degas

Degas DGA-generated domain detection using deep learning models Running I'm currently using Conda Anaconda/Miniconda for development, but you should be able to use Pipenv or virtualenv as well using the included requirements.txt. conda: conda env create -f environment.yml conda activate degas...

deep-loglizer

Deep-loglizer Deep-loglizer is a deep learning-based log analysis toolkit for automated anomaly detection. If you use deep-loglizer in your research for publication, please kindly cite the following paper: Zhuangbin Chen, Jinyang Liu, Wenwei Gu, Yuxin Su, and Michael R. Lyu. Experience Report: De...

DeepTraffic

Deep Learning models for network traffic classification For more information please read our papers. 🎓Wei Wang's Google Scholar Homepage Wei Wang, Xuewen Zeng, Xiaozhou Ye, Yiqiang Sheng and Ming Zhu,"Malware Traffic Classification Using Convolutional Neural Networks for Representation Learning,"...

PassGAN

PassGAN This repository contains code for the PassGAN: A Deep Learning Approach for Password Guessing paper. The model from PassGAN is taken from Improved Training of Wasserstein GANs and it is assumed that the authors of PassGAN used the improvedwgantraining tensorflow implementation in their...

Jammer-Loc

Jammer-Loc This repository includes the code and the Dataset Link for the paper “Machine and Deep Learning for Indoor UWB Jammer Localization.” Usage Example Run the hyperparameter optimization process: python hpo.py \ --task classification \ --mode actual \ --framework ml \ --config config.yaml ...

awesome-latency-attacks

Awesome Deep Learning Latency Attacks & Defenses This repository is maintained as a companion resource for the survey “Deep Learning Latency Attacks and Defenses: A Cross-Domain Survey.” It indexes papers, code links, taxonomy notes, and figures on latency-oriented availability threats in deploye...

SEVulDet

SEVulDet SEVulDet is a semantics-enhanced deep learning-based framework that can accurately pinpoint vulnerability patterns by extracting, preserving, and learning more semantics. E-mail for communication: [email protected] Details of SEVulDet Recent years have seen increased attention to de...

PrivacyRaven

Note: This project is on hiatus. PrivacyRaven is a privacy testing library for deep learning systems. You can use it to determine the susceptibility of a model to different privacy attacks; evaluate privacy preserving machine learning techniques; develop novel privacy metrics and attacks; and...

deep-pwning

Deep-pwning è un framework leggero per sperimentare con modelli di machine learning, con l'obiettivo di valutarne la robustezza contro un avversario motivato. Si noti che deep-pwning nel suo stato attuale è lontano dall'essere maturo o completo. È pensato per essere sperimentato, ampliato ed este...

GCTAuto-Encoder: A Cross Modal Framework for Security Flaw Detection in IoT Networks

IoT encompasses diverse physical entities, from smart home devices to autonomous vehicles, creating a complex environment with heterogeneous security models. This heterogeneity makes IoT sub-systems vulnerable to various network attacks. Modern security systems must therefore be more robust to...

Deep Learning Latency Attacks and Defenses: A Cross-Domain Survey of Availability Threats

Adversarial machine learning has focused mainly on integrity, but availability is an increasingly consequential complement. Latency attacks also energy-latency attacks increase inference-time work, energy, or response time, causing deadline misses, throughput collapse, or resource exhaustion in...

deep-learning-plus (>=0.0.5, <=0.0.7), torchfl (>=0.1.1, <=0.1.5) potentially affected by CVE-2024-5452 via pytorch-lightning (1.5.7)

pytorch-lightning PYPI version 1.5.7 is affected by a known vulnerability. The following packages have a transitive dependency on pytorch-lightning and may be impacted: - deep-learning-plus =0.0.5, =0.1.1, =0.1.5 Source cves: CVE-2024-5452 Source advisory: OSV:PYSEC-2026-3974...

deep-learning-plus (>=0.0.5, <=0.0.7), torchfl (>=0.1.1, <=0.1.5) potentially affected by CVE-2024-5980 via pytorch-lightning (1.5.7)

pytorch-lightning PYPI version 1.5.7 is affected by a known vulnerability. The following packages have a transitive dependency on pytorch-lightning and may be impacted: - deep-learning-plus =0.0.5, =0.1.1, =0.1.5 Source cves: CVE-2024-5980 Source advisory: OSV:PYSEC-2026-3975...

auto-deep-learning (>=0.1.5.1, <=0.1.5.4), autogluon-multimodal (>=0.6.3b20230121, <=0.6.3b20230203) +50 more potentially affected by CVE-2026-9856 via transformers (4.25.1)

transformers PYPI version 4.25.1 is affected by a known vulnerability. The following packages have a transitive dependency on transformers and may be impacted: - auto-deep-learning =0.1.5.1, =0.6.3b20230121, =0.0.1, =0.0.1, =2.3.0, =0.1.3, =0.0.1rc5, =0.0.7, =0.0.1, =1.12.0rc1, =0.12.0, =0.12.1 -...

Temporal and Multimodal Deep Learning for Cyberattack Detection in LEO Satellite Systems

The growing reliance on Low-Earth Orbit LEO satellite communication systems has increased the need for intelligent methods capable of detecting cyberattacks across complex and dynamic space environments. Unlike conventional network intrusion detection, satellite systems generate heterogeneous...

Low-ASR Backdoors: Exploiting Attack Success Rate Reduction and Attacker-Defender Asymmetry

Backdoor attacks are among the most effective and stealthy attacks in deep learning. Existing attacks and defenses are largely designed and evaluated under the assumption that successful backdoors exhibit high Attack Success Rates ASRs. In this paper, we show that this assumption creates a...

Intelligent Wiretap Code Design: Exploiting Wireless Endogenous Security Via Information Theory and Deep Learning Integration

Recent advancements in wireless endogenous security have explored leveraging the inherent randomness of wireless channels to enhance communication security, providing an effective alternative to traditional encryption methods. This paper proposes a wiretap coding scheme within the semantic...

Deep Learning for Cyber Threat Detection and Mitigation in Healthcare-IoT

Cybersecurity is a fundamental requirement for protecting wearable devices used in healthcare Internet of Things H-IoT systems. Security failures in these resource-constrained systems directly compromise patient safety. Physiological data and network traffic are frequent targets of cyberattacks i...

Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection

An increase in advanced Android malware requires the use of deep learning models, which can run on Android devices. But there is a trade-off between security and energy use, as strong detection models can drain the battery of devices fast. This work tests different Multi-Layer Perceptron MLP mode...

deep-learning-plus (>=0.0.5, <=0.0.7), torchfl (>=0.1.1, <=0.1.5) potentially affected by CVE-2026-58659 via pytorch-lightning (1.5.7)

pytorch-lightning PYPI version 1.5.7 is affected by a known vulnerability. The following packages have a transitive dependency on pytorch-lightning and may be impacted: - deep-learning-plus =0.0.5, =0.1.1, =0.1.5 Source cves: CVE-2026-58659 Source advisory: OSV:PYSEC-2026-3967...