In recent years, the increasing number of cyber-attacks has gained the development of innovative tools to quickly detect new threats. A recent approach to this problem is to analyze the content of Social Networks to discover the rising of new malicious software. Twitter is a popular social network which allows millions of users to share their opinions on what happens all over the world. The subscribers can insert messages, called tweet, that are usually related to international news. In this work, we present a system for real-time malware alerting using a set of tweets captured through the Twitter API’s, and analyzed by means of a Bayes naïve classifier. Then, groups of tweets discussing the same topic, e.g, a new malware infection, are summarized in order to produce an alert. Tests have been performed to evaluate the performance of the system and results show the effectiveness of our implementation.

Concone, F., De Paola, A., Lo Re, G., Morana, M. (2017). Twitter Analysis for Real-Time Malware Discovery. In Proceedings of the International Annual Conference of AEIT (2017). IEEE [10.23919/AEIT.2017.8240551].

Twitter Analysis for Real-Time Malware Discovery

Concone, Federico;De Paola, Alessandra;Lo Re, Giuseppe;Morana, Marco
2017-01-01

Abstract

In recent years, the increasing number of cyber-attacks has gained the development of innovative tools to quickly detect new threats. A recent approach to this problem is to analyze the content of Social Networks to discover the rising of new malicious software. Twitter is a popular social network which allows millions of users to share their opinions on what happens all over the world. The subscribers can insert messages, called tweet, that are usually related to international news. In this work, we present a system for real-time malware alerting using a set of tweets captured through the Twitter API’s, and analyzed by means of a Bayes naïve classifier. Then, groups of tweets discussing the same topic, e.g, a new malware infection, are summarized in order to produce an alert. Tests have been performed to evaluate the performance of the system and results show the effectiveness of our implementation.
2017
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
978-8-8872-3737-5
Concone, F., De Paola, A., Lo Re, G., Morana, M. (2017). Twitter Analysis for Real-Time Malware Discovery. In Proceedings of the International Annual Conference of AEIT (2017). IEEE [10.23919/AEIT.2017.8240551].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/250720
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