A DDoS Attack Mitigation Framework for IoT Networks using Fog Computing

Lawal, Muhammad Aminu; Shaikh, Riaz Ahmed; Hassan, Syed Raheel

doi:10.1016/j.procs.2021.02.003

Cited by 25 publications

(8 citation statements)

References 16 publications

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“…In 2021, Lawal et al 24 proposed a DDoS alleviation model for IoT by fog computing that ensured accurate and fast attack recognition. The fog provided resources for effectual employment of alleviation model; this solved the resource deficiency for resource‐based IoT devices.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid classifier strategy with tuned training weights for distributed denial of service attack detection

Dahiya

2022

Concurrency and Computation

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Summary The 5G wireless networks associated with higher data‐transferring speeds considerably affect the performance of IoT networks. Nowadays, the Internet has become a very significant aspect of human lives, and it aids in data transfers, processing, and storing. However, 5G networks are subjected to varied cyber security attacks, which are hard to detect. As a result, it is required to set up attack detection models that can recognize 5G network's distributed denial of service (DDoS) attack. Thereby, this article aims to introduce a new model for detecting DDoS attacks. Initially, from input data, features such as statistical features, improved exponential moving average, higher order statistical features, MI, and improved correlation based features are derived. Further, a gain ratio ranking model is used for picking fine features from the overall derived features. Finally, at the detection stage, bidirectional long short‐term memory as well as optimized deep belief network (DBN) are introduced that portray the detected results in a precise way. DBN weights get fine‐tuned by the combined shark smell and electric fish model with new distance based active eco‐location model. Finally, the created approach's advantages are demonstrated using a variety of metrics, including precision, accuracy, F‐measure, and others. In particular, the performance metrics of the proposed work at node = 6000 are accuracy = 98.59%, sensitivity = 96.82%, specificity = 97.52%, precision = 98.91%, F‐measure = 97.86%, MCC = 92.19%, NPV = 97.24%, FPR = 2.47%, and FNR = 3.17%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid classifier strategy with tuned training weights for distributed denial of service attack detection

Dahiya

2022

Concurrency and Computation

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“…e result of this study shows that DT classifier is more successful that the other two classifiers. Many such related research works can also be found in [19][20][21][21][22][23][24][25][26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

DI-ADS: A Deep Intelligent Distributed Denial of Service Attack Detection Scheme for Fog-Based IoT Applications

Gudla

Bhoi

Nayak

et al. 2022

Mathematical Problems in Engineering

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Nowadays, fog computing plays a very vital role in providing many services to end-based IoT (Internet of Things) systems. The end IoT devices communicate with the middle layer fog nodes and to the above cloud layer to process the user tasks. However, this large data communication experiences many security challenges as IoT devices are being compromised and thus the fog nodes at the fog layer are more prone to a very critical attack known as Distributed Denial of Service (DDoS) attack. The attackers or the compromised IoT devices need to be detected well in the network. Deep Learning (DL) plays a prominent role in predicting the end-user behavior by extracting features and classifying the adversary in the network. But, due to IoT device’s constrained nature in computation and storage facilities, DL cannot be administered on those. In this paper, a deep intelligent DDoS attack detection scheme (DI-ADS) is proposed for fog-based IoT applications. The framework mainly uses a deep learning model (DLM) to detect DDoS attacks in the network. The DLM is installed on the computation module of the fog node that predicts the end IoT device behavior. For the selection of the best DLM model at the fog layer, the performance comparison is made on Deep Neural Multilayer Perceptron (DNMLP) and Long Short-Term Memory (LSTM) models along with the conventional machine learning (ML) models such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Logistic Regression (LR), and Random Forest (RF). The simulation is performed using the Python Anaconda platform by considering a new DDoS-SDN (Mendeley Dataset) dataset that consists of three DDoS attacks such as TCP Syn, UDP Flood, and ICMP attacks. From the results, DNMLP showed the best accuracy of 99.44% as compared to other DL and ML models. By outperforming nature in the detection of DDoS attacks, DNMLP is considered in the proposed framework for being implemented at the fog layer.

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“…We can use the distance calculator or use the cosine formula. The distance calculation mainly uses Euclidean distance function 80 . It is an easily human‐perceivable distance calculator compared to Manhattan, Minkowski, and hamming distance.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Performance evaluation of machine learning models for distributed denial of service attack detection using improved feature selection and hyper‐parameter optimization techniques

Habib

Khursheed

2022

Concurrency and Computation

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This article gives the framework of extensive experimentation of various machine learning models to detect distributed denial of service attacks (DDoS). We use six-tier feature ranking methods that use statistical techniques as well as machine learning based classifiers to obtain the significant features. The measurable statistical based feature selection involves Chi-Square (Chi2), information gain (IG), merged Chi-Square (Chi2)-IG ranking and machine learning classifiers involve ensemble classifiers, that is, decision tree, random forest and eXtreme gradient boosting (XGBoost). Different supervised machine learning models (logistic regression, decision tree classifier, linear support vector machine, k-nearest neighbors, Gaussian Naive Bayes, random forest classifier, XGBoost) are trained on a feature-engineered datasets. To further our research, we use neural networks (ANN and CNN) using both feature-selected and auto-feature selection training setup. We check the validation and adaptability of these models with the optimal tuning of various parameters using GridSearchCV and the effectiveness of random sampling in overcoming the class imbalance problem. Based on various feature selection methods, the models are evaluated for their best performance. The experimental results show that our trained machine learning models and neural networks outperformed the ones in the state of art. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate, sensitivity, specificity, false-positive rate, F1 score, area under curve analysis and loss functions on well-known KDD Cup 99 and UNSW-NB15 datasets. This study is significant for furthering the research in DDoS detection with machine learning and deep neural networks.

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A DDoS Attack Mitigation Framework for IoT Networks using Fog Computing

Cited by 25 publications

References 16 publications

Hybrid classifier strategy with tuned training weights for distributed denial of service attack detection

Hybrid classifier strategy with tuned training weights for distributed denial of service attack detection

DI-ADS: A Deep Intelligent Distributed Denial of Service Attack Detection Scheme for Fog-Based IoT Applications

Performance evaluation of machine learning models for distributed denial of service attack detection using improved feature selection and hyper‐parameter optimization techniques

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