AI-Driven IRM: Transforming Insider Risk Management with Adaptive Scoring and LLM-Based Threat Detection

Insider threats pose a significant challenge to organizational security, often evading traditional rule-based detection systems due to their subtlety and contextual nature. This paper presents an AI-powered Insider Risk Management IRM system that integrates behavioral analytics, dynamic risk...

Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services

Due to the increasing number of tasks that are solved on remote servers, identifying and classifying traffic is an important task to reduce the load on the server. There are various methods for classifying traffic. This paper discusses machine learning models for solving this problem. However, su...

FFCBA: Feature-Based Full-Target Clean-Label Backdoor Attacks

Backdoor attacks pose a significant threat to deep neural networks, as backdoored models would misclassify poisoned samples with specific triggers into target classes while maintaining normal performance on clean samples. Among these, multi-target backdoor attacks can simultaneously target multip...

Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications

This work empirically evaluates machine learning models on two imbalanced public datasets KDDCUP99 and Credit Card Fraud 2013. The method includes data preparation, model training, and evaluation, using an 80/20 train/test split. Models tested include eXtreme Gradient Boosting XGB, Multi Layer...

Quantum Autoencoder for Multivariate Time Series Anomaly Detection

Anomaly Detection AD defines the task of identifying observations or events that deviate from typical - or normal - patterns, a critical capability in IT security for recognizing incidents such as system misconfigurations, malware infections, or cyberattacks. In enterprise environments like SAP...

Contrastive Learning for Continuous Touch-Based Authentication

Smart mobile devices have become indispensable in modern daily life, where sensitive information is frequently processed, stored, and transmitted-posing critical demands for robust security controls. Given that touchscreens are the primary medium for human-device interaction, continuous user...

CSI2Dig: Recovering Digit Content from Smartphone Loudspeakers Using Channel State Information

Eavesdropping on sounds emitted by mobile device loudspeakers can capture sensitive digital information, such as SMS verification codes, credit card numbers, and withdrawal passwords, which poses significant security risks. Existing schemes either require expensive specialized equipment, rely on...

Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders

The rapid growth in web-based services has significantly increased security risks related to user information, as web-based attacks become increasingly sophisticated and prevalent. Traditional security methods frequently struggle to detect previously unknown zero-day web attacks, putting sensitiv...

How to Enhance Downstream Adversarial Robustness (Almost) without Touching the Pre-Trained Foundation Model?

With the rise of powerful foundation models, a pre-training-fine-tuning paradigm becomes increasingly popular these days: A foundation model is pre-trained using a huge amount of data from various sources, and then the downstream users only need to fine-tune and adapt it to specific downstream...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-33976 via tensorflow (1.7.0)

tensorflow PYPI version 1.7.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - easyfacenet =1.0.4, =1.1.0, =0.1.1, =0.1.2, =0.0.0, =0.0.1 Source cves: CVE-2023-33976 Source advisory: OSV:GHSA-GJH7-XX4R-X345...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25661 via tensorflow (1.7.0)

tensorflow PYPI version 1.7.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - easyfacenet =1.0.4, =1.1.0, =0.1.1, =0.1.2, =0.0.0, =0.0.1 Source cves: CVE-2023-25661 Source advisory: OSV:GHSA-FXGC-95XX-GRVQ...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25659 via tensorflow (1.7.0)

tensorflow PYPI version 1.7.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - easyfacenet =1.0.4, =1.1.0, =0.1.1, =0.1.2, =0.0.0, =0.0.1 Source cves: CVE-2023-25659 Source advisory: OSV:GHSA-93VR-9Q9M-PJ8P...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25660 via tensorflow (1.7.0)

tensorflow PYPI version 1.7.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - easyfacenet =1.0.4, =1.1.0, =0.1.1, =0.1.2, =0.0.0, =0.0.1 Source cves: CVE-2023-25660 Source advisory: OSV:GHSA-QJQC-VQCF-5QVJ...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25662 via tensorflow (1.7.0)

tensorflow PYPI version 1.7.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - easyfacenet =1.0.4, =1.1.0, =0.1.1, =0.1.2, =0.0.0, =0.0.1 Source cves: CVE-2023-25662 Source advisory: OSV:GHSA-7JVM-XXMR-V5CW...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25663 via tensorflow (1.7.0)

tensorflow PYPI version 1.7.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - easyfacenet =1.0.4, =1.1.0, =0.1.1, =0.1.2, =0.0.0, =0.0.1 Source cves: CVE-2023-25663 Source advisory: OSV:GHSA-64JG-WJWW-7C5W...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25664 via tensorflow (1.7.0)

tensorflow PYPI version 1.7.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - easyfacenet =1.0.4, =1.1.0, =0.1.1, =0.1.2, =0.0.0, =0.0.1 Source cves: CVE-2023-25664 Source advisory: OSV:GHSA-6HG6-5C2Q-7RCR...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25665 via tensorflow (1.7.0)

tensorflow PYPI version 1.7.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - easyfacenet =1.0.4, =1.1.0, =0.1.1, =0.1.2, =0.0.0, =0.0.1 Source cves: CVE-2023-25665 Source advisory: OSV:GHSA-558H-MQ8X-7Q9G...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25666 via tensorflow (1.7.0)

tensorflow PYPI version 1.7.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - easyfacenet =1.0.4, =1.1.0, =0.1.1, =0.1.2, =0.0.0, =0.0.1 Source cves: CVE-2023-25666 Source advisory: OSV:GHSA-F637-VH3R-VFH2...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25667 via tensorflow (1.7.0)

tensorflow PYPI version 1.7.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - easyfacenet =1.0.4, =1.1.0, =0.1.1, =0.1.2, =0.0.0, =0.0.1 Source cves: CVE-2023-25667 Source advisory: OSV:GHSA-FQM2-GH8W-GR68...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25668 via tensorflow (1.7.0)

tensorflow PYPI version 1.7.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - easyfacenet =1.0.4, =1.1.0, =0.1.1, =0.1.2, =0.0.0, =0.0.1 Source cves: CVE-2023-25668 Source advisory: OSV:GHSA-GW97-FF7C-9V96...