Results 101 to 110 of about 4,196 (220)
IMPLEMENTASI LAYANAN DETEKSI MALWARE BERBASIS ANDROID [PDF]
, 2017 Proyek Akhir ini membuat aplikasi pendeteksi Malware android yaitu Andro Scanner. Saat ini android telah menjadi salah satu sistem operasi yang popular.
MUHAMMAD HABIBI
core
Extracting Android Applications Data for Anomaly-based Malware Detection
, 2015 In order to apply any machine learning algorithm or classifier, it is fundamentally important to first and foremost collect relevant features. This is most important in the field of dynamic analysis approach to anomaly malware detection systems. In this
Ume U.A +4 more
core
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
IEEE AccessWith an increase in the number and complexity of malware, traditional malware detection methods such as heuristic-based and signature-based ones have become less adequate, leaving user applications vulnerable.
Abdurraheem Joomye +4 more
doaj +1 more source
Countering Android Malware: a Scalable Semi-Supervised Approach for Family-Signature Generation [PDF]
, 2018 International audienceReducing the effort required by humans in countering malware is of utmost practical value. We describe a scalable, semi-supervised framework to dig into massive data sets of Android applications and identify new malware families ...
Marcelli, Andrea +11 more
core +1 more source
Monitoring Real Android Malware
, 2020 . In the most comprehensive study on Android attacks so far (undertaken by the Android Malware Genome Project), the behaviour of more than 1, 200 malwares was analysed and categorised into common, recurring groups of attacks.
Andreas Bauer, Jan-Christoph Küster
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MADRAS-NET: A deep learning approach for detecting and classifying android malware using Linknet
Measurement: SensorsMalware is an intentionally created malicious software that still poses a serious threat in cyberspace. Android malware has become one of the most significant online threats in recent years due to its increase in prevalence. Even though a lot of work has
Yi Wang, Shanshan Jia
doaj +1 more source
Emulation vs Instrumentation for Android Malware Detection
, 2019 In resource constrained devices, malware detection is typically based on offline analysis using emulation. In previous work it has been claimed that such emulation fails for a significant percentage of Android malware because well-designed malware ...
Sinha, Anukriti
core +1 more source
En la actualidad, los emprendedores de la ciudad de Ambato se encuentran en una creciente dependencia de los dispositivos móviles para llevar a cabo sus operaciones comerciales.
Chacha Chadan, Édgar Fabricio
core +1 more source
MalQwen: Fine Tuned LLM for Static Android Malware Analysis Report
IEEE AccessThe Android operating system continues to face escalating security challenges, primarily due to its open-source nature and the rapid proliferation of applications from untrusted sources.
Tegar Ganang Satrio Priambodo +7 more
doaj +1 more source