Two-Layer Approach for Mixed High-Rate and Low-Rate Distributed Denial of Service (DDoS) Attack Detection and Filtering

2018 ◽  
Vol 43 (12) ◽  
pp. 7923-7931 ◽  
Author(s):  
S. Toklu ◽  
M. Şimşek
Keyword(s):  
Denial Of Service ◽  
Attack Detection ◽  
High Rate ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
Ddos Attack Detection ◽  
Low Rate

scholarly journals Information Theory-based Approaches to Detect DDoS Attacks on Software-defined Networking Controller a Review

2021 ◽  
Vol 15 ◽  
pp. 83-94
Author(s):  
Mohammad A. Aladaileh ◽  
Mohammed Anbar ◽  
Iznan H. Hasbullah ◽  
Yousef K. Sanjalawe
Keyword(s):  
Information Theory ◽  
Flow Behavior ◽  
Denial Of Service ◽  
Software Defined Networking ◽  
Attack Detection ◽  
High Rate ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Ddos Attack Detection ◽  
Low Rate

The number of network users and devices has exponentially increased in the last few decades, giving rise to sophisticated security threats while processing users’ and devices’ network data. Software-Defined Networking (SDN) introduces many new features, but none is more revolutionary than separating the control plane from the data plane. The separation helps DDoS attack detection mechanisms by introducing novel features and functionalities. Since the controller is the most critical part of the SDN network, its ability to control and monitor network traffic flow behavior ensures the network functions properly and smoothly. However, the controller’s importance to the SDN network makes it an attractive target for attackers. Distributed Denial of Service (DDoS) attack is one of the major threats to network security. This paper presents a comprehensive review of information theory-based approaches to detect low-rate and high-rate DDoS attacks on SDN controllers. Additionally, this paper provides a qualitative comparison between this work and the existing reviews on DDoS attack detection approaches using various metrics to highlight this work’s uniqueness. Moreover, this paper provides in-depth discussion and insight into the existing DDoS attack detection approaches to point out their weaknesses that open the avenue for future research directions. Meanwhile, the finding of this paper can be used by other researchers to propose a new or enhanced approach to protect SDN controllers from the threats of DDoS attacks by accurately detecting both low-rate and high-rate DDoS attacks.

Detecting DDoS Attacks on Multiple Network Hosts

2019 ◽  
pp. 121-139 ◽  
Author(s):  
Konstantinos F. Xylogiannopoulos ◽  
Panagiotis Karampelas ◽  
Reda Alhajj
Keyword(s):  
Denial Of Service ◽  
Attack Detection ◽  
Detection Methods ◽  
Distributed Denial Of Service ◽  
Secondary Sources ◽  
Ddos Attack ◽  
Timely Fashion ◽  
Multiple Network ◽  
Ddos Attack Detection ◽  
Low Traffic

The proliferation of low security internet of things devices has widened the range of weapons that malevolent users can utilize in order to attack legitimate services in new ways. In the recent years, apart from very large volumetric distributed denial of service attacks, low and slow attacks initiated from intelligent bot networks have been detected to target multiple hosts in a network in a timely fashion. However, even if the attacks seem to be “innocent” at the beginning, they generate huge traffic in the network without practically been detected by the traditional DDoS attack detection methods. In this chapter, an advanced pattern detection method is presented that is able to collect and classify in real time all the incoming traffic and detect a developing slow and low DDoS attack by monitoring the traffic in all the hosts of the network. The experimental analysis on a real dataset provides useful insights about the effectiveness of the method by identifying not only the main source of attack but also secondary sources that produce low traffic, targeting though multiple hosts.

scholarly journals Detecting High and Low Intensity Distributed Denial of Service (DDoS) Attacks

2021 ◽  
Author(s):  
◽  
Abigail Koay
Keyword(s):  
Information Sharing ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Traffic Classification ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Low Intensity ◽  
Ddos Attack ◽  
Ddos Attack Detection

<p>High and low-intensity attacks are two common Distributed Denial of Service (DDoS) attacks that disrupt Internet users and their daily operations. Detecting these attacks is important to ensure that communication, business operations, and education facilities can run smoothly. Many DDoS attack detection systems have been proposed in the past but still lack performance, scalability, and information sharing ability to detect both high and low-intensity DDoS attacks accurately and early. To combat these issues, this thesis studies the use of Software-Defined Networking technology, entropy-based features, and machine learning classifiers to develop three useful components, namely a good system architecture, a useful set of features, and an accurate and generalised traffic classification scheme. The findings from the experimental analysis and evaluation results of the three components provide important insights for researchers to improve the overall performance, scalability, and information sharing ability for building an accurate and early DDoS attack detection system.</p>

scholarly journals Improving Distributed Denial of Service (DDOS) Detection using Entropy Method in Software Defined Network (SDN)

