The use of mobile phones has increased because they offer nearly the same functionality as a personal computer. In addition, the number of applications available for Android-based mobile devices has increased. Google offers programmers the opportunity to upload and sell applications in the Android Market, but malware writers upload their malicious code there. In light of this background, we present here manifest analysis for malware detection in Android (MAMA), a new method that extracts several features from the Android manifest of the applications to build machine learning classifiers and detect malware.

The use of new technologies along with the popularity of social networks has given the power of anonymity to the users. The ability to create an alter-ego with no relation to the actual user, creates a situation in which no one can certify the match between a profile and a real person. This problem generates situations, repeated daily, in which users with fake accounts, or at least not related to their real identity, publish news, reviews or multimedia material trying to discredit or attack other people who may or may not be aware of the attack. These acts can have great impact on the affected victims' environment generating situations in which virtual attacks escalate into fatal consequences in real life. In this article, we present a methodology to detect and associate fake profiles on Twitter social network which are employed for defamatory activities to a real profile within the same network by analysing the content of the comments generated by both profiles. Accompanying this approach we also present a successful real life use case in which this methodology was applied to detect and stop a cyberbullying situation in a real elementary school.

The presence of mobile devices has increased in our lives offering almost the same functionality as a personal computer. Android devices have appeared lately and, since then, the number of applications available for this operating system have increased exponentially. Google already has its Android Market where applications are offered and, as happens with every popular media, is prone to misuse. A malware writer may insert a malicious application into this market without being noticed. Indeed, there are already several cases of Android malware within the Android Market. Therefore, an approach that can automatically characterise the different types of applications can be helpful for both organising the Android Market and detecting fraudulent or malicious applications. In this paper, we propose a new method for categorising Android applications through machine-learning techniques. To represent each application, our method extracts different feature sets: (i) the frequency of occurrence of the printable strings, (ii) the different permissions of the application itself and (iii) the permissions of the application extracted from the Android Market. We evaluate this approach of automatically categorisation of Android applications and show that achieves a high performance.

Malware is any type of malicious code that has the potential to harm a computer or network. The volume of malware is growing at a faster rate every year and poses a serious global security threat. Although signature-based detection is the most widespread method used in commercial antivirus programs, it consistently fails to detect new malware. Supervised machine-learning models have been used to address this issue. However, the use of supervised learning is limited because it needs a large amount of malicious code and benign software to be labelled first. In this study, the authors propose a new method that uses single-class learning to detect unknown malware families. This method is based on examining the frequencies of the appearance of opcode sequences to build a machine-learning classifier using only one set of labelled instances within a specific class of either malware or legitimate software. The authors performed an empirical study that shows that this method can reduce the effort of labelling software while maintaining high accuracy.