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Distributed Denial-of-Service (DDoS) is a big threat to the security and stability of Internet-based services today. Among the recent advanced application-layer DDoS attacks, the Very Short Intermittent DDoS (VSI-DDoS) is the attack, which can bypass existing detection systems and significantly degrade the QoS experienced by users of web services. However, in order for the VSI-DDoS attack to work effectively, bots participating in the attack should be tightly synchronized, an assumption that is difficult to be met in reality. In this paper, we conducted a quantitative analysis to understand how a minimal deviation from perfect synchronization in botnets affects the performance and effectiveness of the VSI-DDoS attack. We found that VSI-DDoS became substantially less effective. That is, it lost 85.7% in terms of effectiveness under about 90ms synchronization inaccuracy, which is a very small inaccuracy under normal network conditions. 

Vulnerabilities have a detrimental effect on end-users and enterprises, both direct and indirect; including loss of private data, intellectual property, the competitive edge, performance, etc. Despite the growing software industry and a push towards a digital economy, enterprises are increasingly considering security as an added cost, which makes it necessary for those enterprises to see a tangible incentive in adopting security. Furthermore, despite data breach laws that are in place, prior studies have suggested that only 4% of reported data breach incidents have resulted in litigation in federal courts, showing the limited legal ramifications of security breaches and vulnerabilities. In this paper, we study the hidden cost of software vulnerabilities reported in the National Vulnerability Database (NVD) through stock price analysis. Towards this goal, we perform a high-fidelity data augmentation to ensure data reliability and to estimate vulnerability disclosure dates as a baseline for estimating the implication of software vulnerabilities. We further build a model for stock price prediction using the NARX Neural Network model to estimate the effect of vulnerability disclosure on the stock price. Compared to prior work, which relies on linear regression models, our approach is shown to provide better accuracy. Our analysis also shows that the effect of vulnerabilities on vendors varies, and greatly depends on the specific software industry. Whereas some industries are shown statistically to be affected negatively by the release of software vulnerabilities, even when those vulnerabilities are not broadly covered by the media, some others were not affected at all.