Workforce Cybersecurity Practices Expose Organizations to AI-Driven Threats A comprehensive survey conducted by Accenture has uncovered alarming cybersecurity vulnerabilities in…
Meta is rolling out advanced scam detection features in WhatsApp and Messenger, including AI-powered chat analysis and screen-sharing warnings. The company is also implementing passkey support across multiple platforms to bolster user security against growing online threats.
Meta Platforms has begun deploying comprehensive new security features across its social media applications, according to reports from the company’s announcement. The enhanced protection measures target fraud, scams, and cybersecurity risks on Facebook, Instagram, Threads, WhatsApp, and Messenger, sources indicate.
Developer Receives Chilling Apple Alert In a startling development that blurs the lines between hunter and hunted, an exploit developer…
Veeam’s Landmark Acquisition in Data Security In a significant strategic move that underscores the growing importance of artificial intelligence in…
The Cloud’s Physical Reality When Amazon Web Services experienced significant outages on Monday, millions discovered firsthand that the “cloud” has…
Sophisticated Cyber Espionage Campaign Targets European Communications Infrastructure Security researchers have uncovered a significant expansion of operations by the notorious…
Emergency Patch Restores Critical Recovery Environment Functionality Microsoft has swiftly addressed a significant functionality breakdown in Windows 11’s recovery system…
Muji’s Digital Storefront Grinds to a Halt Following Logistics Partner Breach Japanese minimalist retailer Muji has been forced to suspend…
Major Privacy Oversight in Anti-Fraud Community In a striking case of organizational irony, Cifas—a leading UK anti-fraud nonprofit—has inadvertently exposed…
A groundbreaking study introduces a hybrid AI framework combining advanced feature selection with deep reinforcement learning to detect DDoS attacks in cloud environments. The approach reportedly addresses critical limitations in existing detection systems while maintaining real-time performance and interpretability. Researchers claim the method demonstrates superior accuracy and scalability across diverse network conditions.
Researchers have developed a comprehensive framework for detecting Distributed Denial of Service (DDoS) attacks in cloud environments using hybrid feature selection combined with deep reinforcement learning, according to recent reports. The methodology reportedly addresses significant gaps in existing detection systems, including limited multi-class attack categorization, insufficient scalability in distributed environments, and inadequate continuous learning capabilities.
