Zobrazeno 1 - 10
of 28
pro vyhledávání: '"ElMouatez Billah Karbab"'
Autor:
ElMouatez Billah Karbab, Elias Bou-Harb, Amine Boukhtouta, Chadi Assi, Mourad Debbabi, Sadegh Torabi
Publikováno v:
IEEE Transactions on Dependable and Secure Computing. 19:402-418
The analysis of recent large-scale cyber attacks, which leveraged insecure Internet of Things (IoT) devices to perform malicious activities on the Internet, highlighted the rise of IoT-tailored malware/botnets. These malware propagate by scanning the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e2eaf4d45f0ebd06c46ce9f4cb8e363
https://doi.org/10.1109/tdsc.2020.2979183
https://doi.org/10.1109/tdsc.2020.2979183
Publikováno v:
Expert Systems with Applications. 225:120017
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a562d546ed3e9851aebddc051cbe20f7
https://doi.org/10.1016/j.eswa.2023.120017
https://doi.org/10.1016/j.eswa.2023.120017
Autor:
ElMouatez Billah Karbab, Abdelouahab Amira, Omar Nouali, Farrukh Aslam Khan, Abdelouahid Derhab
Publikováno v:
Journal of Ambient Intelligence and Humanized Computing. 12:1731-1755
The Android platform is highly targeted by malware developers, which aim to infect the maximum number of mobile devices by uploading their malicious applications to different app markets. In order to keep a healthy Android ecosystem, app-markets chec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a76397907ac136d391afe5bcad13a14
https://doi.org/10.1007/s12652-020-02243-0
https://doi.org/10.1007/s12652-020-02243-0
Publikováno v:
Digital Investigation. 28:S77-S87
In response to the volume and sophistication of malicious software or malware, security investigators rely on dynamic analysis for malware detection to thwart obfuscation and packing issues. Dynamic analysis is the process of executing binary samples
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd7c0f3dde6272f8ab5ce17b0e33c949
https://doi.org/10.1016/j.diin.2019.01.017
https://doi.org/10.1016/j.diin.2019.01.017
Publikováno v:
Android Malware Detection using Machine Learning ISBN: 9783030746636
In this chapter, we elaborate a data driven framework for detecting Android malware using automatically engineered features derived from dynamic analyses. The state-of-the-art solutions, such as Chen et al., (Stormdroid: A streaminglized machine lear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::412e4c7b547a38eedee2541cbfde1e5a
https://doi.org/10.1007/978-3-030-74664-3_5
https://doi.org/10.1007/978-3-030-74664-3_5
Publikováno v:
Android Malware Detection using Machine Learning ISBN: 9783030746636
Security practitioners can combat large-scale Android malware by decreasing the analysis window size of newly detected malware. The window starts from the first detection until signature generation by anti-malware vendors. The larger the window is, t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c01ff3a222c9b332e88fe203a4d490dc
https://doi.org/10.1007/978-3-030-74664-3_4
https://doi.org/10.1007/978-3-030-74664-3_4
Publikováno v:
Android Malware Detection using Machine Learning ISBN: 9783030746636
In this chapter, we propose ToGather, an automatic investigation framework for Android malware cyber-infrastructures. In our context, a malware cyber-infrastructure is a set of IP addresses and domain names orchestrated together to serve as a backend
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2452986638ca6cbf177410295b280d24
https://doi.org/10.1007/978-3-030-74664-3_6
https://doi.org/10.1007/978-3-030-74664-3_6
Publikováno v:
Detection of Intrusions and Malware, and Vulnerability Assessment ISBN: 9783030808242
DIMVA
DIMVA
Android malware detection is a significant problem that affects billions of users using millions of Android applications (apps) in existing markets. Thiss paper proposes PetaDroid, a framework for accurate Android malware detection and family cluster
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b047807be93a011032930d746d57a655
https://doi.org/10.1007/978-3-030-80825-9_16
https://doi.org/10.1007/978-3-030-80825-9_16
Publikováno v:
Android Malware Detection using Machine Learning ISBN: 9783030746636
In this chapter, we review and compare the state-of-the-art proposals on Android malware analysis and detection according to a novel taxonomy. Due to the large number of published contributions, we focus our review on the most prominent articles in t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33a26da5dd42ddf4f27a920415627f6b
https://doi.org/10.1007/978-3-030-74664-3_2
https://doi.org/10.1007/978-3-030-74664-3_2
Publikováno v:
Android Malware Detection using Machine Learning ISBN: 9783030746636
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7a6ddc8bea15e4ab4adf71cefc34a792
https://doi.org/10.1007/978-3-030-74664-3_1
https://doi.org/10.1007/978-3-030-74664-3_1