Results 41 to 50 of about 31,810 (207)

A machine learning technique for Android malicious attacks detection based on API calls [PDF]

Decision Science Letters
Android malware is widespread and it is considered as one of the most threatening attacks recently. The threat is targeting to damage access data or information or leaking them; in general, malicious software consists of viruses, worms, and ...
Mousa AL-Akhras, Saud Alghamdi, Hani Omar, Hazzaa Alshareef   +3 more
doaj   +1 more source

Metaheuristics with Deep Learning Model for Cybersecurity and Android Malware Detection and Classification

Applied Sciences, 2023
Since the development of information systems during the last decade, cybersecurity has become a critical concern for many groups, organizations, and institutions.
Ashwag Albakri, Fatimah Alhayan, Nazik Alturki, Saahirabanu Ahamed, Shermin Shamsudheen   +4 more
doaj   +1 more source

AppPoet: Large Language Model based Android malware detection via multi-view prompt engineering [PDF]

Expert systems with applications
Due to the vast array of Android applications, their multifarious functions and intricate behavioral semantics, attackers can adopt various tactics to conceal their genuine attack intentions within legitimate functions.
Wen-Xiang Zhao, Jun-Tao Wu, Z. Meng
semanticscholar   +1 more source

Improved chimp optimization algorithm (ICOA) feature selection and deep neural network framework for internet of things (IOT) based android malware detection

Measurement: Sensors, 2023
Internet of Things (IoT) is extensively implemented using Android applications thus detecting malicious Android apps is necessary. Malicious has been multiplying fast as a result of the growing usage of smartphones.
Tirumala Vasu G, Samreen Fiza, ATA. Kishore Kumar, V. Sowmya Devi, Ch Niranjan Kumar, Afreen Kubra   +5 more
doaj   +1 more source

Android Malware Detection

International Journal of Innovative Research in Advanced Engineering
Machine learning based detection system of Android malware and analysis of features which are static. The deigned system extracts non executable features such as permissions, intents, activities, and API calls from Android APK files. We analyze non-executable APK features and classify them using a Random Forest model deployed on a Flask server.
Aaditya Vikram Saravana Bhavan, Srujana Golla, Yuktha Poral, Alan S. Paul, Prasad B. Honnavalli, S. Supreetha   +5 more
openaire   +2 more sources

DroidDetector: Android Malware Characterization and Detection Using Deep Learning

Tsinghua Science and Technology, 2016
Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign
Zhenlong Yuan, Yongqiang Lu, Yibo Xue
doaj   +1 more source

DroidEnemy: Battling adversarial example attacks for Android malware detection

Digital Communications and Networks, 2022
In recent years, we have witnessed a surge in mobile devices such as smartphones, tablets, smart watches, etc., most of which are based on the Android operating system. However, because these Android-based mobile devices are becoming increasingly popular,
Neha Bala, Aemun Ahmar, Wenjia Li, Fernanda Tovar, Arpit Battu, Prachi Bambarkar   +5 more
doaj   +1 more source

LLM-MalDetect: A Large Language Model-Based Method for Android Malware Detection

IEEE Access
Android malware poses a significant cybersecurity threat, enabling unauthorized data access, financial fraud, and device compromise. Although deep learning methods are widely used for malware detection, they often struggle with stability and adaptability
Ruirui Feng, Hui Chen, Shuo Wang, Md. Monjurul Karim, Qingshan Jiang   +4 more
semanticscholar   +1 more source

Hybrid Android Malware Detection and Classification Using Deep Neural Networks

International Journal of Computational Intelligence Systems
This paper presents a deep learning-based framework for Android malware detection that addresses critical limitations in existing methods, particularly in handling obfuscation and scalability under rapid mobile app development cycles.
M. U. Rashid, Shahnawaz Qureshi, Abdullah Abid, S. S. Alqahtany, Ali Alqazzaz, Mahmood Ul Hassan, Mana Saleh al Reshan, A. Shaikh   +7 more
semanticscholar   +1 more source

Dataset Bias in Android Malware Detection

CoRR, 2022
Researchers have proposed kinds of malware detection methods to solve the explosive mobile security threats. We argue that the experiment results are inflated due to the research bias introduced by the variability of malware dataset. We explore the impact of bias in Android malware detection in three aspects, the method used to flag the ground truth ...
Yan Lin, Tianming Liu 0002, Wei Liu, Zhigaoyuan Wang, Li Li 0029, Guoai Xu, Haoyu Wang 0001   +6 more
openaire   +2 more sources