Biblio

With the rapid development of Internet Technology in recent years, the demand for security support for complex applications is becoming stronger and stronger. Intel Software Guard Extensions (Intel SGX) is created as an extension of Intel Systems to enhance software security. Intel SGX allows application developers to create so-called enclave. Sensitive application code and data are encapsulated in Trusted Execution Environment (TEE) by enclave. TEE is completely isolated from other applications, operating systems, and administrative programs. Enclave is the core structure of Intel SGX Technology. Enclave supports multi-threading. Thread Control Structure (TCS) stores special information for restoring enclave threads when entering or exiting enclave. Each execution thread in enclave is associated with a TCS. This paper analyzes and verifies the possible security risks of enclave under concurrent conditions. It is found that in the case of multithread concurrency, a single enclave cannot resist flooding attacks, and related threads also throw TCS exception codes.

Applying machine learning techniques to detect malicious encrypted network traffic has become a challenging research topic. Traditional approaches based on studying network patterns fail to operate on encrypted data, especially without compromising the integrity of encryption. In addition, the requirement of rendering network-wide intelligent protection in a timely manner further exacerbates the problem. In this paper, we propose to leverage ×86 multicore platforms provisioned at enterprises' network edge with the software accelerators to design an encrypted traffic analytics (ETA) system with accelerated speed. Specifically, we explore a suite of data features and machine learning models with an open dataset. Then we show that by using Intel DAAL and OpenVINO libraries in model training and inference, we are able to reduce the training and inference time by a maximum order of 31× and 46× respectively while retaining the model accuracy.

This paper is to design substitution boxes (S-Boxes) using innovative I-Ching operators (ICOs) that have evolved from ancient Chinese I-Ching philosophy. These three operators-intrication, turnover, and mutual- inherited from I-Ching are specifically designed to generate S-Boxes in cryptography. In order to analyze these three operators, identity, compositionality, and periodicity measures are developed. All three operators are only applied to change the output positions of Boolean functions. Therefore, the bijection property of S-Box is satisfied automatically. It means that our approach can avoid singular values, which is very important to generate S-Boxes. Based on the periodicity property of the ICOs, a new network is constructed, thus to be applied in the algorithm for designing S-Boxes. To examine the efficiency of our proposed approach, some commonly used criteria are adopted, such as nonlinearity, strict avalanche criterion, differential approximation probability, and linear approximation probability. The comparison results show that S-Boxes designed by applying ICOs have a higher security and better performance compared with other schemes. Furthermore, the proposed approach can also be used to other practice problems in a similar way.

In the past three years, Emotion Recognition in the Wild (EmotiW) Grand Challenge has drawn more and more attention due to its huge potential applications. In the fourth challenge, aimed at the task of video based emotion recognition, we propose a multi-clue emotion fusion (MCEF) framework by modeling human emotion from three mutually complementary sources, facial appearance texture, facial action, and audio. To extract high-level emotion features from sequential face images, we employ a CNN-RNN architecture, where face image from each frame is first fed into the fine-tuned VGG-Face network to extract face feature, and then the features of all frames are sequentially traversed in a bidirectional RNN so as to capture dynamic changes of facial textures. To attain more accurate facial actions, a facial landmark trajectory model is proposed to explicitly learn emotion variations of facial components. Further, audio signals are also modeled in a CNN framework by extracting low-level energy features from segmented audio clips and then stacking them as an image-like map. Finally, we fuse the results generated from three clues to boost the performance of emotion recognition. Our proposed MCEF achieves an overall accuracy of 56.66% with a large improvement of 16.19% with respect to the baseline.