FWFS: Selecting Robust Features Towards Reliable and Stable Traffic Classifier in SDN

Zaki, Fatimah Audah Md.; Chin, Tan Saw

doi:10.1109/access.2019.2953565

Cited by 17 publications

(5 citation statements)

References 39 publications

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“…It is based on the principle that multiple models working together often achieve better performance than a single model alone. In [17,24,27], the authors proposed hybrid feature-selection methods to enhance the DDoS-detection systems. Selecting features according to the Information Gain technique was also evaluated in [18,19] to deal with DDoS attacks.…”

Section: Comprehensive Overviewmentioning

confidence: 99%

Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment

Setitra,

Fan,

Agbley

et al. 2023

Network

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In the contemporary landscape, Distributed Denial of Service (DDoS) attacks have emerged as an exceedingly pernicious threat, particularly in the context of network management centered around technologies like Software-Defined Networking (SDN). With the increasing intricacy and sophistication of DDoS attacks, the need for effective countermeasures has led to the adoption of Machine Learning (ML) techniques. Nevertheless, despite substantial advancements in this field, challenges persist, adversely affecting the accuracy of ML-based DDoS-detection systems. This article introduces a model designed to detect DDoS attacks. This model leverages a combination of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) to enhance the performance of ML-based DDoS-detection systems within SDN environments. We propose utilizing the SHapley Additive exPlanations (SHAP) feature-selection technique and employing a Bayesian optimizer for hyperparameter tuning to optimize our model. To further solidify the relevance of our approach within SDN environments, we evaluate our model by using an open-source SDN dataset known as InSDN. Furthermore, we apply our model to the CICDDoS-2019 dataset. Our experimental results highlight a remarkable overall accuracy of 99.95% with CICDDoS-2019 and an impressive 99.98% accuracy with the InSDN dataset. These outcomes underscore the effectiveness of our proposed DDoS-detection model within SDN environments compared to existing techniques.

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Section: Comprehensive Overviewmentioning

confidence: 99%

Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment

Setitra,

Fan,

Agbley

et al. 2023

Network

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“…For example, Mohanty et al [23] proposed a robust stacking ensemble model to combine the predictions of RF, KNN, and DT for darknet traffic classification, achieving accuracy rates of 98.89% and 97.88% for darknet traffic identification and characterization, respectively. Zaki et al [24] proposed a hybrid feature selection algorithm based on the filter and wrapper method. Their method was evaluated using DT, KNN, naive Bayes (NB), and support vector machine (SVM), with average accuracy of 98.90%.…”

Section: Ml-based Network Traffic Classificationmentioning

confidence: 99%

Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables

Adeke,

Liu,

Zhao

et al. 2023

Future Internet

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Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training is an effective defense method against such attacks but relies on access to a substantial number of AEs, a prerequisite that entails significant computational resources and the inherent limitation of poor performance on clean data. To address these problems, this study proposes a novel approach to improve the robustness of ML-based network traffic classification models by integrating derived variables (DVars) into training. Unlike adversarial training, our approach focuses on enhancing training using DVars, introducing randomness into the input data. DVars are generated from the baseline dataset and significantly improve the resilience of the model to AEs. To evaluate the effectiveness of DVars, experiments were conducted using the CSE-CIC-IDS2018 dataset and three state-of-the-art ML-based models: decision tree (DT), random forest (RF), and k-neighbors (KNN). The results show that DVars can improve the accuracy of KNN under attack from 0.45% to 0.84% for low-intensity attacks and from 0.32% to 0.66% for high-intensity attacks. Furthermore, both DT and RF achieve a significant increase in accuracy when subjected to attack of different intensity. Moreover, DVars are computationally efficient, scalable, and do not require access to AEs.

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“…To avoid congestion at the SDN controller, we perform traffic scheduling in the 5G AP. In this step, traffic flows from devices are classified and scheduled using an asymmetric queue model that operates based on Bernoulli's theorem [45,46]. To schedule traffic flow, we use three parameters: data rate , packet delay , and packet length .…”

Section: Traffic Schedulingmentioning

confidence: 99%

T-S3RA: Traffic-Aware Scheduling for Secure Slicing and Resource Allocation in SDN/NFVEnabled 5G Networks

Ramadhan¹

2021

IJETT

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Network slicing and resource allocation play pivotal roles in software-defined network (SDN)/network function virtualization (NFV)-assisted 5G networks. In 5G communications, the traffic rate is high, necessitating high data rates and low latency. Deep learning is a potential solution for overcoming these constraints. Secure slicing avoids resource wastage; however, DDoS attackers can exploit the sliced network. Therefore, we focused on secure slicing with resource allocation under massive network traffic. Traffic-aware scheduling is proposed for secure slicing and resource allocation over SDN/NFV-enabled 5G networks. In this approach (T-S3RA), user devices are authenticated using Boolean logic with a password-based key derivation function. The traffic is scheduled in 5G access points, and secure network slicing and resource allocation are implemented using deep learning models such as SliceNet and HopFieldNet, respectively. To predict DDoS attackers, we computed the Renyi entropy for packet classification. Experiments were conducted using a network simulator with 250 nodes in the network topology. Performance was evaluated using metrics such as throughput, latency, packet transmission ratio, packet loss ratio, slice capacity, bandwidth consumption, and slice acceptance ratio. T-S3RA was implemented in three 5G use cases with different requirements, including massive machine-type communication, ultra-reliable low-latency communication, and enhanced mobile broadband.

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FWFS: Selecting Robust Features Towards Reliable and Stable Traffic Classifier in SDN

Cited by 17 publications

References 39 publications

Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment

Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment

Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables

T-S3RA: Traffic-Aware Scheduling for Secure Slicing and Resource Allocation in SDN/NFVEnabled 5G Networks

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