☆ 4.6 Article

DroidFDR: Automatic Classification of Android Malware Using Model Checking

ELECTRONICS (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Information Systems

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

Yizhao Huang et al.

Summary: This article proposes an Android source code semantic enhancement model based on transfer learning for detecting and categorizing malware. The model utilizes the Transformer architecture and generative adversarial networks for pretraining and employs a regression model-based filter to retain high-quality comments and source code. Additionally, a quantum support vector machine is incorporated with classical deep learning models to improve classification accuracy.

ELECTRONICS (2022)

添加到收藏夹

Article Computer Science, Information Systems

Robust deep learning early alarm prediction model based on the behavioural smell for android malware

Eslam Amer et al.

Summary: Due to the expansion of the Android malware industry, mining malicious Android processes has become necessary. This paper proposes a behavioral Android malware smell predictor model that combines static and dynamic features to overcome the obstacles in mining malicious Android processes. The model shows competitive accuracy in predicting malicious behaviors and suggests an early alarm solution to block malicious payloads.

COMPUTERS & SECURITY (2022)

添加到收藏夹

Article Computer Science, Hardware & Architecture

SDAC: A Slow-Aging Solution for Android Malware Detection Using Semantic Distance Based API Clustering

Jiayun Xu et al.

Summary: SDAC is a novel slow-aging solution proposed to address the model aging problem in Android malware detection, achieving significantly higher accuracy and slower aging speed compared to state-of-the-art solutions. By evaluating the contributions of new APIs and evolving based on existing API contributions, SDAC effectively adapts to changes in Android specifications without the need for retraining on new labeled datasets.

IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING (2022)

添加到收藏夹

Article Computer Science, Hardware & Architecture

Open Source Intelligence for Malicious Behavior Discovery and Interpretation

Yi-Ting Huang et al.

Summary: This research proposes a malicious behavior analysis system based on the MITRE ATT&CK framework, which can effectively detect and respond to cyber threats and provides a mapping from malicious behaviors to ATT&CK techniques and API calls.

IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING (2022)

添加到收藏夹

Article Computer Science, Information Systems

A Multi-Modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store

Naveen Karunanayake et al.

Summary: This paper proposes a method of using deep learning techniques to identify counterfeit apps by combining content, style, and text embeddings, which has successfully increased accuracy and recall rates. An analysis of approximately 1.2 million apps from Google Play Store revealed a large number of potential counterfeit apps, which may contain malware or request excessive permissions.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2022)

添加到收藏夹

Article Computer Science, Information Systems

LibRoad: Rapid, Online, and Accurate Detection of TPLs on Android

Jian Xu et al.

Summary: Detection of third-party libraries (TPLs) is crucial in Android malware analysis. We propose a rapid, online, and accurate TPL detection approach called LibRoad, which addresses the limitations of previous methods. Our approach integrates application preprocessing and pairwise package matching components to reduce time complexity and improve precision.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2022)

添加到收藏夹

Article Computer Science, Theory & Methods

A Survey of Android Malware Detection with Deep Neural Models

Junyang Qiu et al.

Summary: Deep Learning (DL) is a disruptive technology that has revolutionized cyber security research, especially in the detection and classification of Android malware. While offering many advantages, DL faces challenges such as choice of architecture, feature extraction, and obtaining high-quality data.

ACM COMPUTING SURVEYS (2021)

添加到收藏夹

Article Computer Science, Information Systems

An efficient approach for taint analysis of android applications

Jie Zhang et al.

Summary: This paper introduces an efficient and precise approach for taint analysis in Android apps called FastDroid, which outperforms the state-of-the-art tool FlowDroid in terms of performance and efficiency according to the evaluation results.

COMPUTERS & SECURITY (2021)

添加到收藏夹

Article Computer Science, Software Engineering

Why an Android App Is Classified as Malware: Toward Malware Classification Interpretation

Bozhi Wu et al.

Summary: This article introduces a novel and interpretable machine learning-based approach (XMal) for Android malware detection and analysis. XMal not only accurately classifies malware, but also explains the classification results and malicious behavior descriptions, addressing the gaps in existing research.

ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY (2021)

添加到收藏夹

Review Computer Science, Information Systems

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

Janaka Senanayake et al.

Summary: With the increasing use of mobile devices, especially Android phones, malware attacks are on the rise. Machine learning-based methods have proven to be effective in detecting these attacks and a systematic review of ML-based Android malware detection techniques has been provided in this paper. The aim is to help researchers acquire in-depth knowledge in the field and identify potential future research and development directions.

ELECTRONICS (2021)

添加到收藏夹

Article Computer Science, Information Systems

Model checking for malicious family detection and phylogenetic analysis in mobile environment

Mario G. C. A. Cimino et al.

COMPUTERS & SECURITY (2020)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Model checking and machine learning techniques for HummingBad mobile malware detection and mitigation

Fabio Martinelli et al.

SIMULATION MODELLING PRACTICE AND THEORY (2020)

添加到收藏夹

Article Chemistry, Multidisciplinary

Call Graph and Model Checking for Fine-Grained Android Malicious Behaviour Detection

Giacomo Iadarola et al.

APPLIED SCIENCES-BASEL (2020)

添加到收藏夹

Article Computer Science, Theory & Methods

Android Malware Detection via (Somewhat) Robust Irreversible Feature Transformations

Qian Han et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2020)

添加到收藏夹

Article Computer Science, Software Engineering

LEILA: Formal Tool for Identifying Mobile Malicious Behaviour

Gerardo Canfora et al.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2019)

添加到收藏夹

Article Computer Science, Information Systems

Android Malware Detection Using Complex-Flows

Feng Shen et al.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2019)

添加到收藏夹

Article Computer Science, Theory & Methods

Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis

Ming Fan et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2018)

添加到收藏夹

Article Computer Science, Software Engineering

Towards Model Checking Android Applications

Guangdong Bai et al.

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING (2018)

添加到收藏夹

Article Computer Science, Information Systems

Amandroid: A Precise and General Inter-component Data Flow Analysis Framework for Security Vetting of Android Apps

Fengguo Wei et al.

ACM TRANSACTIONS ON PRIVACY AND SECURITY (2018)

添加到收藏夹

Article Computer Science, Theory & Methods

ICCDetector: ICC-Based Malware Detection on Android

Ke Xu et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2016)

添加到收藏夹

Article Computer Science, Information Systems

Droiddetector: android malware characterization and detection using deep learning

Zhenlong Yuan et al.

TSINGHUA SCIENCE AND TECHNOLOGY (2016)

添加到收藏夹

Article Computer Science, Theory & Methods

TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones

William Enck et al.

ACM TRANSACTIONS ON COMPUTER SYSTEMS (2014)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.