Results 151 to 160 of about 46,394 (182)

Enhanced lion swarm optimization and elliptic curve cryptography scheme for secure cluster head selection and malware detection in IoT-WSN. [PDF]

Sci Rep
D USR, R S, A B, D M, Pellakuri V.
europepmc   +1 more source

Analyzing and comparing the effectiveness of malware detection: A study of machine learning approaches. [PDF]

Heliyon
Azeem M, Khan D, Iftikhar S, Bawazeer S, Alzahrani M.   +4 more
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PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection. [PDF]

Sci Rep
Mahindru A, Arora H, Kumar A, Gupta SK, Mahajan S, Kadry S, Kim J.   +6 more
europepmc   +1 more source

NGMD: next generation malware detection in federated server with deep neural network model for autonomous networks. [PDF]

Sci Rep
Babbar H, Rani S, Boulila W.
europepmc   +1 more source

Malware variant detection

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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

Polymorphic Malware Detection

2016 6th International Conference on IT Convergence and Security (ICITCS), 2016
The most regular method of detecting malware relies on signature-based detection. Polymorphic malware pose a serious threat to modern computing. The challenge faced with this type of malware is that there is difficult to Antivirus (AV) technology to detect them. This polymorphic malware can't be detected by AV scanners because of mutated code by itself.
Nur Syuhada Selamat, Fakariah Hani Mohd Ali, Noor Ashitah Abu Othman   +2 more
openaire   +1 more source

Proactive malware detection

SPIE Proceedings, 2014
Small-to-medium sized businesses lack resources to deploy and manage high-end advanced solutions to deter sophisticated threats from well-funded adversaries, but evidence shows that these types of businesses are becoming key targets. As malicious code and network attacks become more sophisticated, classic signature-based virus and malware detection ...
Jonathan Gloster, Michael Diep, David Dredden, Matthew Mix, Mark Olsen, Brian Price, Betty Steil   +6 more
openaire   +1 more source

Malware detection using malware image and deep learning

2017 International Conference on Information and Communication Technology Convergence (ICTC), 2017
These days a lot of malware are generated. In order to deal with the new malware, we need new ways to detect malware. In this paper, we introduce a method to detect malware using deep learning. First, we generate images from benign files and malware. Second, by using deep learning, we train a model to detect malware.
Sunoh Choi, Sungwook Jang, Youngsoo Kim, Jonghyun Kim   +3 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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