deep-pwning

Deep-pwning is a lightweight framework for experimenting with machine learning models with the goal of evaluating their robustness against a motivated adversary. Note that deep-pwning in its current state is no where close to maturity or completion. It is meant to be experimented with, expanded...

robustness

robustness package Install via pip: pip install robustness Read the docs: https://robustness.readthedocs.io/en/latest/index.html robustness is a package we students in the MadryLab created to make training, evaluating, and exploring neural networks flexible and easy. We use it in almost all of ou...

Kaonashi

Kaonashi Project: RootedCON 2019 This repository contains several directories: Sorted Masks for hashcat Advanced Rules for hashcat Links to download the Kaonishi's Wordlists Slides used in our talk I know your p4$$w0rd and if I don't, I will guess it... at RootedCON 2019 A clone may not be...

T-Backdoor

Sample Codes for the paper: 'T-Backdoor: Exploiting Temporal Redundancy in Neuromorphic Data for Spike-preserving Backdoor Attacks on SNNs' Abstract Backdoor attacks are a serious security threat to deep neural networks DNNs and remain largely underexplored for spiking neural networks SNNs...

certified-unlearning-neural-networks-icml-2025

Certified Unlearning for Neural Networks This repository contains the implementation for the paper "Certified Unlearning for Neural Networks" ICML 2025. Methods PABI Privacy Amplification by Iteration : Gradient Clipping in the paper DP-SGD : With group privacy as described in the paper Contracti...

Security Properties of Neural Networks As Decision Problems

Certifying a deployed neural network raises decision problems that the verification literature has not classified: whether the model carries a backdoor planted in its training data, whether a fault in its stored parameters can drive it into an unsafe state, whether its output leaks a private part...

Stress-Testing Structure-Aware Calibration of Malware Graph Neural Networks under Type Shift

Post-hoc malware calibrators can condition confidence on graph structure, but their structural inputs may leave the support represented by validation data under malware-type shift. We study this risk on MalNet-Tiny by holding out each of four malware types across three seeds, freezing a graph...

SEMA-GUARD: Semantic and Graph-Based Vulnerability Detection in Assembly Code

In cases where source code is not available, such as malware analysis, firmware analysis, and embedded systems analysis, vulnerability detection in compiled programs has gained importance. Current methods are heavily reliant on syntactical regularities or higher level representations that are...

Few-Shot Learning for Network Intrusion Detection: Methods, Datasets, and Performance

Anomaly-based network intrusion detection systems NIDS are an important first line of defense. However, training NIDS for new attack types is challenging, because labeled attack data are rarely available. Few-shot learning FSL addresses this problem by learning from few samples. However, the...

Performance Analysis of Machine Learning Algorithms for Predicting Malware

Malware poses a persistent and evolving threat to modern computing systems, making accurate and timely detection a critical cybersecurity challenge. Traditional signature-based antivirus solutions often fail to identify newly emerging malware, leaving systems vulnerable until updated signatures...

Temporal Poisoning: Clean-Label Backdoors Via Event Redistribution in SNNs

Backdoor attacks on Spiking Neural Networks SNNs have primarily assumed dirty-label poisoning, in which triggered training samples are relabeled to an attacker-selected class. We study clean-label temporal poisoning, where a fixed timestamp transformation is applied only to the target-class...

Astra Linux – Vulnerability in Chromium

Integer overflow in WebNN in Google Chrome prior to 150.0.7871.47 allowed a remote attacker to obtain potentially sensitive information from process memory via a crafted HTML page. Chromium security severity: Low...

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...

HarmQ: Harmonic Backdoor Attacks against Quantum Neural Networks

Quantum Neural Networks QNNs have emerged as a promising paradigm for quantum machine learning in the Noisy Intermediate-Scale Quantum NISQ era, leveraging quantum phenomena such as superposition and entanglement to process information in exponentially large Hilbert spaces. However, QNNs inherit...

TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems

The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior. Despite growing interest in trustworthy metaverse analytics, existing datasets typically focus on user behavior, authentication, or...

Enhanced Feature Extraction for IoT Network Intrusion Detection Using GNNs and KAN

Recent advancements in the Internet of Things IoT emphasize the urgent need for advanced network security, as IoT networks feature dynamic topologies, imbalanced traffic, and complex attack patterns. Unlike general IT networks, IoT environments exhibit extreme heterogeneity and sparse topologies...

Towards Improved Anomaly Detection for Cloud Cybersecurity Via Graph Neural Networks

Detecting security threats in an organization's cloud computing environment has become necessary due to the increased reliance on cloud infrastructure. Logging of all cloud computing events enables investigation into any incidents after they are detected. Automated detection of threats using the...

Graph Neural Networks at War: Integrating Cybersecurity and Drone Intelligence in the Israeli-Iranian Conflict

Physical cyber systems have brought about new threats and challenges in detection and immediate response. This study examines how Graph Neural Networks GNNs can be used to aid cybersecurity and drone management in a physical cyber system comprising of cyber intrusions and unmanned aerial vehicles...

Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis

Most TinyML hardware accelerators focus on supporting Quantized Neural Networks QNNs to meet stringent constraints on power consumption and size. Despite this, the security aspects of quantization within TinyML hardware remain largely unexplored. Although previous studies indicate that QNNs...

On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection

Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are computationally demanding, motivating interest in lightweight alternatives suited to edge and neuromorphic deployment. Spiking Neural Networks SNNs are...