SOCO-CISIS-ICEUTE 

Abstract: 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 paper, 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 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.

Abstract: Twitter has become one of the most used social networks. And, as happens with every popular media, it is prone to misuse. In this context, spam in Twitter has emerged in the last years, becoming an important problem for the users. In the last years, several approaches have appeared that are able to determine whether an user is a spammer or not. However, these blacklisting systems cannot filter every spam message and a spammer may create another account and restart sending spam. In this paper, we propose a content-based approach to filter spam tweets. We have used the text in the tweet and machine learning and compression algorithms to filter those undesired tweets.

Abstract: The usage of mobile phones has increased in our lives because they offer nearly the same functionality as a personal computer. Specifically, Android is one of the most widespread mobile operating systems. Indeed, its app store is one of the most visited and the number of applications available for this platform has also increased. However, as it happens with any popular service, it is prone to misuse, and the number of malware samples has increased dramatically in the last months. Thus, we propose a new method based on anomaly detection that extracts the strings contained in application files in order to detect malware.

Abstract: In this paper, we present network anomaly detection with the use of ARFIMA model. We propose the method of estimation parameters using the Hyndman-Khandakar algorithm to estimate the polymonials parameters and the Haslett and Raftery algorithm to estimate the differencing parameters. The choice of optimal values of the model parameters is performed on the basis of information criteria representing a compromise between the consistency model and the size of its error of estimate. In the presented method, we propose to use statistical relationships between predicted and original network traffic to determine if the examined trace is normal or attacked. The efficiency of our method is verified with the use of extended set of benchmark test real traces. The reported experimental results confirm the efficiency of the presented method.

Abstract: The development of automatic methods to produce usable structured information from unstructured text sources is extremely valuable to the oil and gas industry. A structured resource would allow researches and industry professionals to write relatively simple queries to retrieve all the information regards transcriptions of any accident. Instead of the thousands of abstracts provided by querying the unstructured corpus, the queries on structured corpus would result in a few hundred well-formed results.