The rapid increase in new malware necessitates effective detection methods. While machine learning techniques have shown promise for malware detection, most research focuses on identifying malware through the content of executable files or full behavior logs collected from process start to finish. However, detecting threats like ransomware via full logs is redundant, as this malware type openly informs users of the infection.
View Article and Find Full Text PDFCybercriminals have become an imperative threat because they target the most valuable resource on earth, data. Organizations prepare against cyber attacks by creating Cyber Security Incident Response Teams (CSIRTs) that use various technologies to monitor and detect threats and to help perform forensics on machines and networks. Testing the limits of defense technologies and the skill of a CSIRT can be performed through adversary emulation performed by so-called "red teams".
View Article and Find Full Text PDFBehavioral malware detection is based on attributing malicious actions to processes. Malicious processes may try to hide by changing the behavior of other benign processes to achieve their goals. We showcase how Component Object Model (COM) and Windows Management Instrumentation (WMI) can be used to create such spoofing attacks.
View Article and Find Full Text PDFThis paper introduces a novel stability metric specifically developed for IQRF wireless mesh sensor networks, emphasizing flooding routing and data collection methodologies, particularly IQRF's Fast Response Command (FRC) technique. A key feature of this metric is its ability to ensure network resilience against disruptions by effectively utilizing redundant paths in the network. This makes the metric an indispensable tool for field engineers in both the design and deployment of wireless sensor networks.
View Article and Find Full Text PDFBackground And Aims: High-resolution esophageal manometry (HREM) is the gold standard procedure used for the diagnosis of esophageal motility disorders (EMD). Artificial intelligence (AI) might provide an efficient solution for the automatic diagnosis of EMD by improving the subjective interpretation of HREM images. The aim of our study was to develop an AI-based system, using neural networks, for the automatic diagnosis of HREM images, based on one wet swallow raw image.
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