scholarly journals Bit-and-Piece DDoS attack Detection based on the Statistical Metrics

2019 ◽  
Vol 9 (1S4) ◽  
pp. 48-55
Keyword(s):  
Service Providers ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Communication Service ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Novel Approach ◽  
Data Flows ◽  
Ddos Attack Detection ◽  
Statistical Metrics

on each successive day, the DDoS attacks are increasing, improving and becoming more critical than ever before. In 2018, CISCO predicted that DDoS attack traffics may reach to 3.1 billion during 2021. Bit and Piece DDoS attack is an emerging attacking technique was found and reported by Nexusguard. This attack mainly targets the communication service providers and it injects unwanted junk information in to the legitimate traffic and thus bypasses the detection techniques. This work is aimed to propose a novel approach for detecting bit and piece attack using statistical metrics. Here, the packet flow is monitored at every second and the variations in the data flows easily identified as an attack.

scholarly journals Adaptive DDoS Attack Detection Method Based on Multiple-Kernel Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Jieren Cheng ◽  
Chen Zhang ◽  
Xiangyan Tang ◽  
Victor S. Sheng ◽  
Zhe Dong ◽  
...  
Keyword(s):  
Detection Method ◽  
Denial Of Service ◽  
Multiple Kernel Learning ◽  
Attack Detection ◽  
Kernel Learning ◽  
Economic Losses ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Multiple Kernel ◽  
Ddos Attack Detection

Distributed denial of service (DDoS) attacks has caused huge economic losses to society. They have become one of the main threats to Internet security. Most of the current detection methods based on a single feature and fixed model parameters cannot effectively detect early DDoS attacks in cloud and big data environment. In this paper, an adaptive DDoS attack detection method (ADADM) based on multiple-kernel learning (MKL) is proposed. Based on the burstiness of DDoS attack flow, the distribution of addresses, and the interactivity of communication, we define five features to describe the network flow characteristic. Based on the ensemble learning framework, the weight of each dimension is adaptively adjusted by increasing the interclass mean with a gradient ascent and reducing the intraclass variance with a gradient descent, and the classifier is established to identify an early DDoS attack by training simple multiple-kernel learning (SMKL) models with two characteristics including interclass mean squared difference growth (M-SMKL) and intraclass variance descent (S-SMKL). The sliding window mechanism is used to coordinate the S-SMKL and M-SMKL to detect the early DDoS attack. The experimental results indicate that this method can detect DDoS attacks early and accurately.

scholarly journals A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Jia ◽  
Xiaohong Huang ◽  
Rujun Liu ◽  
Yan Ma
Keyword(s):  
Ensemble Learning ◽  
Detection Method ◽  
Detection System ◽  
Detection Algorithm ◽  
Attack Detection ◽  
True Negative ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Accuracy And Precision ◽  
Ddos Attack Detection

The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR), accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD) to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, k-Nearest Neighbor (k-NN), and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.

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 Privacy-Preserving Ddos Attack Detection using Cross-Domain: Challenges and Research

2019 ◽  
Vol 9 (1) ◽  
pp. 2373-2382
Keyword(s):  
Intrusion Detection ◽  
Privacy Preserving ◽  
Attack Detection ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Cross Domain ◽  
Ddos Attack Detection

In this paper, we present a review of on hand IDS (Intrusion Detection Techniques) for DDoS assaults. Interruption discovery framework is a well known and computationally costly task. We additionally clarify the essentials of interruption identification framework. We represent the present methodologies for Intrusion Detection framework. From the expansive assortment of proficient procedures that have been created we will look at the most significant ones. Their qualities and shortcomings are likewise researched. For reasons unknown, the conduct of the calculations is substantially more comparative as not out of the ordinary.

scholarly journals DDoS detection and prevention based on artificial intelligence techniques

2019 ◽  
Vol XXII (1) ◽  
pp. 134-143
Author(s):  
Glăvan D.
Keyword(s):  
Artificial Intelligence ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Ddos Attacks ◽  
Great Loss ◽  
Artificial Intelligence Techniques ◽  
Ddos Attack ◽  
Random Forest Tree ◽  
Ddos Attack Detection

