easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-23586 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-2022-23586 Source advisory: OSV:PYSEC-2026-3096...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-23574 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-2022-23574 Source advisory: OSV:PYSEC-2026-3127...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-23587 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-2022-23587 Source advisory: OSV:PYSEC-2026-3137...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-23588 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-2022-23588 Source advisory: OSV:PYSEC-2026-3181...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-21730 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-2022-21730 Source advisory: OSV:PYSEC-2026-3247...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-21728 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-2022-21728 Source advisory: OSV:PYSEC-2026-3121...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-23585 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-2022-23585 Source advisory: OSV:PYSEC-2026-3174...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-23580 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-2022-23580 Source advisory: OSV:PYSEC-2026-3113...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-21727 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-2022-21727 Source advisory: OSV:PYSEC-2026-3159...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-23581 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-2022-23581 Source advisory: OSV:PYSEC-2026-3175...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-21726 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-2022-21726 Source advisory: OSV:PYSEC-2026-3082...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-41894 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-2022-41894 Source advisory: OSV:PYSEC-2026-936...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-35986 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-2022-35986 Source advisory: OSV:PYSEC-2026-938...

easyfacenet (>=1.0.4, <=1.0.5), keras-pandas (>=1.1.0, <=1.1.1) +3 more potentially affected by CVE-2022-35998 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-2022-35998 Source advisory: OSV:PYSEC-2026-937...

F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks

The rapid proliferation of Internet of things IoT devices has significantly expanded the cyber-attack surface, necessitating robust and privacy-preserving intrusion detection systems IDS. However, centralized learning approaches often suffer from severe performance degradation due to...

Detecting Adversarial Evasion Attacks against Autoencoder-Based Network Intrusion Detection Systems

Evasion attacks deliberately manipulate input to an ML-based system to produce an incorrect prediction while the manipulated input still appears benign. The PANDA framework has demonstrated that adversarial examples developed for the vision domain can be transferred to the network domain by...

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:PYSEC-2026-548...

Learning to Look Benign: Targeted Evasion of Malware Detectors Via API Import Injection

Machine learning-based malware detectors are widely deployed in antivirus and endpoint detection systems, yet their reliance on static features makes them vulnerable to adversarial manipulation. This paper investigates whether a malware sample can be intentionally misclassified as a specific beni...

API Security Based on Automatic OpenAPI Mapping

This paper presents Map Reduce Graph MRG, a novel unsupervised method for modeling and securing HTTP REST APIs. MRG learns API structure from real-world traffic without prior knowledge or labels, automatically generating OpenAPI-compliant documentation by reconstructing routes, methods, and...

Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks

Adversarial examples can represent a serious threat to machine learning ML algorithms. If used to manipulate the behaviour of ML-based Network Intrusion Detection Systems NIDS, they can jeopardize network security. In this work, we aim to mitigate such risks by increasing the robustness of NIDS...