Search Results - Characterization and Detection of Android Malware
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KronoDroid: Time-based Hybrid-featured Dataset for Effective Android Malware Detection and Characterization
ISSN: 0167-4048, 1872-6208Published: Amsterdam Elsevier Ltd 01.11.2021Published in Computers & security (01.11.2021)“…). Critical factors to take into account when aiming to build more effective, robust, and long-lasting Android malware detection systems…”
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Android malware concept drift using system calls: Detection, characterization and challenges
ISSN: 0957-4174, 1873-6793Published: Elsevier Ltd 15.11.2022Published in Expert systems with applications (15.11.2022)“…•Demonstrates the existence of concept drift issues in Android malware detection…”
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A novel Sybil attack detection scheme in mobile IoT based on collaborate edge computing
ISSN: 1687-1499Published: Springer Science and Business Media LLC 05.03.2023Published in EURASIP Journal on Wireless Communications and Networking (05.03.2023)Get full text
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Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions
ISSN: 2169-3536Published: Institute of Electrical and Electronics Engineers (IEEE) 01.01.2022Published in IEEE Access (01.01.2022)Get full text
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Multilayer Framework for Botnet Detection Using Machine Learning Algorithms
ISSN: 2169-3536Published: Institute of Electrical and Electronics Engineers (IEEE) 01.01.2021Published in IEEE Access (01.01.2021)Get full text
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Towards a Network-Based Framework for Android Malware Detection and Characterization
Published: IEEE 01.08.2017Published in 2017 15th Annual Conference on Privacy, Security and Trust (PST) (01.08.2017)“…Mobile malware is so pernicious and on the rise, accordingly having a fast and reliable detection system is necessary for the users…”
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Conference Proceeding -
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Android Malware: Detection, Characterization, and Mitigation
ISBN: 1339761998, 9781339761992Published: ProQuest Dissertations & Theses 01.01.2015“… These malicious apps have posed serious threats to user security and privacy. The primary goal of my research is to understand and mitigate the Android malware threats…”
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Dissertation -
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Dissecting Android Malware: Characterization and Evolution
ISBN: 9781467312448, 1467312444ISSN: 1081-6011Published: IEEE 01.05.2012Published in 2012 IEEE Symposium on Security and Privacy (01.05.2012)“…The popularity and adoption of smart phones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android…”
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Conference Proceeding -
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DroidCat: Effective Android Malware Detection and Categorization via App-Level Profiling
ISSN: 1556-6013, 1556-6021Published: New York IEEE 01.06.2019Published in IEEE transactions on information forensics and security (01.06.2019)“…Most existing Android malware detection and categorization techniques are static approaches, which suffer from evasion attacks, such as obfuscation…”
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Lightweight, Effective Detection and Characterization of Mobile Malware Families
ISSN: 0018-9340, 1557-9956Published: New York IEEE 01.11.2022Published in IEEE transactions on computers (01.11.2022)“… Despite numerous approaches and previous studies to develop solutions for detecting and preventing Android malware, the rapid continuous development of new malware variants requires a careful…”
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Uncovering the Face of Android Ransomware: Characterization and Real-Time Detection
ISSN: 1556-6013, 1556-6021Published: IEEE 01.05.2018Published in IEEE transactions on information forensics and security (01.05.2018)“…In recent years, we witnessed a drastic increase of ransomware, especially on popular mobile platforms including Android…”
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DeepFlow: Deep learning-based malware detection by mining Android application for abnormal usage of sensitive data
Published: IEEE 01.07.2017Published in 2017 IEEE Symposium on Computers and Communications (ISCC) (01.07.2017)“… Traditional malware detection approaches based on signatures or abnormal behaviors are invalid when dealing with novel malware…”
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Conference Proceeding -
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MCGDroid: An android malware classification method based on multi-feature class-call graph characterization
ISSN: 0167-4048Published: Elsevier Ltd 01.01.2026Published in Computers & security (01.01.2026)“…Malicious software (malware) attacks constitute a major category of security risks affecting the Android operating system…”
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Android Malware Detection: an Eigenspace Analysis Approach
ISSN: 2331-8422Published: Ithaca Cornell University Library, arXiv.org 27.07.2016Published in arXiv.org (27.07.2016)“… Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization…”
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Paper -
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SAC: Collaborative learning of structure and content features for Android malware detection framework
ISSN: 0925-2312Published: Elsevier B.V 07.07.2025Published in Neurocomputing (Amsterdam) (07.07.2025)“…With the rapid development of Internet of Things (IoT) technology, Android devices have increasingly become primary targets for malware attacks…”
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An Empirical Study on Android Malware Characterization by Social Network Analysis
ISSN: 0018-9529, 1558-1721Published: New York IEEE 01.03.2024Published in IEEE transactions on reliability (01.03.2024)“…Android malware detection has always been a hot research field. Prior work has validated that graph-based Android malware detection methods are effective…”
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Android malware detection: An eigenspace analysis approach
Published: IEEE 01.07.2015Published in 2015 Science and Information Conference (SAI) (01.07.2015)“… Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization…”
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Conference Proceeding -
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BLADE: Robust malware detection against obfuscation in android
ISSN: 2666-2817Published: 01.09.2021Published in Forensic science international. Digital investigation (Online) (01.09.2021)Get full text
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An Android Malware Detection Approach Using Weight-Adjusted Deep Learning
Published: IEEE 01.03.2018Published in 2018 International Conference on Computing, Networking and Communications (ICNC) (01.03.2018)“…) which severely threaten the security of Android smartphones, we propose an Android malware characterization and identification approach that uses deep learning algorithm to address the urgent need for malware detection…”
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Conference Proceeding -
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Android Malware Characterization Using Metadata and Machine Learning Techniques
ISSN: 1939-0114, 1939-0122Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018Published in Security and communication networks (01.01.2018)“…) other features publicly available at Android markets are more relevant in detecting malware, such as the application developer and certificate issuer; and (3…”
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