Abstract
Preventing DoS attacks are critical to avoiding disruptions of the regular services over networks, by being able to identify the attacks when they occur. In the literature there are several methods which enable the detection and prevention of such an attack. However, with the traditional techniques, the attacks with distributed and low-rate strategies are often successful in the absence of control that is not aware of spatio-temporal characteristics of network traffic. In such cases, the attackers attain the total disruption of the services or the network components, or consumption of the available network resources in bandwidth. Traditional DoS detection controls are easily fooled by attack scenarios if the attackers use non-attack traffic patterns or payloads that are spatially distributed. Even the mechanisms involving traffic patterns or payload contents for detecting the attacks cannot safeguard the networks and servers from complicated DoS attacks. These issues are addressed by this research proposal through a proposed usage of DoS detection techniques and Spatio-temporal network traffic features. Knowledge of Spatio-temporal features enables detection controls to determine the attacks that cannot normally be detected using conventional methods including jamming attacks in the wireless networks. Thus, based on the characteristics of the traffic information, MR can use a hybrid model integrating sequential and convolutional machines to extract the corresponding spatio-temporal features. Different from more traditional use such as seasonal probability and long and short term forecasting. The above mentioned methods shall give the prediction of the temporal-spatial characteristics regarding the delay, the pure throughput and the probability density of the jitter. Hence the main objective of this research is to develop an enhanced procedure to identify DoS attacks particularly in cases of low data rate attacks with the availably of Spatio-temporal features of network traffic.
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