DDoS Detection System: Using a Set of Classification Algorithms Controlled by Fuzzy Logic System in Apache Spark

Alsirhani, Amjad; Sampalli, Srinivas; Bodorik, Peter

doi:10.1109/tnsm.2019.2929425

Cited by 73 publications

(23 citation statements)

References 33 publications

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“…However, the proposed work must be validated with recent real traffic datasets. Alsirhani et al [121] proposed a dynamic DDoS attack detection system consisting of a classification algorithm, a distributed system, and a fuzzy logic system. The proposed system uses fuzzy logic to choose the most suitable algorithm from among a list of existing classification algorithms capable of identifying various DDoS attack patterns.…”

Section: A Ddos Defense Systems Based On ML Techniques In Cloud Compmentioning

confidence: 99%

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani

2021

IEEE Access

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Section: A Ddos Defense Systems Based On ML Techniques In Cloud Compmentioning

confidence: 99%

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Aljuhani

2021

IEEE Access

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“…Fuzzy logic has also been used for detection of highrate DDoS attacks. Balarengadurai and Saraswathi in [72] have used fuzzy logic for detection of exhaustion attacks over IEEE 802.15.4 MAC Layer, Rodrìguez et al have reported in [73] a dynamic DDoS mitigation scheme based on TTL field and fuzzy logic, Mondal et al in [74] have used fuzzy logic for detection of DDoS in cloud computing environments, and Alsirhani et al have proposed in [75] a DDoS detection system using a set of classification algorithms (Naive Bayes, Decision Tree (Entropy), Decision Tree (Gini), and Random Forest) controlled by a fuzzy logic system in Apache Spark.…”

Section: Use Of Machine Learning Algorithms and Fuzzy Logic For Detection Of Ddos Attacksmentioning

confidence: 99%

Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms

et al. 2021

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Distributed Denial of Service (DDoS) attacks are still among the most dangerous attacks on the Internet. With the advance of methods for detecting and mitigating these attacks, crackers have improved their skills in creating new DDoS attack types with the aim of mimicking normal traffic behaviour therefore becoming silently powerful. Among these advanced DDoS attack types, the so-called lowrate DoS attacks aim at keeping a low level of network traffic. In this paper, we study one of these techniques, called Reduction of Quality (RoQ) attack. To investigate the detection of this type of attack, we evaluate and compare the use of four machine learning algorithms: Multi-Layer Perceptron (MLP) neural network with backpropagation, K-Nearest Neighbors (K-NN), Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB). We also propose an approach for detecting this kind of attack based on three methods: Fuzzy Logic (FL), MLP and Euclidean Distance (ED). We evaluate and compare the approach based on FL, MLP and ED to the above machine learning algorithms using both emulated and real traffic traces. We show that among the four Machine Learning algorithms, the best classification results are obtained with MLP, which, for emulated traffic, leads to a F1-score of 98.04% for attack traffic and 99.30% for legitimate traffic, while, for real traffic, it leads to a F1-score of 99.87% for attack traffic and 99.95% for legitimate traffic. Regarding the approach using FL, MLP and EC, for classification of emulated traffic, we obtained a F1-score of 98.80% for attack traffic and 99.60% for legitimate traffic, while, for real traffic, we obtained a F1-score of 100% for attack traffic and 100% for legitimate traffic. However, the better performance of the approach based on FL, MLP and ED is obtained at the cost of larger execution time, since MLP required 0.74 ms and 0.87ms for classification of the emulated and real traffic datasets, respectively, where as the approach using FL, MLP and ED required 11'46" and 46'48" to classify the emulated and real traffic datasets, respectively.

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“…Hoque et al [47] used a novel correlation measure to detect DDoS attacks; further, field programmable gate arrays (FPGA) devices classify an incoming traffic as attack traffic or normal traffic in real-time. Alsirhani et al [48] proposed a dynamic DDoS attack detection system composed of three components: a set of classification algorithms, a distributed system, and a fuzzy logic system. Classification algorithms are used to identify DDoS attacks, a distributed system (i.e., Apache Spark) accelerates the enforcement of these classification algorithms, and the fuzzy logic could dynamically select the right classification algorithm from a set of classification algorithms to detect DDoS attacks.…”

Section: E Proposed Collaborative Architectures Of Network Defense Deploymentmentioning

confidence: 99%

Stability of SDE-LJN System in the Internet to Mitigate Constant-Rate DDoS Attacks

Huang

Tan

Peng

2021

Security and Communication Networks

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The Internet is nowadays suffering dramatically serious attacks, with the distributed denial of service (DDoS) attacks being the representative and dominant ones. It is seen that, to stabilize the buffer queue length around a given target under DDoS attacks in the relevant routes is vitally important and helpful to mitigate the attacks and to improve the quality of service (QoS) for normal users. In the current paper, a stochastic queue dynamic model with L e ´ vy jump noise, which is affected by the continuous Brownian motion and the discontinuous Poisson process, is worked out to develop a novel and accurate mathematical framework for the stability of a route queue that deals with constant-rate DDoS attacks. This article proposes a security defensive mechanism in the network for solving the network collapse that can possibly be caused by DDoS attacks, otherwise. Particularly, based on the formulation of a stochastic queue dynamic with L e ´ vy jump noise, the mechanism that characterizes the behavior of the queue at routers is presented for stabilizing the queue length under constant-rate DDoS attacks. By applying the stochastic control theory into analyzing the performance of queue dynamic under constant-rate DDoS attacks, some explicit conditions are established under which the instantaneous queue length converges to any given target in a route. Simulation results demonstrate the satisfaction of the proposed defense mechanism with sharp contrast to the state of the art active queue management (AQM) schemes.

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DDoS Detection System: Using a Set of Classification Algorithms Controlled by Fuzzy Logic System in Apache Spark

Cited by 73 publications

References 33 publications

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms

Stability of SDE-LJN System in the Internet to Mitigate Constant-Rate DDoS Attacks

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