Distributed denial of service (DDoS) attacks is rising exponentially and creates a severe threat to security. Generally, the DDoS attack may appear uncomplicated but they are hard to prevent and considered as one of the most significant cybersecurity issues. Hence, addressing against DDoS attacks turns out to be imperative. The major goal of this paper involves the optimal detection of data samples as normal data samples and malicious/attacked data samples. This paper proposes a security algorithm against DDoS attacks by employing four significant phases namely the Database training phase, Data pre-processing phase, Feature selection phase and Classification phase. Initially, the data samples are to be trained before using it for attack detection. Later, the sample group is created for every file and the data samples are pre-processed in the data pre-processing phase. Secondly, in feature selection phase, the selected features are optimized by employing kernel principal component analysis (KPCA) to obtain optimal features. Later, in the classification phase, a support vector machine-based discrete elephant herding optimization (SVM-DEHO) classifier is utilized to detect the data sample as normal data and attacked or malicious data. Finally, the proposed approach is examined for four different databases namely NSL-KDD, UNSW-NB15, ISCX ID and CIC-IDS2017 databases respectively. The experimental analyses are performed for various simulation metrics and the outcome reveals that the detection system performances are high using SVM-DEHO approach than other approaches. 

An efficient SVM based DEHO classifier to detect DDoS attack in cloud computing environment

Distributed denial-of-service attack detection for smart grid wide area measurement system: A hybrid machine learning technique

DDoS: Distributed denial of service attack in communication standard vulnerabilities in smart grid applications and cyber security with recent developments

The essential advantage of cloud computing is that it flexibly scales to fulfill various needs and it provides the sufficient environment that scales up and downsizes quickly as indicated by the interest, so it needs incredible security from DDoS attack to handle vacation impacts of DDoS attacks. Circulate DoS assaults fall on the classification of basic assaults that bargain the accessibility of the system. These assaults have gotten refined and keep on developing at a quick pace so to identify and to take down these assaults have had become a difficult undertaking. In this project, we have designed a hybrid algorithm which consists of a combination of several machine learning techniques to train a model which can be used to detect and classify the type of DDoS attack with greater accuracy than that of each individual machine learning technique used in the hybrid model.

Datla Anurag Varma

Papers

Detection of DDOS Attacks Using Machine Learning Techniques: A Hybrid Approach