Constructing features for detecting android malicious applications: issues, taxonomy and directions
The number of applications (apps) available for smart devices or Android based IoT (Internet
of Things) has surged dramatically over the past few years. Meanwhile, the volume of ill …
of Things) has surged dramatically over the past few years. Meanwhile, the volume of ill …
The Android malware detection systems between hope and reality
The widespread use of Android-based smartphones made it an important target for
malicious applications' developers. So, a large number of frameworks have been proposed …
malicious applications' developers. So, a large number of frameworks have been proposed …
Evodeep: a new evolutionary approach for automatic deep neural networks parametrisation
Abstract Deep Neural Networks (DNN) have become a powerful, and extremely popular
mechanism, which has been widely used to solve problems of varied complexity, due to their …
mechanism, which has been widely used to solve problems of varied complexity, due to their …
CANDYMAN: Classifying Android malware families by modelling dynamic traces with Markov chains
Malware writers are usually focused on those platforms which are most used among
common users, with the aim of attacking as many devices as possible. Due to this reason …
common users, with the aim of attacking as many devices as possible. Due to this reason …
MOCDroid: multi-objective evolutionary classifier for Android malware detection
Malware threats are growing, while at the same time, concealment strategies are being used
to make them undetectable for current commercial antivirus. Android is one of the target …
to make them undetectable for current commercial antivirus. Android is one of the target …
Malware: The never-ending arms race
H Menendez - Open Journal of Cybersecurity, 2021 - endsci.net
Abstract" Antivirus is death" and probably every detection system that focuses on a single
strategy for indicators of compromise. This famous quote that Brian Dye--Symantec's senior …
strategy for indicators of compromise. This famous quote that Brian Dye--Symantec's senior …
An in-depth study of the jisut family of android ransomware
Android malware is increasing in spread and complexity. Advanced obfuscation, emulation
detection, delayed payload activation or dynamic code loading are some of the techniques …
detection, delayed payload activation or dynamic code loading are some of the techniques …
[PDF][PDF] Performance evaluation of machine learning algorithms for detection and prevention of malware attacks
Malware is any type of program that is intended to wreak havoc to the computer system and
network. Examples of malware are bot, ransomware, adware, keyloggers, viruses, trojan …
network. Examples of malware are bot, ransomware, adware, keyloggers, viruses, trojan …
Mimicking anti-viruses with machine learning and entropy profiles
HD Menéndez, JL Llorente - Entropy, 2019 - mdpi.com
The quality of anti-virus software relies on simple patterns extracted from binary files.
Although these patterns have proven to work on detecting the specifics of software, they are …
Although these patterns have proven to work on detecting the specifics of software, they are …
Structural characterization of titanium-doped Bioglass using isotopic substitution neutron diffraction
Melt quenched silicate glasses containing calcium, phosphorus and alkali metals have the
ability to promote bone regeneration and to fuse to living bone. Of these glasses 45S5 …
ability to promote bone regeneration and to fuse to living bone. Of these glasses 45S5 …