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1. Distributed Detection of Adversarial Attacks for Resilient Cooperation of Multi-Robot Systems with Intermittent Communication

   Bahrami, Rayan; Jafarnejadsani, Hamidreza (October 2024, IEEE transactions on control of network systems)

   This paper concerns the consensus and formation of a network of mobile autonomous agents in adversarial settings where a group of malicious (compromised) agents are subject to deception attacks. In addition, the communication network is arbitrarily time-varying and subject to intermittent connections, possibly imposed by denial-of-service (DoS) attacks. We provide explicit bounds for network connectivity in an integral sense, enabling the characterization of the system’s resilience to specific classes of adversarial attacks. We also show that under the condition of connectivity in an integral sense uniformly in time, the system is finite-gain L stable and uniformly exponentially fast consensus and formation are achievable, provided malicious agents are detected and isolated from the network. We present a distributed and reconfigurable framework with theoretical guarantees for detecting malicious agents, allowing for the resilient cooperation of the remaining cooperative agents. Simulation studies are provided to illustrate the theoretical findings. 

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2. Flow-Level Loss Detection with Δ-Sketches

   https://doi.org/10.1145/3563647.3563653  

   Landau Feibish, Shir; Liu, Zaoxing; Ivkin, Nikita; Chen Xiaoqi; Braverman, Vladimir; Rexford, Jennifer (October 2022, Proceedings of ACM SIGCOMM Symposium on SDN Research (SOSR '22))

   Packet drops caused by congestion are a fundamental problem in network operation. Yet, it is difficult to detect where drops are happening, let alone which flows are most affected. Detecting the small-timescale drops caused by short bursts of traffic is even more challenging, and traditional monitoring techniques can easily miss them. To uncover packet drops as they occur inside a switch, the analysis must be real-time, fine-grained, and efficient. However, modern switches have distributed packet-processing pipelines that see either the arriving or departing traffic, but not the packet drops. Plus, they do not have enough memory to store per-flow state. Our MIDST system addresses these challenges through a distributed compact data structure with lightweight coordination between ingress and egress pipelines. MIDST identifies the flows experiencing loss, as well as the bursty flows responsible, across different burst durations. Our evaluation with real-world traces and TCP connections shows that MIDST uses little memory (e.g., 320KB) while providing high accuracy (95% to 98%) under varying loss rates and burst durations. We evaluate a low-rate DDoS attack and demonstrate the potential use of our measurement results for attack detection and mitigation. 

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3. Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency

   Manfredi, Victoria; Wolfe, Alicia P.; Zhang, Xiaolan; Wang, Bing (December 2022, Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 36th Conference on Neural Information Processing Systems (NeurIPS))

   Mobile wireless networks present several challenges for any learning system, due to uncertain and variable device movement, a decentralized network architecture, and constraints on network resources. In this work, we use deep reinforcement learning (DRL) to learn a scalable and generalizable forwarding strategy for such networks. We make the following contributions: i) we use hierarchical RL to design DRL packet agents rather than device agents, to capture the packet forwarding decisions that are made over time and improve training efficiency; ii) we use relational features to ensure generalizability of the learned forwarding strategy to a wide range of network dynamics and enable offline training; and iii) we incorporate both forwarding goals and network resource considerations into packet decision-making by designing a weighted DRL reward function. Our results show that our DRL agent often achieves a similar delay per packet delivered as the optimal forwarding strategy and outperforms all other strategies including state-of-the-art strategies, even on scenarios on which the DRL agent was not trained. 

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4. DeResistor: Toward Detection-Resistant Probing for Evasion of Internet Censorship

   Amich, Abderrahmen; Eshete, Birhanu; Yegneswaran, Vinod; Hoang, Nguyen Phong (August 2023, 32nd USENIX Security Symposium)

   Calandrino, Joseph A.; Troncoso, Carmela (Ed.)

   The arms race between Internet freedom advocates and censors has catalyzed the emergence of sophisticated blocking techniques and directed significant research emphasis toward the development of automated censorship measurement and evasion tools based on packet manipulation. However, we observe that the probing process of censorship middleboxes using state-of-the-art evasion tools can be easily fingerprinted by censors, necessitating detection-resilient probing techniques. We validate our hypothesis by developing a real-time detection approach that utilizes Machine Learning (ML) to detect flow-level packet-manipulation and an algorithm for IP-level detection based on Threshold Random Walk (TRW). We then take the first steps toward detection-resilient censorship evasion by presenting DeResistor, a system that facilitates detection-resilient probing for packet-manipulation-based censorship-evasion. DeResistor aims to defuse detection logic employed by censors by performing detection-guided pausing of censorship evasion attempts and interleaving them with normal user-driven network activity. We evaluate our techniques by leveraging Geneva, a state-of-the-art evasion strategy generator, and validate them against 11 simulated censors supplied by Geneva, while also testing them against real-world censors (i.e., China’s Great Firewall (GFW), India and Kazakhstan). From an adversarial perspective, our proposed real-time detection method can quickly detect clients that attempt to probe censorship middle-boxes with manipulated packets after inspecting only two probing flows. From a defense perspective, DeResistor is effective at shielding Geneva training from detection while enabling it to narrow the search space to produce less detectable traffic. Importantly, censorship evasion strategies generated using DeResistor can attain a high success rate from different vantage points against the GFW (up to 98%) and 100% in India and Kazakhstan. Finally, we discuss detection countermeasures and extensibility of our approach to other censor-probing-based tools. 

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5. Baldur: A Power-Efficient and Scalable Network Using All-Optical Switches

   https://doi.org/10.1109/HPCA47549.2020.00022  

   Jokar, Mohammad Reza; Qiu, Junyi; Chong, Frederic T.; Goddard, Lynford L.; Dallesasse, John M.; Feng, Milton; Li, Yanjing (February 2020, 2020 IEEE Symposium on High Performance Computer Architecture (HPCA))

   We present the first all-optical network, Baldur, to enable power-efficient and high-speed communications in future exascale computing systems. The essence of Baldur is its ability to perform packet routing on-the-fly in the optical domain using an emerging technology called the transistor laser (TL), which presents interesting opportunities and challenges at the system level. Optical packet switching readily eliminates many inefficiencies associated with the crossings between optical and electrical domains. However, TL gates consume high power at the current technology node, which makes TL-based buffering and optical clock recovery impractical. Consequently, we must adopt novel (bufferless and clock-less) architecture and design approaches that are substantially different from those used in current networks. At the architecture level, we support a bufferless design by turning to techniques that have fallen out of favor for current networks. Baldur uses a low-radix, multi-stage network with a simple routing algorithm that drops packets to handle congestion, and we further incorporate path multiplicity and randomness to minimize packet drops. This design also minimizes the number of TL gates needed in each switch. At the logic design level, a non-conventional, length-based data encoding scheme is used to eliminate the need for clock recovery. We thoroughly validate and evaluate Baldur using a circuit simulator and a network simulator. Our results show that Baldur achieves up to 3,000X lower average latency while consuming 3.2X-26.4X less power than various state-of-the art networks under a wide variety of traffic patterns and real workloads, for the scale of 1,024 server nodes. Baldur is also highly scalable, since its power per node stays relatively constant as we increase the network size to over 1 million server nodes, which corresponds to 14.6X-31.0X power improvements compared to state-of-the-art networks at this scale. 

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