Abstract-Tor is vulnerable to network-level adversaries who can observe both ends of the communication to deanonymize users. Recent work has shown that Tor is susceptible to the previously unknown active BGP routing attacks, called RAPTOR attacks, which expose Tor users to more network-level adversaries. In this paper, we aim to mitigate and detect such active routing attacks against Tor. First, we present a new measurement study on the resilience of the Tor network to active BGP prefix attacks. We show that ASes with high Tor bandwidth can be less resilient to attacks than other ASes. Second, we present a new Tor guard relay selection algorithm that incorporates resilience of relays into consideration to proactively mitigate such attacks. We show that the algorithm successfully improves the security for Tor clients by up to 36% on average (up to 166% for certain clients). Finally, we build a live BGP monitoring system that can detect routing anomalies on the Tor network in real time by performing an AS origin check and novel detection analytics. Our monitoring system successfully detects simulated attacks that are modeled after multiple known attack types as well as a real-world hijack attack (performed by us), while having low false positive rates.

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Recent work has shown that Tor is vulnerable to attacks that manipulate inter-domain routing to compromise user privacy. Proposed solutions such as Counter-RAPTOR [29] attempt to ameliorate this issue by favoring Tor entry relays that have high resilience to these attacks. However, because these defenses bias Tor path selection on the identity of the client, they invariably leak probabilistic information about client identities. In this work, we make the following contributions. First, we identify a novel means to quantify privacy leakage in guard selection algorithms using the metric of Max-Divergence. Max-Divergence ensures that probabilistic privacy loss is within strict bounds while also providing composability over time. Second, we utilize Max-Divergence and multiple notions of entropy to understand privacy loss in the worst-case for Counter-RAPTOR. Our worst-case analysis provides a fresh perspective to the field, as prior work such as Counter-RAPTOR only analyzed average case-privacy loss. Third, we propose modifications to Counter-RAPTOR that incorporate worst-case Max-Divergence in its design. Specifically, we utilize the exponential mechanism (a mechanism for differential privacy) to guarantee a worst-case bound on Max-Divergence/privacy loss. For the quality function used in the exponential mechanism, we show that a Monte-Carlo sampling-based method for stochastic optimization can be used to improve multi-dimensional trade-offs between security, privacy, and performance. Finally, we demonstrate that compared to Counter-RAPTOR, our approach achieves an 83% decrease in Max-Divergence after one guard selection and a 245% increase in worst-case Shannon entropy after 5 guard selections. Notably, experimental evaluations using the Shadow emulator shows that our approach provides these privacy benefits with minimal impact on system performance.

Modern malware and cyber attacks depend heavily on DNS services to make their campaigns reliable and difficult to track. Monitoring network DNS activities and blocking suspicious domains have been proven an effective technique in countering such attacks. However, recent successful campaigns reveal that attackers adapt by using seemingly benign domains and public web storage services to hide malicious activity. Also, the recent support for encrypted DNS queries provides attacker easier means to hide malicious traffic from network-based DNS monitoring. We propose PDNS, an end-point DNS monitoring system based on DNS sensor deployed at each host in a network, along with a centralized backend analysis server. To detect such attacks, PDNS expands the monitored DNS activity context and examines process context which triggered that activity. Specifically, each deployed PDNS sensor matches domain name and the IP address related to the DNS query with process ID, binary signature, loaded DLLs, and code signing information of the program that initiated it. We evaluate PDNS on a DNS activity dataset collected from 126 enterprise hosts and with data from multiple malware sources. Using ML Classifiers including DNN, our results outperform most previous works with high detection accuracy: a true positive rate at 98.55% and a low false positive rate at 0.03%.

Application-layer and network-layer defenses are critical for fortifying routing attacks.