Distributed computing technology is widely used by Internet-based business applications. Supply chain management (SCM), customer relationship management (CRM), e-Commerce, and banking are some of the applications employing distributed computing. These applications are the main target to massive attacks known as distributed denial-of-service (DDoS) that cause a denial of service or degradation of services being rendered. The servers that provide reliable services to genuine users in a distributed environment are victims of such attacks that flood fake requests that appear genuine. Flash crowd, on the other hand, is the huge amount of traffic caused by certain flash events (FEs) that mimics DDoS attacks. Detection of DDoS attacks in the wake of flash crowds is a challenging problem to be addressed. The existing solutions are generally meant for either flash crowds or DDoS attacks and more research is needed to have a comprehensive approach for catering to the needs of detection of spoofed and non-spoofed variants of DDoS attacks. This paper proposes a methodology that can detect aforementioned DDoS attacks and differentiate them from flash crowds. NS-2 simulations are carried out on Ubuntu platform for validating the effectiveness of the proposed methodology.
Video Surveillance systems are playing vital role in ensuring the security at various public places like bus stops, railway stations, shopping malls, Airports etc. Suspicious activity recognition helps to prevent from threats and identify the causes after threat. Existing semi-automatic approaches depends on human intervention to detect the uncommon activities and suspicious behavior from video context. Due to these limitations they become non-intelligence, very slow and need more human observers. In this paper, to overcome these problems an Intelligent Suspicious Activity Detection Framework (ISADF) for Video data is proposed. This framework uses location dependent training data for intelligence and context (foreground) change information for suspicious activity detection. Experimental results show that ISADF is a high speed intelligent threat detection system than existing approaches.
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