Image-Based Feature Representation for Insider Threat Classification

Image Classification via Sparse Representation and Subspace Alignment

Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...

Defending Insider Threat

Network threat confronting organizations comes from not only outsider threat, but also insider threat. Nowadays, insider threat is widely recognized as an important issue of security management. However, tools and controls on how to fight against it are still in the research phase. Security architecture for defending insider threat is presented, which is composed of four parts: monitoring platf...

Systematizing Insider Threat mitigation

Deep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning

Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...

Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification

Integrating spectral and spatial information is proved effective in improving the accuracy of hyperspectral imagery classification. In recent studies, two kinds of approaches are widely investigated: (1) developing a multiple feature fusion (MFF) strategy; and (2) designing a powerful spectral-spatial feature extraction (FE) algorithm. In this paper, we combine the advantages of MFF and FE, and...