Intrusion Detection Systems (IDSs) are vital in detecting network attacks and ensuring the confidentiality and integrity of network resources. Currently, industry-standard IDSs primarily rely on rule-based or anomaly-detection techniques. However, existing detection techniques often generate false positives and negatives, also known as the alert fatigue problem. This influx of incorrect events diminishes the IDS’s efficiency by overburdening security analysts. In this paper, we present ACVS, an innovative automated alert cross-verification system that leverages Graph Neural Networks for identifying misclassifications in security events. Initially, ACVS generates event graphs using attributes like IP addresses and timestamps from sequences of security events and then employs correlation analysis on these events, utilizing alert information to verify misclassifications. Finally, the system uses Graph Neural Networks to classify and correct these security events automatically. We conduct evaluations for ACVS on a substantial real-world dataset comprising over 5 million security events, which are categorized into 5 distinct groups. The results reveal that ACVS markedly enhances the accuracy of intrusion detection systems and substantially reduces the need for manual analysis.

Existing Deep Learning (DL)-based network Intrusion Detection System (IDS) is able to characterize sequence semantics of traffic and discover malicious behaviors. Yet DL models are often nonlinear and highly non-convex functions that are difficult for in-network deployment. In this paper, we present RIDS, a hardware-friendly Recurrent Neural Network (RNN) model that is co-designed with programmable switches. As its core, RIDS is powered by two tightly-coupled components: (i) rLearner, the RNN learning module with in-network deployability as the first-class requirement; and (ii) rEnforcer, the concrete pipeline design to realize rLearner-generated models inside the network dataplane. We implement a prototype of RIDS and evaluate it on our physical testbed. The experiments show that RIDS could satisfy both detection performance and high-speed bandwidth adaptation simultaneously, when none of the other existing approaches could do so. Inspiringly, RIDS realizes remarkable intrusion/malware detection effect (e.g., ~99% F1 score) and model deployment (e.g., 100 Gbps per port), while only imposing nanoseconds of latency.

To detect unknown attack traffic, anomaly-based network intrusion detection systems (NIDSs) are widely used in Internet infrastructure. However, the security communities realize some limitations when they put most existing proposals into practice. The challenges are mainly concerned with (i) fine-grained emerging attack detection and (ii) incremental updates/adaptations. To tackle these problems, we propose to decouple the need for model capabilities by transforming known/new class identification issues into multiple independent one-class learning tasks. Based on the above core ideas, we develop Trident, a universal framework for fine-grained unknown encrypted traffic detection. It consists of three main modules, i.e., tSieve, tScissors, and tMagnifier are used for profiling traffic, determining outlier thresholds, and clustering respectively, each of which supports custom configuration. Using four popular datasets of network traces, we show that Trident significantly outperforms 16 state-of-the-art (SOTA) methods. Furthermore, a series of experiments (concept drift, overhead/parameter evaluation) demonstrate the stability, scalability, and practicality of Trident.

Defending against Distributed Denial of Service (DDoS) attacks is a fundamental problem in the Internet. Over the past few decades, the research and industry communities have proposed a variety of solutions, from adding incremental capabilities to the existing Internet routing stack, to clean-slate future Internet architectures, and to widely deployed commercial DDoS prevention services. Yet a recent interview with over 100 security practitioners in multiple sectors reveals that existing solutions arestill insufficient against, due to either unenforceable protocol deployment or non-comprehensive traffic filters. This seemingly endless arms race with attackers probably means that we need a fundamental paradigm shift. In this paper, we propose a new DDoS prevention paradigm namedpreference-driven and in-network enforced traffic shaping, aiming to explore the novel DDoS prevention norms that focus on delivering victim-preferred traffic rather than consistently chasing after the DDoS attacks. Towards this end, we propose DFNet, a novel DDoS prevention system that provides reliable delivery of victim-preferred trafficwithoutfull knowledge of DDoS attacks. At a very high level, the core innovative design of DFNet embraces the advances in Machine Learning (ML) and new network dataplane primitives, byencodingthe victim's traffic preference (in the form of complex ML models) into dataplane packet scheduling algorithms such that the victim-preferred traffic is forwarded with priority at line-speed, regardless of the attacker strategy. We implement a prototype of DFNet in 11,560 lines of code, and extensively evaluate it on our testbed. The results show thata single instanceof DFNet can forward 99.93% of victim-desired traffic when facing previously unseen attacks, while imposing less than 0.1% forwarding overhead on a dataplane with 80 Gbps upstream links and a 40 Gbps bottleneck.

Image watermark is a technique widely used for copyright protection. Recent studies show that the image watermark can be added to the clear image as a kind of noise to realize fooling deep learning models. However, previous adversarial example (AE) detection schemes tend to be ineffective since the watermark logo differs from typical noise perturbations. In this poster, we propose Themis, a novel AE detection method against watermark perturbation. Different from prior methods, Themis neither modifies the protected classifier nor requires knowledge of the process for generating AEs. Specifically, Themis leverages usable information theory to calculate the pointwise score, thereby discovering those instances that may be watermark AEs. The empirical evaluations involving 5 different logo watermark perturbations demonstrate the proposed scheme can efficiently detect AEs, and significantly (over 15% accuracy) outperforms five state-of-the-art (SOTA) detection methods. The visualization results display our detection metric is more distinguishable between AEs and non-AEs. Meanwhile, Themis realizes a larger Area Under Curve (AUC) in a threshold-resilient manner, while only introducing ∼0.04s overhead.

Existing Deep Learning (DL)-based network Intrusion Detection System (IDS) is able to characterize sequence semantics of traffic and discover malicious behaviors. Yet DL models are often nonlinear and highly non-convex functions that are difficult for in-network real-time deployment, i.e., existing DL solutions are essentially offline analysis. In this paper, we present RIDS, a hardware-friendly Recurrent Neural Network (RNN) model that is co-designed with programmable switches. As its core, RIDS is powered by two tightly-coupled components: (i) rLearner, the RNN learning module with in-network deployability as the first-class requirement; and (ii) rEnforcer, the concrete pipeline design to realize rLearner-generated models inside the network dataplane. We implement a prototype of RIDS and evaluate it on our physical testbed. The experiments show that RIDS could satisfy both detection performance and high-speed bandwidth adaptation simultaneously, when none of the other existing approaches could do so. Inspiringly, RIDS realizes remarkable intrusion/malware detection effect (e.g., ∽99% F1 score) and model deployment (e.g., 100 Gbps per port), while only imposing nanoseconds of latency.