ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection

The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications. Although applications are required to undergo malware screening before being published on officia...

A Multi-Objective AutoML-Based Efficient Intrusion Detection System for EV Charging Networks

Electric Vehicle Charging Systems EVCSs are increasingly connected with Internet of Things IoT devices, which improves charging intelligence but also expands their exposure to cyber-attacks. Intrusion Detection Systems IDSs are essential for securing EV charging networks; however, conventional...

hexora v0.3.1

hexora Hexora is a static analysis tool designed to detect malicious and harmful patterns in Python code. It combines a rule-based static analysis engine with a machine-learned model that scores entire source files to classify them as malicious or benign. It can be used to: Audit project...

On the Performance of Malware Detection Classifiers Using Hardware Performance Counters

Malware detection using Hardware Performance Counters HPC has emerged as a promising solution to improve the security of computing systems as a complement to antivirus software. Hardware-based malware detectors HMD use Machine Learning ML classifiers to detect malicious application patterns. 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...

Benign on Label, Malicious by Design: Clean-Label Dormant-To-Activated Backdoor Via Machine Unlearning with Removable Camouflage

Existing backdoor attacks often become effective immediately after backdoor implantation and may therefore be exposed before exploitation. Machine unlearning activated dormant backdoors mitigate such behavioral exposure by remaining inactive after training and becoming effective only after select...

BIT-KIBANA-2026-63261 Uncontrolled Resource Consumption in Kibana Leading to Denial of Service

Uncontrolled Resource Consumption CWE-400 in Kibana can lead to denial of service via Excessive Allocation CAPEC-130. A low-privileged authenticated user can send a specially crafted request to a Kibana machine learning feature, causing the server to exhaust available memory and become unavailabl...

BIT-KIBANA-2026-63145 Incorrect Authorization in Kibana Leading to Machine Learning Audit Log Integrity Compromise

Incorrect Authorization CWE-863 in Kibana can lead to integrity compromise of Machine Learning audit and notification records via Accessing Functionality Not Properly Constrained by ACLs CAPEC-1. A vulnerability exists in Kibana's Machine Learning functionality where a Machine Learning management...

BIT-ELK-2026-63261 Uncontrolled Resource Consumption in Kibana Leading to Denial of Service

Uncontrolled Resource Consumption CWE-400 in Kibana can lead to denial of service via Excessive Allocation CAPEC-130. A low-privileged authenticated user can send a specially crafted request to a Kibana machine learning feature, causing the server to exhaust available memory and become unavailabl...

BIT-ELK-2026-63145 Incorrect Authorization in Kibana Leading to Machine Learning Audit Log Integrity Compromise

Incorrect Authorization CWE-863 in Kibana can lead to integrity compromise of Machine Learning audit and notification records via Accessing Functionality Not Properly Constrained by ACLs CAPEC-1. A vulnerability exists in Kibana's Machine Learning functionality where a Machine Learning management...

CVE-2024-14041

In Bouncy Castle for Java from 1.73 to before 1.78, three ML-KEM CRYSTALS-Kyber routines divided secret-derived polynomial coefficients by the modulus q: Poly.toMsg, which decodes the decrypted message, and the ciphertext compression routines Poly.compressPoly and PolyVec.compressPolyVec. An...

CVE-2026-17513

A flaw was found in whisper.cpp, a library for machine learning. A local attacker can exploit a vulnerability in the ggmlftypetoggmltype function by manipulating the ftype argument. This manipulation leads to a reachable assertion, which can cause the application to terminate, resulting in a Deni...

Experimental Side Channel Analysis of Protocol Stages in Quantum Identity Authentication

Quantum networks can enable distributed computing and sensing. To realize these capabilities securely, quantum identity authentication is essential. Without authentication at the quantum layer, malicious repeaters may retain entanglement instead of performing swapping, enabling man-in-the-middle...

TroPUF: Evaluating Hardware Trojan Insertion in Delay-Based Physical Unclonable Functions

Delay-based Physical Unclonable Functions PUFs are commonly used for device authentication and key generation due to the fact that they rely on manufacturing induced delay variations. However, these same variations make PUFs inherently non-deterministic, which can allow malicious logic to blend i...

AIDRS-Hybrid-Intrusion-Detection

AIDRS — AI-Powered Intrusion Detection & Response System A...

Incorrect Authorization

Overview kibana is an open source Apache Licensed, browser-based analytics and search dashboard for Elasticsearch. Affected versions of this package are vulnerable to Incorrect Authorization in the Machine Learning management endpoint due to insufficient authorization checks. An attacker can...

Allocation of Resources Without Limits or Throttling

Overview kibana is an open source Apache Licensed, browser-based analytics and search dashboard for Elasticsearch. Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling via the machine learning feature. An attacker can cause the server to exhaust...

EUVD-2026-47589

Uncontrolled Resource Consumption CWE-400 in Kibana can lead to denial of service via Excessive Allocation CAPEC-130. A low-privileged authenticated user can send a specially crafted request to a Kibana machine learning feature, causing the server to exhaust available memory and become unavailabl...

EUVD-2026-47584

Incorrect Authorization CWE-863 in Kibana can lead to integrity compromise of Machine Learning audit and notification records via Accessing Functionality Not Properly Constrained by ACLs CAPEC-1. A vulnerability exists in Kibana's Machine Learning functionality where a Machine Learning management...

Enhancing Attack Detection Capabilities in BACnet/IP Networks Using Machine-Learning Models

Building Automation Systems BAS manage critical building functions using protocols such as BACnet/IP, yet defenders have limited tooling and few labeled datasets for detecting BACnet-specific attacks. This work addresses these gaps through three contributions. First, CISA's Zeek BACnet parser is...