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“Andromaly”: a behavioral malware detection framework for android devices
This article presents Andromaly—a framework for detecting malware on Android mobile devices. The proposed framework realizes a Host-based Malware Detection System that continuously monitors variousExpand
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N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders
The proliferation of IoT devices that can be more easily compromised than desktop computers has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for newExpand
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Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
Neural networks have become an increasingly popular solution for network intrusion detection systems (NIDS). Their capability of learning complex patterns and behaviors make them a suitable solutionExpand
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Google Android: A Comprehensive Security Assessment
This research provides a security assessment of the Android framework-Google's software stack for mobile devices. The authors identify high-risk threats to the framework and suggest several securityExpand
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Detecting unknown malicious code by applying classification techniques on OpCode patterns
In previous studies classification algorithms were employed successfully for the detection of unknown malicious code. Most of these studies extracted features based on byte n-gram patterns in orderExpand
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Securing Android-Powered Mobile Devices Using SELinux
Google's Android framework incorporates an operating system and software stack for mobile devices. Using a general-purpose operating system such as Linux in mobile devices has advantages but alsoExpand
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A Survey of Data Leakage Detection and Prevention Solutions
SpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 100 pages (approximately 20,000-Expand
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Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks
This paper presents a study on detecting cyber attacks on industrial control systems (ICS) using convolutional neural networks. The study was performed on a Secure Water Treatment testbed (SWaT)Expand
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Automated Static Code Analysis for Classifying Android Applications Using Machine Learning
In this paper we apply Machine Learning (ML) techniques on static features that are extracted from Android's application files for the classification of the files. Features are extracted fromExpand
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Detection of Unauthorized IoT Devices Using Machine Learning Techniques
Security experts have demonstrated numerous risks imposed by Internet of Things (IoT) devices on organizations. Due to the widespread adoption of such devices, their diversity, standardizationExpand
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