Distributed Denial of Service (DDoS) attacks have been the major threats for the Internet and can bring great loss to companies and governments. With the development of emerging technologies, such as cloud computing, Internet of Things (IoT), artificial intelligence techniques, attackers can launch a huge volume of DDoS attacks with a lower cost, and it is much harder to detect and prevent DDoS attacks, because DDoS traffic is similar to normal traffic. Some artificial intelligence techniques like machine learning algorithms have been used to classify DDoS attack traffic and detect DDoS attacks, such as Naive Bayes and Random forest tree. In the paper, we survey on the latest progress on the DDoS attack detection using artificial intelligence techniques and give recommendations on artificial intelligence techniques to be used in DDoS attack detection and prevention.

scholarly journals IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks

2021 ◽  
Vol 19 (2) ◽  
pp. 1280-1303
Author(s):  
Jiushuang Wang ◽  
◽  
Ying Liu ◽  
Huifen Feng
Keyword(s):  
Internet Of Things ◽  
Firefly Algorithm ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Detection Scheme ◽  
Ddos Attack ◽  
Improved Firefly Algorithm ◽  
Iot Devices ◽  
Ddos Attack Detection

<abstract><p>Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recent numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). Therefore, we propose a DDoS attack detection scheme to secure the real-time in the software-defined the internet of things (SD-IoT) environment. In this article, we utilize improved firefly algorithm to optimize the convolutional neural network (CNN), to provide detection for DDoS attacks in our proposed SD-IoT framework. Our results demonstrate that our scheme can achieve higher than 99% DDoS behavior and benign traffic detection accuracy.</p></abstract>

scholarly journals DDoS attack detection using deep learning

2021 ◽  
Vol 10 (2) ◽  
pp. 382
Author(s):  
Thapanarath Khempetch ◽  
Pongpisit Wuttidittachotti
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Attack Surface ◽  
Iot Devices ◽  
Ddos Attack Detection ◽  
Flow Of Information

<span id="docs-internal-guid-58e12f40-7fff-ea30-01f6-fbbed132b03c"><span>Nowadays, IoT devices are widely used both in daily life and in corporate and industrial environments. The use of these devices has increased dramatically and by 2030 it is estimated that their usage will rise to 125 billion devices causing enormous flow of information. It is likely that it will also increase distributed denial-of-service (DDoS) attack surface. As IoT devices have limited resources, it is impossible to add additional security structures to it. Therefore, the risk of DDoS attacks by malicious people who can take control of IoT devices, remain extremely high. In this paper, we use the CICDDoS2019 dataset as a dataset that has improved the bugs and introducing a new taxonomy for DDoS attacks, including new classification based on flows network. We propose DDoS attack detection using the deep neural network (DNN) and long short-term memory (LSTM) algorithm. Our results show that it can detect more than 99.90% of all three types of DDoS attacks. The results indicate that deep learning is another option for detecting attacks that may cause disruptions in the future.</span></span>

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.

scholarly journals SIEM-based detection and mitigation of IoT-botnet DDoS attacks

2020 ◽  
Vol 10 (2) ◽  
pp. 2182
Author(s):  
Basheer Al-Duwairi ◽  
Wafaa Al-Kahla ◽  
Mhd Ammar AlRefai ◽  
Yazid Abedalqader ◽  
Abdullah Rawash ◽  
...  
Keyword(s):  
Internet Security ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Event Management ◽  
Ddos Attack ◽  
Different Types ◽  
Security Information ◽  
Iot Devices ◽  
Ddos Attack Detection ◽  
The Internet Of Things

The Internet of Things (IoT) is becoming an integral part of our daily life including health, environment, homes, military, etc. The enormous growth of IoT in recent years has attracted hackers to take advantage of their computation and communication capabilities to perform different types of attacks. The major concern is that IoT devices have several vulnerabilities that can be easily exploited to form IoT botnets consisting of millions of IoT devices and posing significant threats to Internet security. In this context, DDoS attacks originating from IoT botnets is a major problem in today’s Internet that requires immediate attention. In this paper, we propose a Security Information and Event Management-based IoT botnet DDoS attack detection and mitigation system. This system detects and blocks DDoS attack traffic from compromised IoT devices by monitoring specific packet types including TCP SYN, ICMP and DNS packets originating from these devices. We discuss a prototype implementation of the proposed system and we demonstrate that SIEM based solutions can be configured to accurately identify and block malicious traffic originating from compromised IoT devices.

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