Vulnerabilities are caused by implementation bugs, such as buffer overflow, integer overflow, uncontrolled format strings, and command injection flaws. They are often exploited to intrude software systems. In order to reduce software bugs, testing techniques are proposed. The recent technique to discover security-related bugs is fuzz testing. However, traditional fuzzers can only find bugs when program exceptions, especially crashes, raised. Some security threats may pass these tests due to insufficient code coverage. In this paper, we introduce a software testing framework based on symbolic execution using S2E, a whole system symbolic execution engine. When a program executes our pre-defined security sensitive functions, such as malloc, strcpy or printf, our framework will initiate a triage process. The process will determine whether any related security vulnerabilities would possibly occur in these functions automatically. We successfully and efficiently reproduce 12 previously known vulnerabilities from normal input data within 100 seconds for large applications such as Tiff, VIM, and MPlayer. Our tool can help developers locate bugs faster, and improve the efficiency of software quality maintenance.

In Android system, black box testing has risen to three key issues: S1: There is no source code and for tester to know the internal logic of the testing App. S2: There is no testing criterion for tester to know the correct behavior and testing scope of the testing App. S3: It is difficult to measure the testing coverage without instrumentation the testing App. In this paper, we provide a approach to analyze the GUI model during the testing process, implement the a black-box based android GUI testing system and select 7 Apps for evaluation. Finally we compare our result of our system with the monkey tool and discuss the inner App's properties that influence on the testing result.

In recent years, there has been an increase in video surveillance systems in public and private environments due to a heightened sense of security. The next generation of surveillance systems will be able to annotate video and locally coordinate the tracking of objects while multiplexing hundreds of video streams in real-time. In this paper, we present OmniEye, a wireless distributed real-time surveillance system composed of wireless smart cameras. OmniEye is comprised of custom-designed smart camera nodes called DSPcams that communicate using an IEEE 802.11 mesh network. These cameras provide wide-area coverage and local processing with the ability to direct a sparse number of high-resolution pan, tilt and zoom (PTZ) cameras that can home onto targets of interest. Each DSPcam performs local processing to help classify events and pro-actively draw an operator's attention when necessary. In video-streaming applications, maintaining high network utilization is required in order to maximize image quality as well as the number of cameras. Our experiments show that by using the standard 802.11 DCF MAC protocol for communication, the system does not scale beyond 5-6 cameras while each camera is streaming at 1 Mbps. Also, we see high levels of jitter in video transmissions. This performance degrades further for multi-hop scenarios due to the presence of hidden nodes. In order to improve the system's scalability and reliability, we propose a Time-Synchronized Application- level MAC protocol (TSAM) capable of operating on top of existing 802.11 protocols using commodity off-the-shelf hardware. Through analysis and experimental validation, we show how TSAM is able to improve throughput and provide bounded delay. Unlike traditional CSMA-based systems, TSAM gracefully degrades in a fair manner so that existing streams can still deliver data.