2017 ◽  
Vol 8 (4) ◽  
pp. 215
Author(s):  
Maman Abdurohman ◽  
Dani Prasetiawan ◽  
Fazmah Arif Yulianto
Keyword(s):  
Denial Of Service ◽  
Entropy Method ◽  
Attack Detection ◽  
New Method ◽  
Sudden Increase ◽  
Software Defined Network ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
Ddos Detection ◽  
Ddos Attack Detection

This research proposed a new method to enhance Distributed Denial of Service (DDoS) detection attack on Software Defined Network (SDN) environment. This research utilized the OpenFlow controller of SDN for DDoS attack detection using modified method and regarding entropy value. The new method would check whether the traffic was a normal traffic or DDoS attack by measuring the randomness of the packets. This method consisted of two steps, detecting attack and checking the entropy. The result shows that the new method can reduce false positive when there is a temporary and sudden increase in normal traffic. The new method succeeds in not detecting this as a DDoS attack. Compared to previous methods, this proposed method can enhance DDoS attack detection on SDN environment.

scholarly journals Comparative Study on TCP SYN Flood DDoS Attack Detection: A Machine Learning Algorithm Based Approach

2021 ◽  
Vol 16 ◽  
pp. 584-591
Author(s):  
S. Sumathi ◽  
R. Rajesh
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Algorithm ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
Research Goal ◽  
Ddos Attack Detection ◽  
F Measure

A most common attack on the internet network is a Distributed Denial of Service (DDoS) attack, which involves occupying computational resources and bandwidth to suppress services to potential clients. The attack scenario is to massively flood the packets. The attack is called a denial of service (DoS) if the attack originates from a single server, and a distributed denial of service (DDoS) if the attack originates from multiple servers. Control and mitigation of DDoS attacks have been a research goal for many scholars for over a decade, and they have achieved in delivering a few major DDoS detection and protection techniques. In the current state of internet use, how quickly and early a DDoS attack can be detected in broadcasting network transactions remains a key research goal. After the development of a machine learning algorithm, many potential methods of DDoS attack detection have been developed. The work presents the results of various experiments carried out using data mining and machine learning algorithms as well as a combination of these algorithms on the commonly available dataset named CAIDA for TCP SYN flood attack detection. Also, this work analysis the various performance metrics such as false positive rate, precision, recall, F-measure and receiver operating characteristic (ROC) using various machine learning algorithm. One-R(OR) with an ideal FPR value of 0.05 and recall value of 0.95,decision stump(DS) with an ideal precision value of o.93,PART with an excellent F-measure value of 0.91 are some of the performance metric values while performing TCP SYN flood attack detection.

scholarly journals On the use of information theory metrics for detecting DDoS attacks and flash events: an empirical analysis, comparison, and future directions

2021 ◽  
Vol 48 (4) ◽  
Author(s):  
Jagdeep Singh ◽  
◽  
Navjot Jyoti ◽  
Sunny Behal ◽  
◽  
...  
Keyword(s):  
Information Theory ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
High Rate ◽  
Ddos Attacks ◽  
Operating Characteristics ◽  
Classification Rate ◽  
Ddos Attack ◽  
Ddos Attack Detection

A Distributed Denial of Service (DDoS) attack is one of the lethal threats that can cripple down the computing and communication resources of a web server hosting Internet-based services and applications. It has motivated the researchers over the years to find diversified and robust solutions to combat against DDoS attacks and characterization of flash events (a sudden surge in the legitimate traffic) from HR-DDoS (High-Rate DDoS) attacks. In recent times, the volume of legitimate traffic has also magnified manifolds. It results in behavioral similarities of attack traffic and legitimate traffic that make it very difficult and crucial to differentiate between the two. Predominantly, Netflow-based techniques are in use for detecting and differentiating legitimate and attack traffic flows. Over the last decade, fellow researchers have extensively used distinct information theory metrics for Netflow-based DDoS defense solutions. However, a comprehensive analysis and comparison of these diversified information theory metrics used for particularly DDoS attack detection are needed for a better understanding of the defense systems based on information theory. This paper elucidates the efficacy and effectiveness of information theory-based various entropy and divergence measures in the field of DDoS attack detection. As part of the work, a generalized NetFlow-based methodology has been proposed. The proposed detection methodology has been validated using the traffic traces of various real benchmarked datasets on a set of detection system evaluation metrics such as Detection rate (Recall), Precision, F-Measure, FPR, Classification rate, and Receiver-Operating Characteristics (ROC) curves. It has concluded that generalized divergence-based information theory metrics produce more accuracy in detecting different types of attack flows in contrast to entropy-based information theory metrics.

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