arXiv Machine Learning

Micro-Segmentation Anomaly Detection in Zero-Trust Software-Defined Network Fabrics

arXiv:2608. 02627v1 Announce Type: cross Abstract: Zero Trust Architecture (ZTA) principles need rigorous network segmentation and ongoing verification to reduce implicit trust and lateral threat propagation.

arXiv Computer Vision

Sep 4

Beyond Small Patches: Black-Box Detection and Purification of Diverse Backdoor Triggers

The paper introduces TRIM, a black‑box defense for backdoor attacks in computer vision models. TRIM identifies and removes malicious trigger regions at inference time using region‑based segmentation, adaptive trigger discovery via inpainting and diffusion, and selective purification, without needing model internals, training data, or clean samples. Experiments on various datasets and trigger types show TRIM reduces attack success rates to as low as 1.16% while maintaining high clean accuracy.

By Ahmed Abdelnaby, Mohamed Elmahallawy

rag diffusion computer-vision multimodal

arXiv Machine Learning

Jun 4

Toward a Generalized Defense Across Sparse, Continuous, and Structured Parameter Attacks

arXiv:2606. 04317v1 Announce Type: cross Abstract: Deep neural networks are increasingly deployed across heterogeneous and partially untrusted environments, where models are distributed through cloud storage, CI/CD pipelines, containerized services, and edge execution platforms.

By Bin Duan, Zeyu Bai, Guowei Yang

efficiency safety

arXiv Machine Learning

Aug 3

Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection

arXiv:2512. 22179v3 Announce Type: replace Abstract: Detecting previously unseen attacks remains a major challenge for machine learning-based intrusion detection systems.

By Rajeeb Thapa Chhetri, Saurab Thapa, Avinash Kumar, Zhixiong Chen

llms diffusion benchmarks

arXiv AI

Jun 30

PLAA: Packet-level Adversarial Attacks in Network Traffic Detection

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.

By Jinhao You, Zan Zhou, Shujie Yang, Yi Sun, Lei Zhang, Changqiao Xu

computer-vision safety

arXiv AI

Sep 10

Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification

arXiv:2609.07403v1 Announce Type: cross Abstract: Privacy-sensitive surveillance systems could benefit from large vision-language models (VLMs), but such models typically require centralized access t...

By C\^ome-Alexis Puech, S\'ebastien Thuau, Amira Gran, Arthur Mennessier, Siba Haidar, Rachid Chelouah

llms multimodal safety

arXiv Machine Learning

Jul 31

ARES: Anomaly Recognition Model For Edge Streams

arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.

By Simone Mungari, Albert Bifet, Giuseppe Manco, Bernhard Pfahringer

rag diffusion

arXiv Machine Learning

Oct 2

Cybersecurity in Edge Computing: A Trust-Aware Federated Hybrid Intrusion Detection Framework

arXiv:2609.39584v1 Announce Type: cross Abstract: Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Intern...

By Zawad Yalmie Sazid, Robert Abbas

agents benchmarks safety

arXiv Machine Learning

Jun 25

A Hybrid CNN-LSTM Intrusion Detection Framework for Cybersecurity in Smart Renewable Energy Grids

arXiv:2606. 25200v1 Announce Type: new Abstract: The accelerated digitalization of renewable energy smart grids through IoT sensors, AMI, and SCADA systems has significantly expanded the attack surface for sophisticated cyberattacks, FDI attacks that stealthily distort state estimation and DoS/DDoS attacks that flood communication channels.

By Sajib Debnath, Remon Das

efficiency benchmarks

arXiv Machine Learning

Jun 10

Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017

arXiv:2606. 11098v1 Announce Type: cross Abstract: Recent deep learning approaches for network intrusion detection increasingly incorporate temporal architectures such as recurrent networks and Transformers, often reporting near-perfect performance on CIC-IDS2017.

By Zach Moczkodan (Royal Military College of Canada, Kingston, Canada), Hany Ragab (Royal Military College of Canada, Kingston, Canada)

llms benchmarks

arXiv AI

Sep 15

SENTINEL: A Multi-Pathway Architecture for Detecting Living-Off-the-Land APT Attacks on Windows Command Lines

arXiv:2609.14593v1 Announce Type: cross Abstract: Living-Off-the-Land (LOTL) is the dominant evasion technique of Advanced Persistent Threat (APT) actors, exploiting legitimate Windows utilities to c...

By Ahad Bin Islam Shoeb, Kamrul Hasan, Jamal Uddin Tanvin, Liang Hong, Imtiaz Ahmed, Md Arif Billah, Al Amin

benchmarks safety

arXiv Machine Learning

Jul 9

InferNet: Exploiting Aggregate GPU Profiles as Side-Channel for DNN Architecture Inference

arXiv:2304. 03388v2 Announce Type: replace Abstract: Deep Neural Networks (DNNs) have become ubiquitous for their ability to solve problems across various domains, including computer vision, natural language processing, and speech recognition.

By Raja Hasnain Anwar, Jonah O'Brien Weiss, Tiago Alves, Sandip Kundu

llms computer-vision nlp robotics multimodal safety

arXiv AI

Jun 9

XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection

arXiv:2502. 09194v1 Announce Type: cross Abstract: Generative Artificial Intelligence (AI) techniques have become integral part in advancing next generation wireless communication systems by enabling sophisticated data modeling and feature extraction for enhanced network performance.

By Osman Tugay Basaran, Falko Dressler

safety

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