Biblio

This paper proposes an infrared sensor human target locating system for the Internet of Things. In this design, the wireless sensor network is designed and developed to detect human targets by using 6LoWPAN protocol and pyroelectric infrared (PIR) sensors. Based on the detection data acquired by multiple sensor nodes, K-means++ clustering algorithm combined with cost function is applied to complete human target location in a 10m×10m detection area. The experimental results indicate the human locating system works well and the user can view the location information on the terminal devices.

Many social networks contain sensitive relational information. One approach to protect the sensitive relational information while offering flexibility for social network research and analysis is to release synthetic social networks at a pre-specified privacy risk level, given the original observed network. We propose the DP-ERGM procedure that synthesizes networks that satisfy the differential privacy (DP) via the exponential random graph model (EGRM). We apply DP-ERGM to a college student friendship network and compare its original network information preservation in the generated private networks with two other approaches: differentially private DyadWise Randomized Response (DWRR) and Sanitization of the Conditional probability of Edge given Attribute classes (SCEA). The results suggest that DP-EGRM preserves the original information significantly better than DWRR and SCEA in both network statistics and inferences from ERGMs and latent space models. In addition, DP-ERGM satisfies the node DP, a stronger notion of privacy than the edge DP that DWRR and SCEA satisfy.

Malicious login, especially lateral movement, has been a primary and costly threat for enterprises. However, there exist two critical challenges in the existing methods. Specifically, they heavily rely on a limited number of predefined rules and features. When the attack patterns change, security experts must manually design new ones. Besides, they cannot explore the attributes' mutual effect specific to login operations. We propose MLTracer, a graph neural network (GNN) based system for detecting such attacks. It has two core components to tackle the previous challenges. First, MLTracer adopts a novel method to differentiate crucial attributes of login operations from the rest without experts' designated features. Second, MLTracer leverages a GNN model to detect malicious logins. The model involves a convolutional neural network (CNN) to explore attributes of login operations, and a co-attention mechanism to mutually improve the representations (vectors) of login attributes through learning their login-specific relation. We implement an evaluation of such an approach. The results demonstrate that MLTracer significantly outperforms state-of-the-art methods. Moreover, MLTracer effectively detects various attack scenarios with a remarkably low false positive rate (FPR).

Cloud Native, the emerging computing infrastructure has become a new trend for cloud computing, especially after the development of containerization technology such as docker and LXD, and the orchestration system for them like Kubernetes and Swarm. With the growing popularity of Cloud Native, the following problems have been raised: (i) most Cloud Native applications were designed for making full use of the cloud platform, but their file storage has not been completely optimized for adapting it. (ii) the traditional file system is designed as a utility for storing and retrieving files, usually built into the kernel of the operating systems. But when placing it to a large-scale condition, like a network storage server shared by thousands of computing instances, and stores millions of files, it will be slow and even unstable. (iii) most storage solutions use metadata for faster tracking of files, but the metadata itself will take up a lot of space, and the capacity of it is usually limited. If the file system store metadata directly into hard disk without caching, the tracking of massive small files will be a lot slower. (iv) The traditional object storage solution can't provide enough features to make itself more practical on the cloud such as caching and auto replication. This paper proposes a new storage engine based on the well-known Haystack storage engine, optimized in terms of service discovery and Automated fault tolerance, make it more suitable for Cloud Native infrastructure, deployment and applications. We use the object storage model to solve the large and high-frequency file storage needs, offering a simple and unified set of APIs for application to access. We also take advantage of Kubernetes' sophisticated and automated toolchains to make cloud storage easier to deploy, more flexible to scale, and more stable to run.

As a multimedia security mechanism, CAPTCHAs are completely automated public turing test to tell computers and humans apart. Although cracking CAPTCHA has been explored for many years, it is still a challenging problem for real practice. In this demo, we present a text based CAPTCHA cracking system by using convolutional neural networks(CNN). To solve small sample problem, we propose to combine conditional deep convolutional generative adversarial networks(cDCGAN) and CNN, which makes a tremendous progress in accuracy. In addition, we also select multiple models with low pearson correlation coefficients for majority voting ensemble, which further improves the accuracy. The experimental results show that the system has great advantages and provides a new mean for cracking CAPTCHAs.

Quality of service (QoS) has been considered as a significant criterion for querying among functionally similar web services. Most researches focus on the search of QoS under certain data which may not cover some practical scenarios. Recent approaches for uncertain QoS of web service deal with discrete data domain. In this paper, we try to build the search of QoS under continuous probability distribution. We offer the definition of two kinds of queries under uncertain QoS and form the optimization approaches for specific distributions. Based on that, the search is extended to general cases. With experiments, we show the feasibility of the proposed methods.

Software structure analysis is crucial in software testing. Using complex network theory, we present a series of methods and build a two-layer network model for software analysis, including network metrics calculation and features extraction. Through identifying the critical functions and reused modules, we can reduce nearly 80% workload in software testing on average. Besides, the structure network shows some interesting features that can assist to understand the software more clearly.