Biblio

Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form part of a larger botnet have become quite common, thus posing a serious threat. This calls for more effective methods to detect botnets on the Android platform. Hence, in this paper, we present a deep learning approach for Android botnet detection based on Convolutional Neural Networks (CNN). Our proposed botnet detection system is implemented as a CNN-based model that is trained on 342 static app features to distinguish between botnet apps and normal apps. The trained botnet detection model was evaluated on a set of 6,802 real applications containing 1,929 botnets from the publicly available ISCX botnet dataset. The results show that our CNN-based approach had the highest overall prediction accuracy compared to other popular machine learning classifiers. Furthermore, the performance results observed from our model were better than those reported in previous studies on machine learning based Android botnet detection.

The safety of web browsers is essential to the privacy of Internet users and the security of their computing systems. In the last few years, there have been several cyber attacks geared towards compromising surfers' data and systems via exploiting browser-based vulnerabilities. Android and a number of mobile operating systems have been supporting a UI component called WebView, which can be embedded in any mobile application to render the web contents. Yet, this mini-browser component has been found to be vulnerable to various kinds of attacks. For instance, an attacker in her WebView-Embedded app can inject malicious JavaScripts into the WebView to modify the web contents or to steal user's input values. This kind of attack is particularly challenging due to the full control of attackers over the content of the loaded pages. In this paper, we are proposing and testing a server-side moving target defense technique to counter the risk of JavaScript injection attacks on mobile WebViews. The solution entails creating redundant HTML forms, randomizing their attributes and values, and asserting stealthy prompts for the user data. The solution does not dictate any changes to the browser or applications codes, neither it requires key sharing with benign clients. The results of our performance and security analysis suggest that our proposed approach protects the confidentiality and integrity of user input values with minimum overhead.

Mobile security is as critical as the PIN number on our ATM card or the lock on our front door. More than our phone itself, the information inside needs safeguarding as well. Not necessarily for scams, but just peace of mind. Android seems to have attracted the most attention from malicious code writers due to its popularity. The flexibility to freely download apps and content has fueled the explosive growth of smart phones and mobile applications but it has also introduced a new risk factor. Malware can mimic popular applications and transfer contacts, photos and documents to unknown destination servers. There is no way to disable the application stores on mobile operating systems. Fortunately for end-users, our smart phones are fundamentally open devices however they can quite easily be hacked. Enterprises now provide business applications on these devices. As a result, confidential business information resides on employee-owned device. Once an employee quits, the mobile operating system wipe-out is not an optimal solution as it will delete both business and personal data. Here we propose H-Secure application for mobile security where one can store their confidential data and files in encrypted form. The encrypted file and encryption key are stored on a web server so that unauthorized person cannot access the data. If user loses the mobile then he can login into web and can delete the file and key to stop further decryption process.