netsentryx

NetSentryx PRO 🛡️ AI-Driven, High-Performance Real-Time Network Observability & Threat Detection Engine NetSentryx PRO is a next-generation, event-driven network monitoring and threat intelligence platform. Combining rule-based signatures with a PyTorch Deep Autoencoder, it provides sub-10ms packe...

class_struggle

Class Struggle Repository accompanying the "Class Struggle" article. The repository demonstrates use of an autoencoder for detecting domains similar to a known subset of phishing domains published by CERT Polska. Data Sources CERT Polska Warning List This project uses data from the CERT Polska...

A Hybrid Approach to Malware Detection: Integrating Few-Shot Model-Agnostic Meta-Learning with Autoencoders

Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are typically launched through phishing emails, malicious downloads, or exploitation of software vulnerabilities to gain system access. Once inside, the...

Cybersecurity in Edge Computing: A Trust-Aware Federated Hybrid Intrusion Detection Framework

Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Internet of Things IoT devices. While this paradigm decentralizes processes, minimizes latency, and reduces backhaul bandwidth congestion, it exponentiall...

netsentryx — Updated!

NetSentryx PRO 🛡️ AI-Driven, High-Performance Real-Time Network Observability & Threat Detection Engine NetSentryx PRO is a next-generation, event-driven network monitoring and threat intelligence platform. Combining rule-based signatures with a PyTorch Deep Autoencoder, it provides sub-10ms packe...

Cyber-Electromagnetic Anomaly Detection through Time-Series Analysis

Military operations benefit from the coordination between kinetic and non-kinetic domains. In particular, the coordination of cyber operations and electromagnetic warfare has become increasingly relevant for gaining operational advantage. This coordination is also relevant for Cyber Situational...

Identifying Agentic Automation with Behavioral Telemetry

Learn how Akamai uses Masked Autoencoder Transformer models to detect sparse behavioral telemetry from autonomous AI browser agents, such as Comet...

Benchmarking Cyberattack Detection in Electric Vehicle Charging Infrastructure with Benign User Updates

Cyberattack detection in electric vehicle charging infrastructure is complicated by legitimate post-activation revisions to requested energy and departure time. Charging manipulation attacks can exploit the same interface and variables; therefore, detecting a request change alone does not establi...

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:PYSEC-2026-3182...

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:PYSEC-2026-3122...

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:PYSEC-2026-3130...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25669 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-2023-25669 Source advisory: OSV:PYSEC-2026-3236...

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:PYSEC-2026-3106...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25674 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-2023-25674 Source advisory: OSV:PYSEC-2026-3189...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25672 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-2023-25672 Source advisory: OSV:PYSEC-2026-3143...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25670 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-2023-25670 Source advisory: OSV:PYSEC-2026-3098...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2023-25673 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-2023-25673 Source advisory: OSV:PYSEC-2026-3115...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-41910 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-2022-41910 Source advisory: OSV:PYSEC-2026-3178...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-41907 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-2022-41907 Source advisory: OSV:PYSEC-2026-3091...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-41909 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-2022-41909 Source advisory: OSV:PYSEC-2026-3238...