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Nowadays highly-skilled attackers can find the vulnerabilities of many networked applications. Meanwhile, the risk of a data breach increases dramatically as a software or application vulnerability always remains without a patch. By exploiting such vulnerability (called zero-day), hackers gain entry to the target network and can steal sensitive data. It is challenging to detect zero-day with traditional defenses because signature information in zero-day attacks is unknown. Consequently, a novel security solution is required that will discover zero-day attacks and estimate the severity of identified zero-day vulnerability. In this paper, we propose a framework that constitutes an integrated approach for detection and prioritization (based on likelihood) of zero-day attacks. The proposed framework follows a probabilistic approach for identification of the zero-day attack path and further to rank the severity of identified zero-day vulnerability. It is a hybrid detection-based technique that detects unknown flaws present in the network that are not detected yet. To evaluate the performance of the proposed framework, we adopted it in the network environment of Vikram university campus, India. The framework is very promising as experimental results showed detection rate of 96% for zero-day attacks with 0.3% false positive rate. [ABSTRACT FROM AUTHOR]

• We developed a Delay-based Adaptive Congestion Control TCP variant (DACC). • DACC has an initialization method that obtains an optimal initial value for the congestion window. • It has a queueing-delay variation-based scheme that contemplates the contribution of all contention traffic flows. • It has improved performance in goodput, throughput, file transfer time, and fairness. Delay-based congestion control policies of the Transmission Control Protocol (TCP) mainly use the Round-Trip-Time (RTT) and bottleneck queue size to detect congestion. However, these policies identify congestion by only taking into account the contribution of their own data flow. Thus, the congestion identification in these policies fails to be completely accurate. Moreover, these policies experience many packet losses resulting in retransmissions, which can further reduce channel utilization. This article proposes a queuing delay variation-based adaptive congestion control TCP variant, called Delay-based Adaptive Congestion Control (DACC), that considers the presence of background flows. DACC quickly adapts to the current network conditions and adjusts the transmission rate accordingly. The simulation results exemplify the significance of DACC as it yields better outcomes in terms of goodput, Packet Loss Ratio (PLR), inter-protocol fairness, intra-protocol fairness, and fast file transfers. [Display omitted] [ABSTRACT FROM AUTHOR]