A novel sensitive DDoS attacks against statistical test in network traffic fusion

Abstract

Distributed denial of service (DDoS) attacks are common attacks that limit casual users from cracking critical network resources. A method to find the DDoS attack comes to light as an essential and hot research issue. Therefore, the increment in network usage scale demands more challenges to see the DDoS attack. Non‐real‐time collection of network traffic will make sincere complications inaccuracy; DDoS attack detection, and efficiency. Lately, sketch data structures play an excellent role in high‐speed networks to suppress and combine network traffic issues. However, the sketch data structure has issues rebuilding the reverse keys that behave abnormally due to the reversible hash function. To rectify the above difficulties, we include the Chinese Remainder Theorem‐based Reverse Sketching (CRTRS). However, Reverse Sketching is not good at suppressing and combining heavy network traffic, but it can easily detect abnormal keys. According to the network traffic records setup by CRTRS, we add the Modified Cumulative Sum Quality Control Scheme (MCSQCS) that encourages DDoS attack detection. The enforcement of the proposed work with DDoS attack detection has two kind of different data sets. The result of the experiments shows that it gives more accuracy and efficiency in detecting DDoS attacks. Therefore, comparing other DDoS detecting methods with the sketch data structure has fewer computational complications. Improving the anomalous keys is the most critical challenge in this proposed work. To illustrate the accuracy rate, we experiment with a new technique called CIC‐Friday Heavy network traffic fragment. We also explain the experimental results of the MAWI heavy network fragment with the various parameter setups. To compare our proposed method with other similar existing methods, we use a sketch data structure to manage the network traffic data. It also uses the MSPCA method to detect the DDoS attack. The information‐based analysis helps to see the DDoS attack. The result analysis of True Positive Value (TPV) and False Positive Value (FPV) with MSPCA techniques has 79% and .1% accuracy. Similarly, information‐based detection has an accuracy of about 90% and 5%. Hence, the proposed method gives more accuracy for the detection of DDoS attacks.

Dynamic sliding window‐cooperative spectrum sensing against massive SSDF attack in interweave cognitive internet of things

“…In References 33–38, the general state of development of IoT was indicated. The difficulty of IoT lies in lower latency and flexible networking that takes into account security and openness.…”