MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection and Classification

Out of distribution (OOD) detection remains a critical challenge in malware classification due to the substantial intra family variability introduced by polymorphic and metamorphic malware variants. Most existing deep learning based malware detectors rely on closed world assumptions and fail to adequately model this intra class variation, resulting in degraded performance when confronted with previously unseen malware families. This paper presents MADOOD, a novel two stage, cluster driven deep learning framework for robust OOD malware detection and classification. In the first stage, malware family embeddings are modeled using class conditional spherical decision boundaries derived from Gaussian Discriminant Analysis (GDA), enabling statistically grounded separation of indistribution and OOD samples without requiring OOD data during training. Z score based distance analysis across multiple class centroids is employed to reliably identify anomalous samples in the latent space. In the second stage, a deep neural network integrates cluster based predictions, refined embeddings, and supervised classifier outputs to enhance final classification accuracy. Extensive evaluations on benchmark malware datasets comprising 25 known families and multiple novel OOD variants demonstrate that MADOOD significantly outperforms state of the art OOD detection methods, achieving an AUC of up to 0.911 on unseen malware families. The proposed framework provides a scalable, interpretable, and statistically principled solution for real world malware detection and anomaly identification in evolving cybersecurity environments.

Enhancing Border Security and Countering Terrorism Through Computer Vision: a Field of Artificial Intelligence

Border security had been a persistent problem in international border especially when it get to the issue of preventing illegal movement of weapons, contraband, drugs, and combating issue of illegal or undocumented immigrant while at the same time ensuring that lawful trade, economic prosperity coupled with national sovereignty across the border is maintained. In this research work, we used open source computer vision (Open CV) and adaboost algorithm to develop a model which can detect a moving object a far off, classify it, automatically snap full image and face of the individual separately, and then run a background check on them against worldwide databases while making a prediction about an individual being a potential threat, intending immigrant, potential terrorists or extremist and then raise sound alarm. Our model can be deployed on any camera device and be mounted at any international border. There are two stages involved, we first developed a model based on open CV computer vision algorithm, with the ability to detect human movement from afar, it will automatically snap both the face and the full image of the person separately, and the second stage is the automatic triggering of background check against the moving object. This ensures it check the moving object against several databases worldwide and is able to determine the admissibility of the person afar off. If the individual is inadmissible, it will automatically alert the border officials with the image of the person and other details, and if the bypass the border officials, the system is able to detect and alert the authority with his images and other details. All these operations will be done afar off by the AI powered camera before the individual reach the border