The paper benchmarks static embedding models—Word2Vec, FastText, and Doc2Vec—for detecting anomalous HTTP requests using a single‑class classification framework. It introduces HEDA, a modular pipeline that trains both embeddings and detectors solely on benign traffic in an unsupervised setting. Experiments on synthetic and real datasets show that FastText embeddings consistently yield high detection rates with controlled false positives.
arXiv:2507.12295v2 Announce Type: replace-cross Abstract: Text anomaly detection is a critical task in natural language processing (NLP), with applications spanning fraud detection, misinformation id...
arXiv:2406.13547v2 Announce Type: replace Abstract: ModSecurity is widely recognized as the standard open-source Web Application Firewall (WAF), maintained by the OWASP Foundation. It detects malicio...
arXiv:2609.36039v1 Announce Type: cross Abstract: Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing s...
arXiv:2608. 08100v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enables large language models to classify network flows and generate human-readable incident reports by retrieving semantically similar historical traffic from a vector knowledge base.
arXiv:2409. 08521v2 Announce Type: replace-cross Abstract: In cybersecurity practice, new forms of cyberattacks continuously emerge, deliberately designed to evade defense systems that rely on previously observed behaviors.
arXiv:2609.08200v1 Announce Type: new Abstract: Token-level text anomaly detection, as an emerging trend of text anomaly detection, moves beyond coarse-grained document-level detection by localizing...
The paper presents a simple detector for model extraction attacks on large language model APIs. It frames detection as a benign‑calibrated traffic‑window distribution test, embedding queries into a semantic space and using maximum mean discrepancy (MMD) to compare against historical benign traffic. Evaluated on fourteen attacker‑normal query pairs across four extraction scenarios, MMD achieves near‑perfect true‑positive rates while maintaining a 0.3% false‑positive rate, outperforming several existing baselines.
arXiv:2606. 28439v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are widely applied in Network-based Intrusion Detection System (NIDS) due to their high accuracy.
WAInjectBench introduces the first comprehensive benchmark for detecting prompt injection attacks against web agents, offering a fine‑grained categorization of threats and datasets that include malicious and benign text and image samples. The study systematically evaluates both text‑based and image‑based detection methods across multiple scenarios, revealing that detectors perform well on attacks with explicit instructions or visible perturbations but struggle with subtle or instruction‑free attacks. The authors release the datasets and code to facilitate further research in this area.
arXiv:2607. 01276v1 Announce Type: cross Abstract: Embedding models are essential components of modern Information Retrieval (IR) systems, yet they are typically hidden behind APIs.
The paper introduces Learning to Detect (LoD), a framework for identifying unseen jailbreak attacks in Large Vision‑Language Models without relying on attack data or hand‑crafted heuristics. LoD extracts layer‑wise safety representations via Multi‑modal Safety Concept Activation Vectors and compresses them into a one‑dimensional anomaly score using a Safety Pattern Auto‑Encoder. Experiments show that LoD achieves state‑of‑the‑art AUROC across diverse unseen attacks on multiple LVLMs while improving efficiency.
arXiv:2607. 15379v1 Announce Type: cross Abstract: Network anomaly detection is increasingly challenging due to the growing diversity and variability of traffic patterns, which are not always well captured by traditional statistical features.