Despite advances in network security, attacks targeting mission critical systems and applications remain a significant problem for network and datacenter providers. Existing telemetry platforms detect volumetric attacks at terabit scales using approximation techniques and coarse grain analysis. However, the prevalence of low and slow attacks that require very little bandwidth, makes flow-state tracking critical to overall attack mitigation. Traffic queries deployed on network switches are often limited by hardware constraints, preventing them from carrying out flow tracking features required to detect stealthy attacks. Such attacks can go undetected in the midst of high traffic volumes.
We design SmartWatch, a novel flow state tracking and flow logging system at line rate, using SmartNICs to optimize performance and simultaneously detect a number of stealthy attacks. SmartWatch leverages advances in switch based network telemetry platforms to process the bulk of the traffic and only forward suspicious traffic subsets to the SmartNIC. The programmable network switches perform coarse-grained traffic analysis while the SmartNIC conducts the finer-grained analysis which involves additional processing of the packet as a 'bump-in-the-wire'. A control loop between the SmartNIC and programmable switch tunes the queries performed in the switch to direct the most appropriate traffic subset to the SmartNIC. SmartWatch's cooperative monitoring approach yields 2.39 times better detection rate compared to existing platforms deployed on programmable switches. SmartWatch can detect covert timing channels and perform website fingerprinting more efficiently compared to standalone programmable switch solutions, relieving switch memory and control-plane processor resources. Compared to host-based approaches, SmartWatch can reduce the packet processing latency by 72.32%.
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P4DDPI: Securing P4-Programmable Data Plane Networks via DNS Deep Packet Inspection
One of the main roles of the Domain Name System (DNS) is to map domain names to IP addresses. Despite the importance of this function, DNS traffic often passes without
being analyzed, thus making the DNS a center of attacks that keep evolving and growing. Software-based mitigation approaches and dedicated state-of-the-art firewalls can become
a bottleneck and are subject to saturation attacks, especially in high-speed networks. The emerging P4-programmable data plane can implement a variety of network security mitigation
approaches at high-speed rates without disrupting legitimate traffic.
This paper describes a system that relies on programmable switches and their stateful processing capabilities to parse and analyze DNS traffic solely in the data plane, and subsequently apply security policies on domains according to the network administrator. In particular, Deep Packet Inspection (DPI) is leveraged to extract the domain name consisting of any number of labels and hence, apply filtering rules (e.g., blocking malicious domains). Evaluation results show that the proposed approach can parse more domain labels than any state-of-the-art P4-based approach. Additionally, a significant performance gain is attained
when comparing it to a traditional software firewall -pfsense-, in terms of throughput, delay, and packet loss. The resources occupied by the implemented P4 program are minimal, which allows for more security functionalities to be added.
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- Award ID(s):
- 2118311
- NSF-PAR ID:
- 10359081
- Date Published:
- Journal Name:
- NDSS Symposium 2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Intrusion detection through classifying incoming packets is a crucial functionality at the network edge, requiring accuracy, efficiency and scalability at the same time, introducing a great challenge. On the one hand, traditional table-based switch functions have limited capacity to identify complicated network attack behaviors. On the other hand, machine learning based methods providing high accuracy are widely used for packet classification, but they typically require packets to be forwarded to an extra host and therefore increase the network latency. To overcome these limitations, in this paper we propose an architecture with programmable data plane switches. We show that Binarized Neural Networks (BNNs) can be implemented as switch functions at the network edge classifying incoming packets at the line speed of the switches. To train BNNs in a scalable manner, we adopt a federated learning approach that keeps the communication overheads of training small even for scenarios involving many edge network domains. We next develop a prototype using the P4 language and perform evaluations. The results demonstrate that a multi-fold improvement in latency and communication overheads can be achieved compared to state-of the-art learning architectures.more » « less
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
