No abstract

Information leakages are one of the main security threats in today's Internet. As ICN is expected to become the core architecture for Future Internet, it is therefore mandatory to prevent this threat. This paper proves that some ICN configuration prevents information leakages via Data packets and shows that it is an open problem to prevent interest packets from carrying encoded crucial information in their names. Assuming that names in ICN will follow the current URL format commonly used in the Internet, we get the statistics of web URL based on extensive crawling experiments of main internet organizations. Then we propose a simple filtering technique based on these statistics for firewall to detect anomalous names in ICN. The experiment shows that our filtering technique recognizes 15% of names in our dataset as malicious. As the false positive rate is still high for this filter to be used in a real world operation, this work is an important step for detecting anomalous names and preventing information-leakage in ICN.

Many enterprises are under threat of targeted attacks aiming at data exfiltration. To launch such attacks, in recent years, attackers with their malware have exploited a covert channel that abuses the domain name system (DNS) named DNS tunneling. Although several research efforts have been made to detect DNS tunneling, the existing methods rely on features that advanced tunneling techniques can easily obfuscate by mimicking legitimate DNS clients. Such obfuscation would result in data leakage. To tackle this problem, we focused on a "trace" left by DNS tunneling that cannot be easily hidden. In the context of data exfiltration by DNS tunneling, the malware connects directly to the DNS cache server and the generated DNS tunneling queries produce cache misses with absolute certainty. In this study, we propose a DNS tunneling detection method based on the cache-property-aware features. Our experiments show that one of the proposed features can efficiently characterize the DNS tunneling traffic. Furthermore, we introduce a rule-based filter and a long short-term memory (LSTM)-based filter using this proposed feature. The rule-based filter achieves a higher rate of DNS tunneling attack detection than the LSTM one, which instead detects the attack more quickly, while both maintain a low misdetection rate.

The orchestration of counter-measures in the context of security incidents remains a challenging task for network operators. The main objective of this demonstration is to present how this orchestration is possible in the context of a virtualized NDN network. Based on an adaptation of the TOSCA topology and orchestration model, it is possible to trigger these countermeasures after the detection of NDN specific attacks. We show how the Montimage Monitoring Tool (MMT) has been adapted to detect typical Content Poisoning Attack (CPA), and how the orchestrator can trigger reactions to mitigate their impact on the network.