CVE-2022-35939

TensorFlow is an open source platform for machine learning. The ScatterNd function takes an input argument that determines the indices of of the output tensor. An input index greater than the output tensor or less than zero will either write content at the wrong index or trigger a crash. We have...

CVE-2022-35938

TensorFlow is an open source platform for machine learning. The GatherNd function takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered. This issue has been...

Unified Framework for Qualifying Security Boundary of PUFs against Machine Learning Attacks

Physical Unclonable Functions PUFs serve as lightweight, hardware-intrinsic entropy sources widely deployed in IoT security applications. However, delay-based PUFs are vulnerable to Machine Learning Attacks MLAs, undermining their assumed unclonability. There are no valid metrics for evaluating P...

How Cisco Talos powers the solutions protecting your organization

Cisco Talos is Cisco's threat intelligence and security research organization that powers Cisco's product portfolio with that intelligence. While we are well known for the security research in our blog, vulnerability discoveries, and our open-source software, you may not be aware of exactly how o...

Integrating Multi-Agent Simulation, Behavioral Forensics, and Trust-Aware Machine Learning for Adaptive Insider Threat Detection

We present a hybrid framework for adaptive insider-threat detection that tightly integrates multi-agent simulation MAS, layered Security Information and Event Management SIEM correlation, behavioral and communication forensics, trust-aware machine learning, and Theory-of-Mind ToM reasoning...

Threat Detection in Social Media Networks Using Machine Learning Based Network Analysis

The accelerated development of social media websites has posed intricate security issues in cyberspace, where these sites have increasingly become victims of criminal activities including attempts to intrude into them, abnormal traffic patterns, and organized attacks. The conventional rule-based...

AI-Powered Hybrid Intrusion Detection Framework for Cloud Security Using Novel Metaheuristic Optimization

Cybersecurity poses considerable problems to Cloud Computing CC, especially regarding Intrusion Detection Systems IDSs, facing difficulties with skewed datasets and suboptimal classification model performance. This study presents the Hybrid Intrusion Detection System HyIDS, an innovative IDS that...

PT-2026-1142

Name of the Vulnerable Software and Affected Versions Cloudflare affected versions not specified Description A buffer overflow exists in a simulated API. The issue is identified with a hypothetical identifier. The risk assessment is medium overall, and mitigation is suggested with patches. The...

Towards Eco Friendly Cybersecurity: Machine Learning Based Anomaly Detection with Carbon and Energy Metrics

The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection framework that unifies machine learning based network...

Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems

Coordinated stealth attacks are a serious cybersecurity threat to distributed generation systems because they modify control and measurement signals while remaining close to normal behavior, making them difficult to detect using standard intrusion detection methods. This study investigates quantu...

cyber-attack-detection-main

🔥 Smart Firewall with Machine Learning WAF + ML Đồ án d...

Application-Specific Power Side-Channel Attacks and Countermeasures: A Survey

Side-channel attacks try to extract secret information from a system by analyzing different side-channel signatures, such as power consumption, electromagnetic emanation, thermal dissipation, acoustics, time, etc. Power-based side-channel attack is one of the most prominent side-channel attacks i...

Machine Learning Power Side-Channel Attack on SNOW-V

This paper demonstrates a power analysis-based Side-Channel Analysis SCA attack on the SNOW-V encryption algorithm, which is a 5G mobile communication security standard candidate. Implemented on an STM32 microcontroller, power traces captured with a ChipWhisperer board were analyzed, with Test...

Evasion-Resilient Detection of DNS-Over-HTTPS Data Exfiltration: A Practical Evaluation and Toolkit

The purpose of this project is to assess how well defenders can detect DNS-over-HTTPS DoH file exfiltration, and which evasion strategies can be used by attackers. While providing a reproducible toolkit to generate, intercept and analyze DoH exfiltration, and comparing Machine Learning vs...

Elevating Intrusion Detection and Security Fortification in Intelligent Networks through Cutting-Edge Machine Learning Paradigms

The proliferation of IoT devices and their reliance on Wi-Fi networks have introduced significant security vulnerabilities, particularly the KRACK and Kr00k attacks, which exploit weaknesses in WPA2 encryption to intercept and manipulate sensitive data. Traditional IDS using classifiers face...

Enhancing Decision-Making in Windows PE Malware Classification during Dataset Shifts with Uncertainty Estimation

Artificial intelligence techniques have achieved strong performance in classifying Windows Portable Executable PE malware, but their reliability often degrades under dataset shifts, leading to misclassifications with severe security consequences. To address this, we enhance an existing LightGBM...

Hyperparameter Tuning-Based Optimized Performance Analysis of Machine Learning Algorithms for Network Intrusion Detection

Network Intrusion Detection Systems NIDS are essential for securing networks by identifying and mitigating unauthorized activities indicative of cyberattacks. As cyber threats grow increasingly sophisticated, NIDS must evolve to detect both emerging threats and deviations from normal behavior. Th...

Detecting Prompt Injection Attacks against Application Using Classifiers

Prompt injection attacks can compromise the security and stability of critical systems, from infrastructure to large web applications. This work curates and augments a prompt injection dataset based on the HackAPrompt Playground Submissions corpus and trains several classifiers, including LSTM,...

Virtual Camera Detection: Catching Video Injection Attacks in Remote Biometric Systems

Face anti-spoofing FAS is a vital component of remote biometric authentication systems based on facial recognition, increasingly used across web-based applications. Among emerging threats, video injection attacks -- facilitated by technologies such as deepfakes and virtual camera software -- pose...

Smart Surveillance: Identifying IoT Device Behaviours Using ML-Powered Traffic Analysis

The proliferation of Internet of Things IoT devices has grown exponentially in recent years, introducing significant security challenges. Accurate identification of the types of IoT devices and their associated actions through network traffic analysis is essential to mitigate potential threats. B...