Permission based Mobile Malware Detection System using Machine Learning Techniques

Mobile technology has grown dramatically around the world. Nowadays smart mobile devices are ubiquitous, i.e. they serve multiple purposes such as personal mobile communication, data storage, multimedia and entertainment etc. They have become important part of life. Implementing secure mobile and wireless networks is crucial for enterprises operating in the Internet-based business environment. Mobile market share has grown significantly in past few years so that we need to think about mobile security. Mobile security can be compromised due to design flaws, vulnerabilities, and protocol failures in any mobile applications, viruses, spyware, malware and other threats. In this paper we will more focus on mobile malware. Many tools are available in the market to detect malware but new research trend in the mobile security is users should be aware of app before he/she installs from the app store. Hence we propose a novel approach for permission based mobile malware detection system. It is based on static analysis. It has 3 major parts in it 1) a signature database for storing analysis results of training and testing. 2) An Android client who is used by end users for making analysis requests, and 3) a central server plays important role as it communicates with both signature database and smartphone client. We can say that he is the manager of whole analysis process. It alerts user if the app is malicious or the benign based on it user can proceed whether to continue with it or not

Cyber Security for Microreactors in Advanced Energy Systems

Demand for clean and resilient energy has led to new and advancing frontiers of energy development in nuclear technology, specifically in the development of microreactors. These miniaturised modular reactors are generally \u3c 20 megawatts thermal (MWt) or 10 megawatts electric (MWe) and offer new opportunities to meet energy needs in remote locations and mobile operations. As with the slightly larger small modular reactors (\u3c 300 MWe), microreactor development must demonstrate security and safety, as well as economic competitiveness, to be seen as potential opportunities for new applications. Current research focuses on passive safety features, capital costs, reliability, semi-autonomous or autonomous control, cyber informed design, digital twins and non-proliferation. This paper focuses specifically on microreactor cyber informed design and cyber risk. An overview of microreactor technology provides a basis for examining the cyber nuclear playing field, with an emphasis on the USA. Frameworks for evaluating cyber security threats, and thereby designing for them, are reviewed. Recommendations follow with ideas for future research

MBotCS: A mobile botnet detection system based on machine learning

As the use of mobile devices spreads dramatically, hackers have started making use of mobile botnets to steal user information or perform other malicious attacks. To address this problem, in this paper we propose a mobile botnet detection system, called MBotCS. MBotCS can detect mobile device traffic indicative of the presence of a mobile botnet based on prior training using machine learning techniques. Our approach has been evaluated using real mobile device traffic captured from Android mobile devices, running normal apps and mobile botnets. In the evaluation, we investigated the use of 5 machine learning classifier algorithms and a group of machine learning box algorithms with different validation schemes. We have also evaluated the effect of our approach with respect to its effect on the overall performance and battery consumption of mobile devices

Malware Detection and Analysis

Malicious software poses a serious threat to the cybersecurity of network infrastructures and is a global pandemic in the form of computer viruses, Trojan horses, and Internet worms. Studies imply that the effects of malware are deteriorating. The main defense against malware is malware detectors. The methods that such a detector employ define its level of quality. Therefore, it is crucial that we research malware detection methods and comprehend their advantages and disadvantages. Attackers are creating malware that is polymorphic and metamorphic and has the capacity to modify their source code as they spread. Furthermore, existing defenses, which often utilize signature-based approaches and are unable to identify the previously undiscovered harmful executables, are significantly undermined by the diversity and volume of their variations. Malware families\u27 variations exhibit common behavioral characteristics that reveal their origin and function. Machine learning techniques may be used to detect and categorize novel viruses into their recognized families utilizing the behavioral patterns discovered via static or dynamic analysis. In this paper, we\u27ll talk about malware, its various forms, malware concealment strategies, and malware attack mechanisms. Additionally, many detection methods and classification models are presented in this study. The method of malware analysis is demonstrated by conducting an analysis of a malware program in a contained environment