International Journal of Computer Networks and Applications

Number V-6-I-5, 2019

73 - 79
Two-Level Grid Access Control Model Based on Resource Performance and Request Priority

Authors: Sarra Namane, Nacira Ghoualmi

Abstract

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This paper summarizes all the mechanisms presented previously to efficiently represent grid computing security policies. The prime advantage of this work is to reduce the complexity by minimizing the number of security rules that require checking. This is achieved by combining the Grid Authorization Graph (GAG) and the Weighted Grid Authorization Graph (WGAG) in a two-level access control model. This model attributes the grid resources to users according to the performance that the job requires, thus avoiding performance waste. Simulations results showed the effectiveness of the proposed model in reducing the number of security rules that require checking.

80 - 91
A Review of Static Malware Detection for Android Apps Permission Based on Deep Learning

Authors: Hamida Lubuva, Qiming Huang, Godfrey Charles Msonde

Abstract

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Number of views: 195

In recent years, Android has been the main mobile operating system. The proliferation of apps powered not only by Android magnetized app developers, but also by malware developers with criminal intent to design and distribute malicious apps that can influence the ordinary activity of Android phones and tablets, steal private information and credentials, or even worse, lock the phone and ask for ransom. This study was carried out with a view of bring out clearly the review of previous researches carried regarding static analysis and pinpoint out what to be done in future. A systematic literature review which involves studying 56 research papers published in regard to static analysis. This review elaborate permissions misuse, reverse engineering and concept of static analysis in general. The outcomes of the review revealed that static analysis is widely used since it is not performed at run-time hence malicious applications cannot access to the device during analysis unlike dynamic analysis. During the review no single work done to the satisfaction curbing the existing and future evolving malwares. This study will help academicians to gain insight concerning static analysis without extensively perusing several articles to understand static malware analysis based on deep learning.