Visible to the public Biblio

Filters: Author is Pescape, A.  [Clear All Filters]
Journal Article
Dainotti, A., King, A., Claffy, K., Papale, F., Pescape, A..  2015.  Analysis of a #x201c;/0 #x201d; Stealth Scan From a Botnet. Networking, IEEE/ACM Transactions on. 23:341-354.

Botnets are the most common vehicle of cyber-criminal activity. They are used for spamming, phishing, denial-of-service attacks, brute-force cracking, stealing private information, and cyber warfare. Botnets carry out network scans for several reasons, including searching for vulnerable machines to infect and recruit into the botnet, probing networks for enumeration or penetration, etc. We present the measurement and analysis of a horizontal scan of the entire IPv4 address space conducted by the Sality botnet in February 2011. This 12-day scan originated from approximately 3 million distinct IP addresses and used a heavily coordinated and unusually covert scanning strategy to try to discover and compromise VoIP-related (SIP server) infrastructure. We observed this event through the UCSD Network Telescope, a /8 darknet continuously receiving large amounts of unsolicited traffic, and we correlate this traffic data with other public sources of data to validate our inferences. Sality is one of the largest botnets ever identified by researchers. Its behavior represents ominous advances in the evolution of modern malware: the use of more sophisticated stealth scanning strategies by millions of coordinated bots, targeting critical voice communications infrastructure. This paper offers a detailed dissection of the botnet's scanning behavior, including general methods to correlate, visualize, and extrapolate botnet behavior across the global Internet.
 

Conference Paper
Montieri, A., Ciuonzo, D., Aceto, G., Pescape, A..  2017.  Anonymity Services Tor, I2P, JonDonym: Classifying in the Dark. 2017 29th International Teletraffic Congress (ITC 29). 1:81–89.

Traffic classification, i.e. associating network traffic to the application that generated it, is an important tool for several tasks, spanning on different fields (security, management, traffic engineering, R&D). This process is challenged by applications that preserve Internet users' privacy by encrypting the communication content, and even more by anonymity tools, additionally hiding the source, the destination, and the nature of the communication. In this paper, leveraging a public dataset released in 2017, we provide (repeatable) classification results with the aim of investigating to what degree the specific anonymity tool (and the traffic it hides) can be identified, when compared to the traffic of the other considered anonymity tools, using machine learning approaches based on the sole statistical features. To this end, four classifiers are trained and tested on the dataset: (i) Naïve Bayes, (ii) Bayesian Network, (iii) C4.5, and (iv) Random Forest. Results show that the three considered anonymity networks (Tor, I2P, JonDonym) can be easily distinguished (with an accuracy of 99.99%), telling even the specific application generating the traffic (with an accuracy of 98.00%).