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Showing posts with the label Neural Network

Oops, No Victims: The Largest Supply Chain Attack Stole 5 Cents

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The Biggest NPM Supply Chain Attack What is a Supply Chain Attack? A supply chain attack occurs when attackers target trusted third-party components, such as libraries or registries, instead of attacking users directly. By injecting malicious code at the source, they can spread it to all downstream users. These attacks are dangerous because updates happen automatically in build pipelines, making detection harder. A small modification in a common dependency can silently compromise thousands of projects. Defenses require strong authentication, artifact signing, reproducible builds, and active monitoring of supply chain integrity. Introduction On September 8, 2025, the npm ecosystem faced one of its largest compromises. A maintainer’s account was hijacked, and malicious versions of popular packages were published. Since npm packages are used globally in countless projects, the exposure was immediate and severe. Although the financial damage was limited, the operational dis...
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Convolution Neural Network

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What is a Convolutional Neural Network (CNN)?      A Convolutional Neural Network (CNN) is a type of deep learning model specifically designed to process visual data like images and videos. It works by automatically learning patterns such as edges, textures, shapes, or objects from images without needing manual feature engineering. CNNs are inspired by the way the human visual cortex works and are widely used in computer vision tasks like image classification, object detection, face recognition, and more. CNNs reduce the complexity of image data using a structure of layers that include convolution layers, pooling layers, and fully connected layers. Each layer plays a unique role in extracting, simplifying, and interpreting the visual features. By stacking multiple such layers, CNNs can identify complex patterns and even recognize complete objects. One major advantage is that CNNs learn spatial hierarchies—starting from small patterns like edges to entire objects. CNNs ha...
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