Visible to the public Biblio

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Li, Guanyu, Zhang, Menghao, Liu, Chang, Kong, Xiao, Chen, Ang, Gu, Guofei, Duan, Haixin.  2019.  NETHCF: Enabling Line-rate and Adaptive Spoofed IP Traffic Filtering. 2019 IEEE 27th International Conference on Network Protocols (ICNP). :1–12.
In this paper, we design NETHCF, a line-rate in-network system for filtering spoofed traffic. NETHCF leverages the opportunity provided by programmable switches to design a novel defense against spoofed IP traffic, and it is highly efficient and adaptive. One key challenge stems from the restrictions of the computational model and memory resources of programmable switches. We address this by decomposing the HCF system into two complementary components-one component for the data plane and another for the control plane. We also aggregate the IP-to-Hop-Count (IP2HC) mapping table for efficient memory usage, and design adaptive mechanisms to handle end-to-end routing changes, IP popularity changes, and network activity dynamics. We have built a prototype on a hardware Tofino switch, and our evaluation demonstrates that NETHCF can achieve line-rate and adaptive traffic filtering with low overheads.
Demoulin, Henri Maxime, Vaidya, Tavish, Pedisich, Isaac, DiMaiolo, Bob, Qian, Jingyu, Shah, Chirag, Zhang, Yuankai, Chen, Ang, Haeberlen, Andreas, Loo, Boon Thau et al..  2018.  DeDoS: Defusing DoS with Dispersion Oriented Software. Proceedings of the 34th Annual Computer Security Applications Conference. :712-722.

This paper presents DeDoS, a novel platform for mitigating asymmetric DoS attacks. These attacks are particularly challenging since even attackers with limited resources can exhaust the resources of well-provisioned servers. DeDoS offers a framework to deploy code in a highly modular fashion. If part of the application stack is experiencing a DoS attack, DeDoS can massively replicate only the affected component, potentially across many machines. This allows scaling of the impacted resource separately from the rest of the application stack, so that resources can be precisely added where needed to combat the attack. Our evaluation results show that DeDoS incurs reasonable overheads in normal operations, and that it significantly outperforms standard replication techniques when defending against a range of asymmetric attacks.

Chen, Ang, Wu, Yang, Haeberlen, Andreas, Zhou, Wenchao, Loo, Boon Thau.  2016.  The Good, the Bad, and the Differences: Better Network Diagnostics with Differential Provenance. Proceedings of the 2016 ACM SIGCOMM Conference. :115–128.

In this paper, we propose a new approach to diagnosing problems in complex distributed systems. Our approach is based on the insight that many of the trickiest problems are anomalies. For instance, in a network, problems often affect only a small fraction of the traffic (e.g., perhaps a certain subnet), or they only manifest infrequently. Thus, it is quite common for the operator to have “examples” of both working and non-working traffic readily available – perhaps a packet that was misrouted, and a similar packet that was routed correctly. In this case, the cause of the problem is likely to be wherever the two packets were treated differently by the network. We present the design of a debugger that can leverage this information using a novel concept that we call differential provenance. Differential provenance tracks the causal connections between network states and state changes, just like classical provenance, but it can additionally perform root-cause analysis by reasoning about the differences between two provenance trees. We have built a diagnostic tool that is based on differential provenance, and we have used our tool to debug a number of complex, realistic problems in two scenarios: software-defined networks and MapReduce jobs. Our results show that differential provenance can be maintained at relatively low cost, and that it can deliver very precise diagnostic information; in many cases, it can even identify the precise root cause of the problem.