection methods. Also, their benefits along with limitations are deduced. A novel 3-level hybrid Malware Detection model for Android operating systems is developed, that can provide high Detection accuracy by combining the benefits of the three different levels: 1) Static and Dynamic Analysis; 2) Local and Remote Host; and 3) Machine Learning Intelligence. Experimental results show that SAMADroid achieves high Malware Detection accuracy by ensuring the efficiency in terms of power and storage consumption.