Results 151 to 160 of about 16,685 (199)

Ensemble machine learning for proactive android ransomware detection using network traffic. [PDF]

Sci Rep
Kirubavathi G, Padma Mayuri B, Pranathasree S, Alagappan R, Ajayan A, Ismael WM, Rehman AU.   +6 more
europepmc   +1 more source

Two stage malware detection model in internet of vehicles (IoV) using deep learning-based explainable artificial intelligence with optimization algorithms. [PDF]

Sci Rep
Alohali MA, Alahmari S, Aljebreen M, Asiri MM, Miled AB, Albouq SS, Alrusaini O, Alqazzaz A.   +7 more
europepmc   +1 more source

An accurate approach to discriminate android colluded malware from single app malware using permissions intelligence. [PDF]

Sci Rep
Mawoh RY, Wacka JBA, Tchakounte F, Fachkha C, Kolyang.   +4 more
europepmc   +1 more source

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Learning Android Malware

Proceedings of the 12th International Conference on Availability, Reliability and Security, 2017
The number of Android malware is increasing every day. Thus Android malware detection is nowadays a big challenge. One of the most tedious tasks in malware detection is the extraction of malicious behaviors. This task is usually done manually and requires a huge effort of engineering. To avoid this step, we propose in this paper to use machine learning
Khanh-Huu-The Dam, Tayssir Touili
openaire   +1 more source

Anatomizing Android Malwares

2019 26th Asia-Pacific Software Engineering Conference (APSEC), 2019
Android OS being the popular choice of majority users also faces the constant risk of breach of confidentiality, integrity and availability (CIA). Effective mitigation efforts needs to identified in order to protect and uphold the CIA triad model, within the android ecosystem.
Anand Tirkey, Ramesh Kumar Mohapatra, Lov Kumar   +2 more
openaire   +1 more source

Android application security: detecting Android malware and evaluating anti-malware software

International Journal of Internet Technology and Secured Transactions, 2020
Mobile devices have become widespread computing technology, people prefer to use than desktop devices. These connected devices and their features like an exchange of data, video calling etc. have made our lives simple but, this has also increased data security concerns. At present, Google's Android is dominating the market share of mobile devices; as a
Sangeeta Rani, Kanwalvir Singh Dhindsa
openaire   +1 more source

Android Malware Detection

2020
Smartphones and mobile tablets are rapidly becoming essential 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 intermingled with a large number of benign apps in Android markets that seriously threaten Android security.
Shymala Gowri Selvaganapathy, G. Sudha Sadasivam, Hema Priya N, Rajeshwari N, Dharani M, K. Karthik   +5 more
openaire   +1 more source

Identifying Android malware instructions

2014 IEEE Latin-America Conference on Communications (LATINCOM), 2014
Android is a very attractive platform for malware developers because it is widely used. There is a need to understand how malware works and how it can exploit a system's security architecture. To do so, this work decompiles Android malware applications to study their source code and to look for patterns, regarding instructions, method calls, and ...
Laura Victoria Morales Medina, Sandra Julieta Rueda   +1 more
openaire   +1 more source

Android Malware Detection

2022
For the past few decades, the growth in usage of mobile phones has been increasing abnormally. Recent surveys hypothesize most of the mobile phone market segment is benignly dominated by Android Operating System and this made the Android OS (Operating System) the most vulnerable Operating System; as more users are adopting to use Android OS (Operating ...
Gadde, Sayi Rosshhun, Kaushal, J. C. Pavan, Rao, T. Vijay, Srikanth, N., Gadde, Sayi Khushhal   +4 more
openaire   +1 more source

Android malware detection

Malware is one of the major issues regarding the operating framework or in thesoftware world. The android framework is also going through the same issues. We have seenother Signature based malware location strategies were utilized to recognize malware. Yet, thestrategies couldn't recognize obscure malware.
Aaditya Vikram Saravana Bhavan, Srujana Golla, Yuktha Poral, Alan S. Paul, Prasad B. Honnavalli, S. Supreetha   +5 more
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