offensive-ai-compilation

Offensive AI Compilation Offensive AI를 다루는 유용한 리소스 모음입니다. 📁 목차 📁 🚫 악용 🚫 🧠 적대적 머신러닝 🧠 ⚡ 공격 ⚡ 🔒 추출 🔒 ⚠️ 한계 ⚠️ 🛡️ 방어 조치 🛡️ 🔗 유용한 링크 🔗 ⬅️ 역전 또는 추론 ⬅️ 🛡️ 방어 조치 🛡️ 🔗 유용한 링크 🔗 💉 오염 💉 🔓 백도어 🔓 🛡️ 방어 조치 🛡️ 🔗 유용한 링크 🔗 🏃‍♂️ 회피 🏃‍♂️ 🛡️ 방어 조치 🛡️ 🔗 유용한 링크 🔗 🛠️ 도구 🛠️ ART Cleverhans 🔧 사용 🔧 🕵️‍♂️ 침투 테스트 🕵️‍♂️ 🦠 악성코드...

SecML

SecML: 안전하고 설명 가능한 머신러닝을 위한 라이브러리 SecML은 머신러닝ML 알고리즘의 보안 평가 를 위한 오픈소스 Python 라이브러리입니다. 다양한 강력한 기능을 제공합니다: 광범위한 지원 ML 알고리즘. scikit-learn에서 지원하는 모든 지도 학습 알고리즘과 PyTorch 딥러닝 플랫폼을 통한 신경망NNs을 사용할 수 있습니다. 내장 공격 알고리즘. 자체 개발한 빠른 솔버 기반의 회피Evasion 및 중독Poisoning 공격. 또한, 다른 타사 적대적 머신러닝 라이브러리와의 연결을 제공합니다. 밀집/희...

Targeted Adversarial Traffic Generation : Black-Box Approach to Evade Intrusion Detection Systems in IoT Networks

The integration of machine learning ML algorithms into Internet of Things IoT applications has introduced significant advantages alongside vulnerabilities to adversarial attacks, especially within IoT-based intrusion detection systems IDS. While theoretical adversarial attacks have been extensive...

SourceBroken: A Large-Scale Analysis on the (Un)Reliability of SourceRank in the PyPI Ecosystem

SourceRank is a scoring system made of 18 metrics that assess the popularity and quality of open-source packages. Despite being used in several recent studies, none has thoroughly analyzed its reliability against evasion attacks aimed at inflating the score of malicious packages, thereby...

DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection

Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain vulnerable against adversarial examples, i.e., examples that are subtly manipulated to fool a machine learning model into making incorrect predictions...

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective

The widespread application of Deep Learning across diverse domains hinges critically on the quality and composition of training datasets. However, the common lack of disclosure regarding their usage raises significant privacy and copyright concerns. Dataset auditing techniques, which aim to...

Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation

The security of modern vehicles has become increasingly important, with the controller area network CAN bus serving as a critical communication backbone for various Electronic Control Units ECUs. The absence of robust security measures in CAN, coupled with the increasing connectivity of vehicles,...

Learning from the Good Ones: Risk Profiling-Based Defenses against Evasion Attacks on DNNs

Safety-critical applications such as healthcare and autonomous vehicles use deep neural networks DNN to make predictions and infer decisions. DNNs are susceptible to evasion attacks, where an adversary crafts a malicious data instance to trick the DNN into making wrong decisions at inference time...

Overlapping Data in Network Protocols: Bridging OS and NIDS Reassembly Gap

IPv4, IPv6, and TCP have a common mechanism allowing one to split an original data packet into several chunks. Such chunked packets may have overlapping data portions and, OS network stack implementations may reassemble these overlaps differently. A Network Intrusion Detection System NIDS that...

NIST Warns of Security and Privacy Risks from Rapid AI System Deployment

The U.S. National Institute of Standards and Technology NIST is calling attention to the privacy and security challenges that arise as a result of increased deployment of artificial intelligence AI systems in recent years. "These security and privacy challenges include the potential for adversari...

PT-2022-23866 · Chipolo · Chipolo One Bluetooth Tracker +1

Name of the Vulnerable Software and Affected Versions: Chipolo ONE Bluetooth tracker 2020 version 4.13.0 Chipolo iOS app version 4.13.0 Description: The issue concerns Incorrect Access Control, allowing access revocation evasion attacks. Once a malicious sharee obtains access credentials, Chipolo...

Introduction and Application of Model Hacking

ARCHIVED STORY Introduction and Application of Model Hacking By Steve Povolny · Febraury 19, 2020 Catherine Huang, Ph.D., and Shivangee Trivedi contributed to this blog. The term “Adversarial Machine Learning” AML is a mouthful! The term describes a research field regarding the study and design o...

Introduction and Application of Model Hacking

ARCHIVED STORY Introduction and Application of Model Hacking By Steve Povolny · Febraury 19, 2020 Catherine Huang, Ph.D., and Shivangee Trivedi contributed to this blog. The term “Adversarial Machine Learning” AML is a mouthful! The term describes a research field regarding the study and design o...