Results 41 to 50 of about 2,905 (223)

Applying Bayesian probability for Android malware detection using permission features [PDF]

, 2021
he tremendous rise of mobile technology has boosted malware and has raised the threat of malware. The proliferation of malware has given a great concern among mobile users.
Mohd Nizam, Mohmad Kahar, Mohd Faizal, Ab Razak, Sharfah Ratibah, Tuan Mat, Juliza, Mohamad Arif, Azlee, Zabidi   +4 more
core   +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

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

Comment on "AndrODet: An adaptive Android obfuscation detector" [PDF]

, 2020
We have identified a methodological problem in the empirical evaluation of the string encryption detection capabilities of the AndrODet system described by Mirzaei et al. in the recent paper "AndrODet: An adaptive Android obfuscation detector".
Mohammadinodooshan, Alireza,, Shahmehri, Nahid,, Kargén, Ulf,   +2 more
core  

Towards explainable CNNs for android malware detection [PDF]

, 2021
A challenge for implementing deep learning research in the real-world is the availability of techniques that explain predictions of a model, particularly in light of potential legal requirements to give an account of algorithmic outcomes for certain use ...
Millar, Stuart, O'Kane, Philip, McLaughlin, Niall; id_orcid, Kinkead, Martin   +3 more
core   +1 more source

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

Continuous Learning for Android Malware Detection

CoRR, 2023
Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and ineffective, due to the evolution of malware apps and benign apps. Our research finds that, after training an Android malware classifier on one year's worth of data, the F1 ...
Yizheng Chen 0001, Zhoujie Ding, David A. Wagner 0001   +2 more
openaire   +3 more sources

Android Malware Detection: an Eigenspace Analysis Approach [PDF]

, 2015
The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and
Muttik, Igor, Igor Muttik, Yerima, Suleiman, Suleiman Y. Yerima, Yerima, Suleiman Y., Sezer, Sakir, Sakir Sezer, Muttik, I.   +7 more
core   +1 more source

Explainable AI for Android Malware Detection [PDF]

, 2023
Android malware detection based on machine learning (ML) is widely used by the mobile device security community. Machine learning models offer benefits in terms of detection accuracy and efficiency, but it is often difficult to understand how such models
Kulkarni, Maithili
core   +2 more sources

Malware Detection In Android Using Machine Learning [PDF]

, 2023
In an era that is increasingly fast with advanced technology, smartphones are a priority and a necessity for everyone. These gadgets are developing every day towards more advanced and appropriate ways of use.
Muhammad Hazriq Akmal, Zairol
core