547 research outputs found
Android Malware Characterization using Metadata and Machine Learning Techniques
Android Malware has emerged as a consequence of the increasing popularity of
smartphones and tablets. While most previous work focuses on inherent
characteristics of Android apps to detect malware, this study analyses indirect
features and meta-data to identify patterns in malware applications. Our
experiments show that: (1) the permissions used by an application offer only
moderate performance results; (2) other features publicly available at Android
Markets are more relevant in detecting malware, such as the application
developer and certificate issuer, and (3) compact and efficient classifiers can
be constructed for the early detection of malware applications prior to code
inspection or sandboxing.Comment: 4 figures, 2 tables and 8 page
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections
The influence of Deep Learning on image identification and natural language
processing has attracted enormous attention globally. The convolution neural
network that can learn without prior extraction of features fits well in
response to the rapid iteration of Android malware. The traditional solution
for detecting Android malware requires continuous learning through
pre-extracted features to maintain high performance of identifying the malware.
In order to reduce the manpower of feature engineering prior to the condition
of not to extract pre-selected features, we have developed a coloR-inspired
convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2)
system. The system can convert the bytecode of classes.dex from Android archive
file to rgb color code and store it as a color image with fixed size. The color
image is input to the convolutional neural network for automatic feature
extraction and training. The data was collected from Jan. 2017 to Aug 2017.
During the period of time, we have collected approximately 2 million of benign
and malicious Android apps for our experiments with the help from our research
partner Leopard Mobile Inc. Our experiment results demonstrate that the
proposed system has accurate security analysis on contracts. Furthermore, we
keep our research results and experiment materials on http://R2D2.TWMAN.ORG.Comment: Verison 2018/11/15, IEEE BigData 2018, Seattle, WA, USA, Dec 10-13,
2018. (Accepted
Defending Your Mobile Fortress: An In-Depth Look at on-Device Trojan Detection in Machine Learning: Systematic Literature Review
Mobile app trojans are becoming an increasingly serious threat to personal information security. They can cause severe damage by exposing sensitive and personally-identifying information to malicious actors. This paper’s contribution is a comprehensive review of the attack vectors for trojan attacks, and ways to eliminate the risks posed by attack vectors and generate settlement automatically. As such, such attacks must be prevented. In this study, we explore to find how to detect the trojan attack in detail, and the way that we know in machine learning. A review is conducted on the state-of-the-art methods using the preferred reporting items for reviews and meta-analyses (PRISMA) guidelines. We review literature from several publications and analyze the use of machine learning for on-device trojan detection. This review provides evidence for the effectiveness of machine learning in detecting such threats. The current trend shows that signature-based analysis using various metadata, such as permission, intent, API and system calls, and network analysis, are capable of detecting trojan attacks before and after the initial infectio
Protecting Android Devices from Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges
Advancements in microelectronics have increased the popularity of mobile devices like
cellphones, tablets, e-readers, and PDAs. Android, with its open-source platform, broad device support,
customizability, and integration with the Google ecosystem, has become the leading operating system for
mobile devices. While Android's openness brings benefits, it has downsides like a lack of official support,
fragmentation, complexity, and security risks if not maintained. Malware exploits these vulnerabilities for
unauthorized actions and data theft. To enhance device security, static and dynamic analysis techniques can
be employed. However, current attackers are becoming increasingly sophisticated, and they are employing
packaging, code obfuscation, and encryption techniques to evade detection models. Researchers prefer
flexible artificial intelligence methods, particularly deep learning models, for detecting and classifying
malware on Android systems. In this survey study, a detailed literature review was conducted to investigate
and analyze how deep learning approaches have been applied to malware detection on Android systems. The
study also provides an overview of the Android architecture, datasets used for deep learning-based detection,
and open issues that will be studied in the future
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