Smartphones have become an important part of human lives, and this led to an increase number of smartphone users. However, this also attracts hackers to develop malicious applications especially Android botnet to steal the private information and causing financial losses. Due to the fast modifications in the technologies used by malicious application (app) developers, there is an urgent need for more advanced techniques for Android botnet detection. In this paper, a new approach for Android botnet classification based on features selection and classification algorithms is proposed. The proposed approach uses the permissions requested in the Android app as features, to differentiate between the Android botnet apps and benign apps. The Information Gain algorithm is used to select the most significant permissions, then the classification algorithms Na�ve Bayes, Random Forest and J48 used to classify the Android apps as botnet or benign apps. The experimental results show that Random Forest Algorithm achieved the highest detection accuracy of 94.6% with lowest false positive rate of 0.099. � 2017 American Scientific Publishers All rights reserved.

The username/password combination is still the most widely used method albeit various user authentication techniques have been introduced. Numerous studies have been conducted to investigate the scheme and it could be summarized that despite it weaknesses, it is the most favourable scheme. Thus, to reduce the weakness, authenticating users with image or pictures (i.e. graphical password) is proposed as one possible alternative as it was claimed that pictures were easy to remember, easy to use and has considerable security. This paper presents a study carried out to investigate initial user's performance and feedback towards the use of hybrid graphical methods (i.e. combining two graphical methods) as a method of authentication. Initially, a survey was conducted to identify participants' drawing patterns as their secret using paper-based method, and then the graphical software prototype was developed and pilot tested by selected participants. Overall, the pilot test on the prototype showed positive results as participants enjoyed using it and able to register within tolerable time. � 2013 IEEE.

Nowadays mobile devices such as smartphones had widely been used. People use smartphones not limited for phone calling or sending messages but also for web browsing, social networking and online banking transaction. To certain extend, all confidential information are kept in their smartphone. As a result, smartphones became as one of the cyber-criminal main target especially through an installation of mobile botnet. Eurograbber is an example of mobile botnet that being installed via infected mobile application without victim knowledge. It will pretense as mobile banking application software and steal financial transaction information from victim's smartphone. In 2012, Eurograbber had caused a total loss of USD 47 Million accumulatively all over the world. Based on the implications posed by this botnet, this is the urge where this research comes in. This paper presents a proof of concept on how the botnet works and the ongoing research to detect and respond to the mobile botnet efficiently. Detection of botnet malicious activity is done through an analysis of Crusewind Botnet code using reverse engineering process and static analysis technique. � 2014 IEEE.

This paper presents a system development named Tweep that enables a consumer to analyze depression status using Machine Learning based on personal Twitter posts. In order for the consumer to curb mental illness, Tweep does not only analyze Twitter users’ personal depression status, but also that of the people they follow on Twitter i.e. their ‘following’. This project is the first work that practices a user-friendly interface system that analyzes depression status for public use. The system uses rule-based Vader Sentiment Analysis and two Ma-chine Learning techniques namely Naive Bayes and Convolutional Neural Net-work. The output of the system is the percentage of the positive and negative posts of the Twitter users and of their followings.