281 matches found
AlertBERT: A Noise-Robust Alert Grouping Framework for Simultaneous Cyber Attacks
Automated detection of cyber attacks is a critical capability to counteract the growing volume and sophistication of cyber attacks. However, the high numbers of security alerts issued by intrusion detection systems lead to alert fatigue among analysts working in security operations centres SOC,...
Reference-Free EM Validation Flow for Detecting Triggered Hardware Trojans
Hardware Trojans HTs threaten the trust and reliability of integrated circuits ICs, particularly when triggered HTs remain dormant during standard testing and activate only under rare conditions. Existing electromagnetic EM side-channel-based detection techniques often rely on golden references o...
CVE-2021-22997
On all 7.x and 6.x versions fixed in 8.0.0, BIG-IQ HA ElasticSearch service does not implement any form of authentication for the clustering transport services, and all data used by ElasticSearch for transport is unencrypted. Note: Software versions which have reached End of Software Development...
CVE-2016-10826
cPanel before 55.9999.141 allows attackers to bypass Two Factor Authentication via DNS clustering requests SEC-93...
Hesperus Is Phosphorus: Mapping Threat Actor Naming Taxonomies at Scale
This paper studies the problem of Threat Actor TA naming convention inconsistency across leading Cyber Threat Intelligence CTI vendors. The current decentralized and proprietary nomenclature creates confusion and significant obstacles for researchers, including difficulties in integrating and...
Clustering Malware at Scale: A First Full-Benchmark Study
Recent years have shown that malware attacks still happen with high frequency. Malware experts seek to categorize and classify incoming samples to confirm their trustworthiness or prove their maliciousness. One of the ways in which groups of malware samples can be identified is through malware...
Exploring AI in Steganography and Steganalysis: Trends, Clusters, and Sustainable Development Potential
Steganography and steganalysis are strongly related subjects of information security. Over the past decade, many powerful and efficient artificial intelligence AI - driven techniques have been designed and presented during research into steganography as well as steganalysis. This study presents a...
BLADE: Behavior-Level Anomaly Detection Using Network Traffic in Web Services
With their widespread popularity, web services have become the main targets of various cyberattacks. Existing traffic anomaly detection approaches focus on flow-level attacks, yet fail to recognize behavior-level attacks, which appear benign in individual flows but reveal malicious purpose using...
Smartphone User Fingerprinting on Wireless Traffic
Due to the openness of the wireless medium, smartphone users are susceptible to user privacy attacks, where user privacy information is inferred from encrypted Wi-Fi wireless traffic. Existing attacks are limited to recognizing mobile apps and their actions and cannot infer the smartphone user...
CVE-2025-59188
CVE-2025-59188 is described as an information-disclosure vulnerability in the Windows Failover Cluster that could allow an authorized local attacker to disclose sensitive information. The available connected MS advisories and vulnerability lists indicate this affects Windows Failover Cluster and ...
GPS Spoofing Attack Detection in Autonomous Vehicles Using Adaptive DBSCAN
As autonomous vehicles become an essential component of modern transportation, they are increasingly vulnerable to threats such as GPS spoofing attacks. This study presents an adaptive detection approach utilizing a dynamically tuned Density Based Spatial Clustering of Applications with Noise...
net.optionfactory.keycloak:optionfactory-keycloak-providers (>=8.1 <=8.9), org.keycloak.testframework:keycloak-test-framework-clustering (>=26.3.0 <=26.3.3) +21 more potentially affected by CVE-2025-9162 via org.keycloak:keycloak-model-storage-services (>=26.3.0 <=26.3.3)
org.keycloak:keycloak-model-storage-services MAVEN version =26.3.0, =8.1, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.0, =26.3.3 and more Source cves: CVE-2025-...
EUVD-2016-1820
Malware in sbrugna...
EUVD-2019-13988
Malware in sbrugna...
EUVD-2015-4259
Malware in sbrugna...
EUVD-2018-8942
Malware in sbrugna...
EUVD-2020-20335
Malware in sbrugna...
EUVD-2019-13748
Malware in sbrugna...
Towards Reliable and Practical LLM Security Evaluations Via Bayesian Modelling
Before adopting a new large language model LLM architecture, it is critical to understand vulnerabilities accurately. Existing evaluations can be difficult to trust, often drawing conclusions from LLMs that are not meaningfully comparable, relying on heuristic inputs or employing metrics that fai...