Lei Shi

Weichun Xia, Jiaxin Jiang, Lei Shi

We introduce a novel diffusion-based spectral algorithm to tackle regression analysis on high-dimensional data, particularly data embedded within lower-dimensional manifolds. Traditional spectral algorithms often fall short in such contexts, primarily due to the reliance on predetermined kernel functions, which inadequately address the complex structures inherent in manifold-based data. By employing graph Laplacian approximation, our method uses the local estimation property of heat kernel, offering an adaptive, data-driven approach to overcome this obstacle. Another distinct advantage of our algorithm lies in its semi-supervised learning framework, enabling it to fully use the additional unlabeled data. This ability enhances the performance by allowing the algorithm to dig the spectrum and curvature of the data manifold, providing a more comprehensive understanding of the dataset. Moreover, our algorithm performs in an entirely data-driven manner, operating directly within the intrinsic manifold structure of the data, without requiring any predefined manifold information. We provide a convergence analysis of our algorithm. Our findings reveal that the algorithm achieves a convergence rate that depends solely on the intrinsic dimension of the underlying manifold, thereby avoiding the curse of dimensionality associated with the higher ambient dimension.

Lei Shi, Yuepeng Wang, Rajeev Alur et al.

Debugging imperative network programs is a challenging task for developers because understanding various network modules and complicated data structures is typically time-consuming. To address the challenge, this paper presents an automated technique for repairing network programs from unit tests. Specifically, given as input a faulty network program and a set of unit tests, our approach localizes the fault through symbolic reasoning, and synthesizes a patch such that the repaired program can pass all unit tests. It applies domain-specific abstraction to simplify network data structures and utilizes modular analysis to facilitate function summary reuse for symbolic analysis. We implement the proposed techniques in a tool called NetRep and evaluate it on 10 benchmarks adapted from real-world software-defined networking controllers. The evaluation results demonstrate the effectiveness and efficiency of NetRep for repairing network programs.

Lei Shi

State-of-the-art visual analytics techniques in application domains are often designed by VA professionals over qualitative requirement collected from end users. These VA techniques may not leverage users' domain knowledge about how to achieve their analytical goals. In this position paper, we propose a user-driven design process of VA applications centered around a new concept called analytical representation (AR). AR features a formal abstraction of user requirement and their desired analytical trails for certain VA application, and is independent of the actual visualization design. A conceptual graph schema is introduced to define the AR abstraction, which can be created manually or constructed by semi-automated tools. Designing VA applications with AR provides a shared opportunity for both optimal analysis blueprint from the perspective of end users and optimal visualization/algorithm from the perspective of VA designers. We demonstrate the usage of the design process in two case studies.

Lei Shi, Yahui Li, Rajeev Alur et al.

Writing classification rules to identify malicious network traffic is a time-consuming and error-prone task. Learning-based classification systems automatically extract such rules from positive and negative traffic examples. However, due to limitations in the representation of network traffic and the learning strategy, these systems lack both expressiveness to cover a range of attacks and interpretability in fully describing the attack traffic's structure at the session layer. This paper presents Sharingan system, which uses program synthesis techniques to generate network classification programs at the session layer. Sharingan accepts raw network traces as inputs, and reports potential patterns of the attack traffic in NetQRE, a domain specific language designed for specifying session-layer quantitative properties. Using Sharingan, network operators can better analyze the attack pattern due to the following advantages of Sharingan's learning process: (1) it requires minimal feature engineering, (2) it is amenable to efficient implementation of the learnt classifier, and (3) the synthesized program is easy to decipher and edit. We develop a range of novel optimizations that reduce the synthesis time for large and complex tasks to a matter of minutes. Our experiments show that Sharingan is able to correctly identify attacks from a diverse set of network attack traces and generates explainable outputs, while achieving accuracy comparable to state-of-the-art learning-based intrusion detection systems.