SearcharxivSearch

arXiv subjects

Antonia Affinito

Publications and source records attributed to Antonia Affinito.

2 recordsLinked to original sources

DarkDNS: Revisiting the Value of Rapid Zone Update

Malicious actors exploit the DNS namespace to launch spam campaigns, phishing attacks, malware, and other harmful activities. Combating these threats requires visibility into domain existence, ownership and nameservice activity that the DNS protocol does not itself provide. To facilitate visibility and security-related study of the expanding gTLD namespace, ICANN introduced the Centralized Zone Data Service (CZDS) that shares daily zone file snapshots of new gTLD zones. However, a remarkably high concentration of malicious activity is associated with domains that do not live long enough make it into these daily snapshots. Using public and private sources of newly observed domains, we discover that even with the best available data there is a considerable visibility gap in detecting short-lived domains. We find that the daily snapshots miss at least 1% of newly registered and short-lived domains, which are frequently registered with likely malicious intent. In reducing this critical visibility gap using public sources of data, we demonstrate how more timely access to TLD zone changes can provide valuable data to better prevent abuse. We hope that this work sparks a discussion in the community on how to effectively and safely revive the concept of sharing Rapid Zone Updates for security research. Finally, we release a public live feed of newly registered domains, with the aim of enabling further research in abuse identification.

cs.NI

Spark-Based Anomaly Detection: the Case of Port and Net Scan

The two most spread network anomalies are port and net scan. In this work, we present and analyze the results obtained by traditional approaches for the detection of net scan and port scans. We use a simple threshold-based algorithm, working at flow-level and adapt it for the execution on Apache Spark. The use of Big Data Analytics technologies allows to significantly the execution times of the algorithm so to be used even in current, high-speed networks. The paper describes our approach and presents an experimental analysis in terms of detection performance and execution time. We use real traffic traces from MAWI archive and MAWILab anomaly detectors to compare with our results. The analysis shows that i) our traditional threshold-based algorithm is already able to achieve detection performance higher than MAWILab (in 95% of the considered cases with the best threshold value), currently considered the gold standard in the field; ii) the execution time is much shorter than the trace time, which makes it usable also in real time. Moreover, for each traffic trace we provide the research community with a new labeled dataset, validated by comparisons with MAWILab and extended with other anomalies not detected by it. We publish an updated dataset every day at our project website.

cs.NI