Check it out todaySeven-year browser-extension campaign from “ShadyPanda” infected 4.3M users : The group known as ShadyPanda spent years publishing seemingly legitimate extensions to browsers like Chrome and Edge — accumulating user trust — before silently updating them with malicious code. The campaign reportedly infected around 4.3 million users. The case underscores long-term supply-chain-style extension abuse and raises alarm about post-installation update security.“Telemetry Complexity Attacks”, a new class of bypass techniques against malware analysis & EDR platforms: A recent research paper demonstrated how adversaries can exploit weaknesses in telemetry collection pipelines used by malware analysis and EDR systems. By generating deeply nested and oversized telemetry data, attackers can trigger serializer or database failures — effectively causing denial-of-analysis (DoA) and hiding malicious behavior from detection. The research flagged real-world systems for failure under this technique.
AI & Security: Revolutionizing Cybersecurity in the Digital Age: This article explores how artificial intelligence (AI) is transforming cybersecurity — shifting defences from reactive to proactive. It examines use-cases where AI helps detect and mitigate threats, analyzes the challenges of integrating AI into security strategies, and highlights how organizations can leverage modern AI/ML to improve their security posture.
Intrusion detection using TCP/IP single packet header binary image for IoT networks(Mohamed El-Sherif, Ahmed Khattab & Magdy El-Soudani):This paper proposes a novel intrusion detection approach for IoT networks by converting single raw TCP/IP packet headers into binary (black-and-white) images. Then, using a lightweight Convolutional Neural Network (CNN), the system classifies traffic as benign or malicious. On benchmark IoT datasets (Edge-IIoTset and MQTTset), the method achieved perfect or near-perfect detection rates (100% binary accuracy, ~97–100% multiclass accuracy) — all with minimal computational resources. The approach avoids heavy feature engineering or payload inspection, making it suitable for resource-constrained IoT devices and real-time deployment.
Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions (Hemanth Ravipati): As neuromorphic computing (brain-inspired hardware) becomes more common — especially in edge devices, IoT, and AI application — this paper demonstrates for the first time a novel class of threats: Neuromorphic Mimicry Attacks (NMAs). Because neuromorphic chips operate with probabilistic and non-deterministic neural activity, attackers can tamper with synaptic weights or poison sensory inputs to mimic legitimate neural signals. Such attacks can evade conventional intrusion detection systems. The paper provides a theoretical framework, simulations, and proposes countermeasures (e.g., neural-specific anomaly detection, secure learning protocols). The study warns that as neuromorphic hardware spreads, these threats will become increasingly relevant.
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