Biblio

In current deep network architectures, deeper layers in networks tend to contain hundreds of independent neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons by correlation, humans can observe how clusters of neighbouring neurons interact with each other. In this paper, we propose a novel algorithm for back propagation, called Locality Guided Neural Network (LGNN) for training networks that preserves locality between neighbouring neurons within each layer of a deep network. Heavily motivated by Self-Organizing Map (SOM), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other. This method contributes to the domain of Explainable Artificial Intelligence (XAI), which aims to alleviate the black-box nature of current AI methods and make them understandable by humans. Our method aims to achieve XAI in deep learning without changing the structure of current models nor requiring any post processing. This paper focuses on Convolutional Neural Networks (CNNs), but can theoretically be applied to any type of deep learning architecture. In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100. In depth analyses presenting both qualitative and quantitative results demonstrate that our method is capable of enforcing a topology on each layer while achieving a small increase in classification accuracy.

Cryptosystems are essential for computer and communication security, e.g., RSA or ECDSA in PGP Email clients and AES in full disk encryption. In practice, the cryptographic keys are loaded and stored in RAM as plain-text, and therefore vulnerable to cold-boot attacks exploiting the remanence effect of RAM chips to directly read memory data. To tackle this problem, we propose Copker, a cryptographic engine that implements asymmetric cryptosystems entirely within the CPU, without storing any plain-text sensitive data in RAM. Copker supports the popular asymmetric cryptosystems (i.e., RSA and ECDSA), and deterministic random bit generators (DRBGs) used in ECDSA signing. In its active mode, Copker stores kilobytes of sensitive data, including the private key, the DRBG seed and intermediate states, only in on-chip CPU caches (and registers). Decryption/signing operations are performed without storing any sensitive information in RAM. In the suspend mode, Copker stores symmetrically-encrypted private keys and DRBG seeds in memory, while employs existing solutions to keep the key-encryption key securely in CPU registers. Hence, Copker releases the system resources in the suspend mode. We implement Copker with the support of multiple private keys. With security analyses and intensive experiments, we demonstrate that Copker provides cryptographic services that are secure against cold-boot attacks and introduce reasonable overhead.

In this paper, an advanced security and stability defense framework that utilizes multisource power system data to enhance the power system security and resilience is proposed. The framework consists of early warning, preventive control, on-line state awareness and emergency control, requires in-depth collaboration between power engineering and data science. To realize this framework in practice, a cross-disciplinary research topic — the big data analytics for power system security and resilience enhancement, which consists of data converting, data cleaning and integration, automatic labelling and learning model establishing, power system parameter identification and feature extraction using developed big data learning techniques, and security analysis and control based on the extracted knowledge — is deeply investigated. Domain considerations of power systems and specific data science technologies are studied. The future technique roadmap for emerging problems is proposed.