Distributed Denial of Service (DDoS) attack is one of the biggest cyber threats. DDoS attacks have evolved in quantity and volume to evade detection and increase damage. Changes during the COVID-19 pandemic have left traditional perimeter-based security measures vulnerable to attackers that have diversified their activities by targeting health services, e-commerce, and educational services. DDoS attack prediction searches for signals of attack preparation to warn about the imminence of the attack. Prediction is necessary to handle high-volumetric DDoS attacks and to increase the time to defend against them. This survey article presents the classification of studies from the literature comprising the current state-of-the-art on DDoS attack prediction. It highlights the results of this extensive literature review categorizing the works by prediction time, architecture, employed methodology, and the type of data utilized to predict attacks. Further, this survey details each identified study and, finally, it emphasizes the research opportunities to evolve the DDoS attack prediction state-of-the-art. 

Distributed denial of service attack prediction: Challenges, open issues and opportunities

The detection of anomaly traffic has become one of the principal directions in the field of network security intending to identify the attacks based on the specific deviations of the captured traffic. The cybercrime rate is increasing, capabilities of the cyber terrorists and hackers are growing at a higher rate. Today there is a requirement for the innovation and exploration for the mitigation of DDoS attacks. One of the most popular attacks in different layers of the network is Distributed Denial of Service (DDoS) a malicious try to interrupt regular traffic of a directed server, service, or network by irresistible to the target of its nearby infrastructure with anomalous flood traffic to the legitimate servers. An attacker usually targets for gaining access to virtual things like servers, applications, networks and sometimes targets particular transactions in an application. The detection of anomalous network traffic is one of the main challenging problems. Measures taken to stand as a wall of protection are intrusion detection systems and firewalls, which are still insufficient because day by day, there are different types of attacks happening, so these pillars are to be updated regularly. The predictive analytics methodology used for the detection of anomalous network traffic is by using statistical differences and alternate methods to improve detection performance based on Machine learning using statistical features of the dataset. This paper presents a detection model for DDoS attacks to enhance enterprise network security via machine learning. The machine learning framework extracts the high-level features and identifies the hidden patterns from network traffic and detects the DDoS attacks. The experimental results demonstrate the better performance of K- nearest neighbor and naive bayes algorithms compared with the conventional learning models.

Distributed Denial of Service Attack Detection using Naïve Bayes and K-Nearest Neighbor for Network Forensics

This paper proposes a smart grid distributed security architecture based on blockchain technology and SDN cluster structure, referred to as ClusterBlock model, which combines the advantages of two emerging technologies, blockchain and SDN. The blockchain technology allows for distributed peer-to-peer networks, where the network can ensure the trusted interaction of untrusted nodes in the network. At the same time, this article adopts the design of an SDN controller distributed cluster to avoid single point of failure and balance the load between equipment and the controller. A cluster head was selected in each SDN cluster, and it was used as a blockchain node to construct an SDN cluster head blockchain. By combining blockchain technology, the security and privacy of the SDN communication network can be enhanced. At the same time, this paper designs a distributed control strategy and network attack detection algorithm based on blockchain consensus and introduces the Jaccard similarity coefficient to detect the network attacks. Finally, this paper evaluates the ClusterBlock model and the existing model based on the OpenFlow protocol through simulation experiments and compares the security performance. The evaluation results show that the ClusterBlock model has more stable bandwidth and stronger security performance in the face of DDoS attacks of the same scale.

/pdf/a-distributed-security-sdn-cluster-architecture-for-smart-q3gizdubuk.pdf

A Distributed Security SDN Cluster Architecture for Smart Grid Based on Blockchain Technology

The results of the development of a denial of service network attack detection method for various services of storing, processing, and transmitting data on the Internet are presented. The focus is on the detection of low-level denial of service attacks. The opinion is refuted that special means for detecting denial of service attacks are not required since the fact of a detecting denial of service attacks cannot be overlooked. It is shown that for effective counteraction it is necessary to know the type, nature, and other indicators of a denial of service attack, and distributed attack detection systems allow you to quickly obtain this information. Also, the use of this type of attack detection system can significantly reduce the time it takes to determine the fact of an attack from 2-3 days to several tens of minutes, which reduces the cost of traffic and the downtime of the attacked resource. A hybrid neural network based on the Kohonen network and a multilayer perceptron is used as a detection module. The work of the created prototype of the attack detection system, the methodology for the formation of the training sample, the course of experiments, and the topology of the experimental stand are described. The results of an experimental study of the prototype are presented, during which errors of the first and second kind, respectively.

Using Hybrid Neural Networks to Detect DDOS Attacks

TPF-IEHO: Tuning phantom features on traffic flow network behavioral conditions to detected DDos based on improved elephant herding optimization neural classification

In order to detect Distributed Denial of Service (DDoS) attack accurately and rapidly, a DDoS attack detection algorithm based on hybrid traffic prediction model (DADA- HTPM) is proposed. Firstly, the hybrid traffic prediction model (HTPM) is proposed, in which the local projection, phase space reconstruction and RBF neural network technology are all used to restore the chaos of network traffic and train the network traffic samples to predict the future network traffic accurately. Then, the DDoS attack detection algorithm is developed based on two thresholds to reduce the false alerts caused by environment noise. Simulation results show that the DDoS attack detection algorithm based on hybrid traffic prediction model presented in this paper not only has higher traffic prediction accuracy and lower complexity, but also can detect DDoS attack quickly and accurately.

DDoS Attack Detection Algorithm Based on Hybrid Traffic Prediction Model

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DDoS Attack Detection Algorithm Based on Hybrid Traffic Prediction Model