Hyperdimensional computing (HDC) is emerging as an efficient and robust computing paradigm that has strong resilience to various types of errors. The error robustness nature of HDC makes it a good match for error-prone memory systems. However, the mechanisms behind HDCs robustness are not fully understood. In this work, we propose HyperMetric, a framework to train highly robust and hardware-friendly HDC models. We found that HDC’s error resilience is driven by Hamming distance margin between hypervectors. Based on this, we propose HyperMetric training that is based on metric learning in order to optimize for high robustness. The experiments show that HyperMetric trained HDC models deliver up to 17W larger Hamming distance margin and up to 14.3 We accelerate HyperMetric trained models using ReRAM. As compared to state-of-the-art HDC algorithms OnlineHD and HyDREA, HyperMetric ReRAM accelerator is > 20% more accurate for computing-in-memory (CIM) errors and > 10% more accurate for bit errors even in the face of variations. Furthermore, HyperMetric hardware is 35% more accurate in comparison with existing tinyHD and GENERIC accelerators in the face of 3× ReRAM resistance variance, and 20% more accurate with BER of up to 20% due to voltage scaling while keeping a good balance between area, power, and processing laten

Intrusion detection systems (IDS) play a crucial role in IoT and network security by monitoring system data and alerting to suspicious activities. Machine learning (ML) has emerged as a promising solution for IDS, offering highly accurate intrusion detection. However, ML-IDS solutions often overlook two critical aspects needed to build reliable systems: continually changing data streams and a lack of attack labels. Streaming network traffic and associated cyber attacks are continually changing, which can degrade the performance of deployed ML models. Labeling attack data, such as zero-day attacks, in real-world intrusion scenarios may not be feasible, making the use of ML solutions that do not rely on attack labels necessary. To address both these challenges, we propose CND-IDS, a continual novelty detection IDS framework which consists of (i) a learning-based feature extractor that continuously updates new feature representations of the system data, and (ii) a novelty detector that identifies new cyber attacks by leveraging principal component analysis (PCA) reconstruction. Our results on realistic intrusion datasets show that CND-IDS achieves up to $6.1 \times$ F-score improvement, and up to $6.5 \times$ improved forward transfer over the SOTA unsupervised continual learning algorithm. Our code is available at https://github.com/Sean-Fuhrman/CND-IDS.

Hyperdimensional computing (HDC) is emerging as an efficient and robust computing paradigm that has strong resilience to various types of errors. The robustness of HDC makes it a good match for error-prone memory systems. In this work, we propose HyperMetric, a framework to develop highly robust and hardware-friendly HDC models. First, we propose HyperMetric training which is based on metric learning to optimize for high robustness. The experiments show that HyperMetric-trained HDC models deliver up to 17× larger distance margin and 14.3% accuracy gain. Compared to state-of-the-art HDC algorithms OnlineHD [1] and HyDREA [2], HyperMetric ReRAM accelerator is > 20% more accurate for computing-in-memory (CIM) errors and > 10% more accurate for bit errors even in the face of variations. Furthermore, HyperMetric hardware is 35% more accurate in comparison with state of the art tinyHD [3] and GENERIC [4] accelerators in the face of 3× ReRAM resistance variance, and 20% more accurate with BER of up to 20% due to voltage scaling while keeping a good balance between area, power, and processing latency.