A Review of Cloud-Based Malware Detection System: Opportunities, Advances and Challenges

Autor: Merve Ozkan-Okay, Deepti Gupta, Omer Aslan
Rok vydání: 2021
Předmět:
Zdroj: European Journal of Engineering and Technology Research. 6:1-8
ISSN: 2736-576X
DOI: 10.24018/ejeng.2021.6.3.2372
Popis: Cloud computing has an important role in all aspects of storing information and providing services online. It brings several advantages over traditional storing and sharing schema such as an easy access, on-request storage, scalability and decreasing cost. Using its rapidly developing technologies can bring many advantages to the protection of Internet of Things (IoT), Cyber-Physical Systems (CPS) from a variety of cyber-attacks, where IoT, CPS provides facilities to humans in their daily lives. Since malicious software (malware) is increasing exponentially and there is no well-known approach to detecting malware, the usage of cloud environments to detect malware can be a promising method. A new generation of malware is using advanced obfuscation and packing techniques to escape from detection systems. This situation makes almost impossible to detect complex malware by using a traditional detection approach. The paper presents an extensive review of cloud-based malware detection approach and provides a vision to understand the benefit of cloud for protection of IoT, CPS from cyber-attack. This research explains advantages and disadvantages of cloud environments in detecting malware and also proposes a cloud-based malware detection framework, which uses a hybrid approach to detect malware.
Databáze: OpenAIRE