Results 101 to 110 of about 156,914 (217)
Detecting Android Malware by Analyzing Manifest Files [PDF]
, 2013 The threat of Android malware has increased owing to the increasingpopularity of smartphones. Once an Android smartphone is infected with malware, theuser suffers from various damages, such as the theft of personal information stored in thesmartphones ...
Goto, Shigeki; Waseda University +2 more
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ANFIS-AMAL: Android Malware Threat Assessment Using Ensemble of ANFIS and GWO
Cybernetics and Information TechnologiesThe Android malware has various features and capabilities. Various malware has distinctive characteristics. Ransomware threatens financial loss and system lockdown.
Nwasra Nedal +2 more
doaj +1 more source
Challenges in Android Malware Analysis.
ERCIM News, 2016 The best protection against malware is to execute it: a security paradox.
Viet Triem Tong, Valérie +2 more
openaire +2 more sources
An agent-based model to simulate coordinated response to malware outbreak within an organisation [PDF]
, 2012 Malware is a major threat to organisations. It affects business continuity and induces risks to organisations. Current anti-malware solutions are challenged to keep the risks at bay.
Fung, C.C., Pan, J.
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International Journal of Innovative Research in Advanced EngineeringMachine 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 +5 more
openaire +2 more sources
Mağallaẗ Al-kūfaẗ Al-handasiyyaẗAndroid malware has grown steadily into a major internet threat. Despite efforts to identify and categorize malware in seemingly safe Android apps, addressing this issue is still lacking.
abdullah alsraratee, Ahmed Al-Azawei
doaj +1 more source
HTTP behavior characteristics generation and extraction approach for Android malware
Dianxin kexue, 2016 Growing of Android malware，not only seriously endangered the security of the Android market，but also brings challenges for detection.A generation and extraction approach of automatic Android malware behavioral signatures was proposed based on HTTP ...
Yaling LUO, Wenwei LI, Xin SU
doaj +2 more sources
Not so Crisp, Malware! Fuzzy Classification of Android Malware Classes
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018 Mobile devices have been spreading at great rate in recent years. Not only smartphone, but also tablets and IoT devices, are gaining an increasingly place in our everyday lives. This is the reason why attackers are developing more and more aggressive techniques with the aim to exfiltrate our sensitive and private information.
Mercaldo F., Saracino A.
openaire +4 more sources
Data Drift in Android Malware Detection
2024 International Conference on Machine Learning and Cybernetics (ICMLC)Android malware detectors are now widely implemented with machine learning algorithms, trained on large datasets of goodware and malware applications gathered at a fixed moment in time. However, as recent work showed, this domain is not stationary, causing detectors to show degrading performance over time.
Minnei, Luca +5 more
openaire +2 more sources
Evaluation of Ensemble Learning for Android Malware Family Identification
, 2020 Every Android malware sample generally belongs to a specific family that performs a similar set of actions and characteristics. Having the ability to effectively identify Android malware families can assist in addressing the damage caused by malware ...
Wylie, Jordan +3 more
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