scholarly journals DDoS Attack Detection Algorithms Based on Pattern Classification and Machine Learning

2021 ◽  
Vol 23 (2) ◽  
Author(s):  
Anup Ingle ◽  
◽  
Dr. Avinash Gour ◽  
Dr. Ketki Kshirsagar ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Classification ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Memory Space ◽  
Detection Algorithms ◽  
Ddos Attack ◽  
Ddos Attack Detection ◽  
Day By Day

Damage from DDoS attack in increasing day by day and an efficient attack detection algorithm is urgently needed. Many current DDoS algorithms are based on anomaly detections which are ineffective in real environment. Detection DDoS attack can be tackled effectively with pattern classification based on flow of packet and machine learning algorithms. In this paper three such pattern classificationsbased on flow of packet and machine learning based algorithm for detection of DDoS attack are discussed. Implementation of these algorithms gives better accuracy in limited time and memory space; hence it’s one of the highly scalable and effective in detection of DDoS attack.

DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment

2021 ◽  
Vol 15 (3) ◽  
pp. 1-18
Author(s):  
Hongsong Chen ◽  
Caixia Meng ◽  
Jingjiu Chen
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Network Traffic ◽  
Large Scale ◽  
Learning Algorithms ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Ddos Attack ◽  
Experimental Environment ◽  
Ddos Attack Detection

Aiming at the problem of DDoS attack detection in internet of things (IoT) environment, statistical and machine-learning algorithms are proposed to model and analyze the network traffic of DDoS attack. Docker-based virtualization platform is designed and configured to collect IoT network traffic data. Then the packet-level, flow-level, and second-level network traffic datasets are generated, and the importance of features in different traffic datasets are sorted. By SKlearn and TensorFlow machine-learning software framework, different machine learning algorithms are researched and compared. In packet-level DDoS attack detection, KNN algorithm achieves the best results; the accuracy is 92.8%. In flow-level DDoS attack detection, the voting algorithm achieves the best results; the accuracy is 99.8%. In second-level DDoS attack detection, the RNN algorithm behaves best results; the accuracy is 97.1%. The DDoS attack detection method combined with statistical analysis and machine-learning can effectively detect large-scale DDoS attacks on the internet of things simulation experimental environment.

Real-Time DDoS Attack Detection Based on Machine Learning Algorithms

2021 ◽  
Author(s):  
Shriram Rajesh ◽  
Marvin Clement ◽  
Sooraj S. B. ◽  
Al Shifan S. H. ◽  
Jyothi Johnson
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Algorithms ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Ddos Attack ◽  
Ddos Attack Detection

scholarly journals Comparison of machine learning algorithms for DDoS attack detection in SDN

2020 ◽  
pp. 59-70
Author(s):  
Duc Le ◽  
Minh Dao ◽  
Quyen Nguyen
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Software Defined Networks ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Flow Table ◽  
Ddos Attack Detection

Introduction: Distributed denial-of-service (DDoS) has become a common attack type in cyber security. Apart from the conventional DDoS attacks, software-defined networks also face some other typical DDoS attacks, such as flow-table attack or controller attack. One of the most recent solutions to detect a DDoS attack is using machine learning algorithms to classify the traffic. Purpose: Analysis of applying machine learning algorithms in order to prevent DDoS attacks in software-defined network. Results: A comparison of six algorithms (random forest, decision tree, naive Bayes, support vector machine, multilayer perceptron, k-nearest neighbors) with accuracy and process time as the criteria has shown that a decision tree and naïve Bayes are the most suitable algorithms for DDoS attack detection. As compared to other algorithms, they have higher accuracy, faster processing time and lower resource consumption.  The main features that identify malicious traffic compared to normal one are the number of bytes in a flow, time flow, Ethernet source address, and Ethernet destination address. A flow-table attack can be detected easier than a bandwidth attack, as all the six algorithms can predict this type with a high accuracy. Practical relevance: Important features which play a supporting role in correct data classification facilitate the development of a DDoS protection system with a smaller dataset, focusing only on the necessary data. The algorithms more suitable for machine learning can help us to detect DDoS attacks in software-defined networks more accurately.

Structural Design of Intrusion Detection System

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng
Keyword(s):  
Intrusion Detection ◽  
Structural Design ◽  
Intrusion Detection System ◽  
Detection System ◽  
Algorithm Design ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Detection Algorithms ◽  
Defense Systems ◽  
Ddos Attack Detection

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.

scholarly journals Machine-learning and statistical methods for DDoS attack detection and defense system in software defined networks

2021 ◽  
Author(s):  
Merlin James Rukshan Dennis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Statistical Approach ◽  
Denial Of Service ◽  
Attack Detection ◽  
Learning Approach ◽  
Ddos Attack ◽  
Machine Learning Approach ◽  
Ddos Detection ◽  
Ddos Attack Detection

Distributed Denial of Service (DDoS) attack is a serious threat on today’s Internet. As the traffic across the Internet increases day by day, it is a challenge to distinguish between legitimate and malicious traffic. This thesis proposes two different approaches to build an efficient DDoS attack detection system in the Software Defined Networking environment. SDN is the latest networking approach which implements centralized controller, which is programmable. The central control and the programming capability of the controller are used in this thesis to implement the detection and mitigation mechanisms. In this thesis, two designed approaches, statistical approach and machine-learning approach, are proposed for the DDoS detection. The statistical approach implements entropy computation and flow statistics analysis. It uses the mean and standard deviation of destination entropy, new flow arrival rate, packets per flow and flow duration to compute various thresholds. These thresholds are then used to distinguish normal and attack traffic. The machine learning approach uses Random Forest classifier to detect the DDoS attack. We fine-tune the Random Forest algorithm to make it more accurate in DDoS detection. In particular, we introduce the weighted voting instead of the standard majority voting to improve the accuracy. Our result shows that the proposed machine-learning approach outperforms the statistical approach. Furthermore, it also outperforms other machine-learning approach found in the literature.

An Enhanced Way of Distributed Denial of Service Attack Detection by Applying Machine Learning Algorithms in Cloud Computing

2020 ◽  
Vol 17 (8) ◽  
pp. 3765-3769
Author(s):  
N. P. Ponnuviji ◽  
M. Vigilson Prem
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Denial Of Service ◽  
Learning Algorithms ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Distributed Denial Of Service ◽  
Detection Techniques ◽  
Network Bandwidth ◽  
Ddos Attack

Cloud Computing has revolutionized the Information Technology by allowing the users to use variety number of resources in different applications in a less expensive manner. The resources are allocated to access by providing scalability flexible on-demand access in a virtual manner, reduced maintenance with less infrastructure cost. The majority of resources are handled and managed by the organizations over the internet by using different standards and formats of the networking protocols. Various research and statistics have proved that the available and existing technologies are prone to threats and vulnerabilities in the protocols legacy in the form of bugs that pave way for intrusion in different ways by the attackers. The most common among attacks is the Distributed Denial of Service (DDoS) attack. This attack targets the cloud’s performance and cause serious damage to the entire cloud computing environment. In the DDoS attack scenario, the compromised computers are targeted. The attacks are done by transmitting a large number of packets injected with known and unknown bugs to a server. A huge portion of the network bandwidth of the users’ cloud infrastructure is affected by consuming enormous time of their servers. In this paper, we have proposed a DDoS Attack detection scheme based on Random Forest algorithm to mitigate the DDoS threat. This algorithm is used along with the signature detection techniques and generates a decision tree. This helps in the detection of signature attacks for the DDoS flooding attacks. We have also used other machine learning algorithms and analyzed based on the yielded results.

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