Detection equipment
Researchers at the Institute of Fluid Dynamics have successfully designed and experimentally evaluated muon trigger detectors tailored for industrial-scale applications. The study utilized an organic scintillator plate measuring 250 x 250 x 10 mm in size, configured with varying numbers of silicon photomultiplier (SiPM) devices, each with a detection area
Cyberterrorism
Researchers at Princess Nourah bint Abdulrahman University have made significant advancements in the field of artificial intelligence (AI) for cybersecurity. Their study, published in Scientific Reports, proposes an Enhanced Intrusion Detection in Cybersecurity through Dimensionality Reduction and Explainable Artificial Intelligence with Attention Mechanism in Deep Learning (EIDCDR-XAIADL) model. This innovative
Cyberterrorism
Researchers from the Koneru Lakshmaiah Education Foundation in Telangana, India, have developed a novel fine-grained intelligent framework to address the growing threat of cyber-attacks. The framework combines Chaotic Theory with Tasmanian Devil Optimization to perform effective feature selection, followed by a Multi-Layered Extreme Learning Machine for enhanced classification. The proposed
Cardiff University
The detection of gravitational waves by scientists at Cardiff University a decade ago marked a significant milestone in understanding the universe. On September 14, 2015, the Laser Interferometer Gravitational-Wave Observatory (LIGO) detected the first-ever signal of gravitational waves, which had traveled 1.3 billion years from a pair of merging
Detection equipment
Scientists at the University of Glasgow marked the 10th anniversary of the first-ever direct detection of gravitational waves, a momentous occasion that predicted a new era in space-time exploration. On September 14, 2015, twin detectors at the Laser Interferometer Gravitational-Wave Observatory (LIGO) picked up a signal from a pair of
Detection equipment
The LIGO-Virgo-KAGRA collaboration has made a significant discovery with the detection of the loudest gravitational wave ever observed, confirming Stephen Hawking's area theorem on black holes. The event, GW250114, observed on January 14, 2025, has provided new insights into black hole entropy and mass, strengthening Hawking's
Machine learning
A team of researchers from Abdelmalek Essaadi University has developed a novel approach for detecting Android malware using a wavelet-based model that enhances system call feature selection. The model leverages the Chi-Square test for feature selection and utilizes the Haar wavelet to transform the selected attributes into wavelet coefficients. The
Machine learning
Researchers at Victoria University have proposed a new framework, the Top-K Similarity Graph Framework (TKSGF), for detecting intrusions in the Internet of Things (IoT) ecosystem. The framework uses a graph neural network, GraphSAGE, to capture node representations and maintain scalability. Experiments using the NF-ToN IoT and NF-BoT IoT datasets showed
Machine learning
Researchers from the University of Science and Technology China have developed a novel sensing platform for the quantitative real-time detection of isoquercitrin in phytopharmaceutical preparations. This research presents a graphene-integrated multiresonator architecture that leverages machine learning optimization to enhance analytical performance. The study demonstrates exceptional sensitivity parameters of up to
Machine learning
Cybersecurity experts are sounding the alarm as machine learning-based Intrusion Detection Systems (IDS) have significantly improved operational efficiency in Industrial Control Systems (ICS), but are increasingly vulnerable to Adversarial Machine Learning (AML) attacks. Researchers from Texas A&M University have introduced Reactive Autoencoder Defense for Industrial Adversarial Network Threats
Machine learning
Researchers from Polytechnic University Milan have investigated the potential impact of quantum machine learning (QML) on cybersecurity applications of traditional machine learning. Their findings suggest that QML could revolutionize the field of cybersecurity, but they also highlight the need for future quantum hardware and software advancements. The study focused on
Machine learning
Researchers at Space Engineering University in Beijing, People's Republic of China, have developed a novel framework called Transfficformer to generate adversarial attack traffic that can evade modern Network Intrusion Detection Systems (NIDS) powered by machine learning (ML) and deep learning (DL). The framework combines a heuristic algorithm and
