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In the very last years, cybersecurity attacks have increased at anunprecedented pace, becoming ever more sophisticated and costly. Their impacthas involved both private/public companies and critical infrastructures. At thesame time, due to the COVID-19 pandemic, the security perimeters of manyorganizations expanded, causing an increase of the attack surface exploitableby threat actors through malware and phishing attacks. Given these factors, itis of primary importance to monitor the security perimeter and the eventsoccurring in the monitored network, according to a tested security strategy ofdetection and response. In this paper, we present a protocol tunneling detectorprototype which inspects, in near real time, a company's network traffic usingmachine learning techniques. Indeed, tunneling attacks allow malicious actorsto maximize the time in which their activity remains undetected. The detectormonitors unencrypted network flows and extracts features to detect possibleoccurring attacks and anomalies, by combining machine learning and deeplearning. The proposed module can be embedded in any network securitymonitoring platform able to provide network flow information along with itsmetadata. The detection capabilities of the implemented prototype have beentested both on benign and malicious datasets. Results show 97.1% overallaccuracy and an F1-score equals to 95.6%.

Towards a Near-real-time Protocol Tunneling Detector based on Machine Learning Techniques