Malware image classification has emerged as an effective approach for identifying malware families by transforming binary files into visual representations and learning discriminative image patterns. While deep learning methods have achieved strong performance on this task, they often rely on computationally intensive training and large model sizes. In this paper, we present MalHDC, a lightweight hyperdimensional computing framework for malware image classification that encodes each malware image as a compact hypervector representation that captures both intensity and spatial information. Class prototypes are learned directly from training samples through non-backpropagation prototype learning, and inference is performed using similarity-based matching. We evaluate the proposed framework on the Malimg dataset, which contains 25 malware families, and analyze the impact of hypervector dimensionality on classification performance. Experimental results show that MalHDC achieves 98.07% classification accuracy for identifying the family of a malware sample, using only 125,000 stored hypervector elements, while also offering significant efficiency advantages over prior top-performing methods. In particular, MalHDC is 2.23 × faster than the next-fastest baseline in inference time and 1.32 × smaller than the most compact competing model, while maintaining higher or comparable accuracy to existing approaches. These results demonstrate that MalHDC offers a highly compact and efficient alternative to conventional deep learning methods for malware image classification.

Deep neural networks deployed in real-world systems may produce unreliable predictions when encountering inputs outside the training distribution. Detecting such out-of-distribution (OOD) inputs during inference is therefore critical for reliable deployment. We propose a lightweight monitoring framework that observes intermediate neural network embeddings and detects abnormal inputs using hyperdimensional computing (HDC). The monitor converts embeddings into binary hypervectors via random projection and sign encoding, and evaluates distance to class prototypes using only XOR and population-count operations. Experiments using CIFAR-10 as the in-distribution dataset and SVHN and CIFAR-100 as OOD datasets show that the proposed method achieves strong detection performance with highly efficient binary computation.

Word-aware sentiment analysis has posed a significant challenge over the past decade. Despite the considerable efforts of recent language models, achieving a lightweight representation suitable for deployment on resource-constrained edge devices remains a crucial concern. This study proposes a novel solution by merging two emerging paradigms, the Word2Vec language model and Hyperdimensional Computing, and introduces an innovative framework named Word2HyperVec. Our framework prioritizes model size and facilitates low-power processing during inference by incorporating embeddings into a binary space. Our solution demonstrates significant advantages, consuming only 2.2 W, up to 1.81 × more efficient than alternative learning models such as support vector machines, random forest, and multi-layer perceptron.