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The Internet of Things (IoT) has emerged as an inevitable part of human life, that includes online learning, smart homes, smart cars, smart grids, smart cities, agriculture, and e-healthcare. It allows us to operate them 24/7 from anywhere. These smart IoT devices streamline our daily lives by automating everything around us. Several security issues have arisen with the continuous growth of non-secure IoT devices. Distributed Denial of Service (DDoS) attack is one of the most prominent security threats to Internet-based services and IoT platforms. It has the potential to break down the victim's server or network by transferring an immense amount of irrelevant traffic from the pool of compromised IoT devices. In this article, we present: (i) A comprehensive cyberattacks taxonomy for IoT platforms, (ii) Systematically demonstrate IoT technology: evolution, applications, and challenges, (iv) Systematic review of existing machine learning (ML) and deep learning (DL)-based detection approaches for large-scale IoT traffic-based DDoS attacks, (v) Characterize publicly available IoT-traffic-specific datasets, and (vi) Discuss various open research issues with possible solutions for detecting IoT traffic-based DDoS attacks, including future directions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has achieved exponential growth worldwide. Although the IoT is used by millions of users, these networks are handicapped by attacks such as denial of service, man in the middle, and spoofing. These attacks threaten the entire IoT ecosystem and affect the integrity and security of the user. Hence, the prediction and identification of novel network attacks in an IoT network remains a challenge for researchers. Recently, machine learning and deep learning have played a pivotal role in predicting and classifying different attacks in an IoT network. However, these algorithms suffer from computational complexity as the number of attacks increases. Hence, a novel hybrid optimized long short-term memory (LSTM) approach is proposed. Whereas a convolutional neural network is used to extract the temporal and spatial correlated features of the IoT network, the optimized LSTM is used to predict the different attacks in the network. Furthermore, firefly swarm optimization is integrated with LSTM to reduce the computational overhead, which in turn increases the prediction accuracy. Nearly 19,00,503 real-time normal and attack data were collected from the experimental simulation setup based on the OMNET++–Python–IoT framework. Extensive experimentation was carried out to evaluate the proposed algorithm, and various metrics, such as accuracy, sensitivity, specificity, and F1-score, were calculated and compared with state-of-the-art learning-based network intrusion detection systems. Furthermore, other benchmarks, such as the CIDCC-15, UNSW-NB15, and NSL-KDD datasets, were used to evaluate the performance of the different deep-learning-based intrusion detection systems. The results demonstrate that the proposed deep-learning method outperforms other classical learning models with low complexity and high prediction performance. [ABSTRACT FROM AUTHOR]

Service availability plays a vital role on computer networks, against which Distributed Denial of Service (DDoS) attacks are an increasingly growing threat each year. Machine learning (ML) is a promising approach widely used for DDoS detection, which obtains satisfactory results for pre-known attacks. However, they are almost incapable of detecting unknown malicious traffic. This paper proposes a novel method combining both supervised and unsupervised algorithms. First, a clustering algorithm separates the anomalous traffic from the normal data using several flow-based features. Then, using certain statistical measures, a classification algorithm is used to label the clusters. Employing a big data processing framework, we evaluate the proposed method by training on the CICIDS2017 dataset and testing on a different set of attacks provided in the more up-to-date CICDDoS2019. The results demonstrate that the Positive Likelihood Ratio (LR+) of our method is approximately 198% higher than the ML classification algorithms. [ABSTRACT FROM AUTHOR]

This research presents the detection and mitigation of distributed denial of service (DDoS) in software defined networks (SDN). The proposed method consists of three modules: classifier module, mitigation module, and collaborative module. An ensemble classifier called V-NKDE is capable of detecting DDoS attacks accurately. The mitigation module blocks malicious traffics and purges entries of malicious traffic from the switch flow table. The collaborative module shares DDoS detection and mitigation rules among multiple SDN controllers using Redis Simple Message Queue mechanism. The proposed classifier performance validation on InSDN2020, CICIDS2017, NSL-KDD and UNSW-NB15 datasets. Furthermore we evaluated our proposed classifier in real traffic on an SDN simulation tested. The results show that the proposed method can detect DDoS attacks with high accuracy using an ensemble classifier, which performs better than single classifiers. More importantly, the false positive rate is greatly reduced, showing detection and mitigation of DDoS attacks across multi-controller domains with low controller overhead. [ABSTRACT FROM AUTHOR]

The distributed denial-of-service (DDoS) attack is a security challenge for the software-defined network (SDN). The different limitations of the existing DDoS detection methods include the dependency on the network topology, not being able to detect all DDoS attacks, applying outdated and invalid datasets and the need for powerful and costly hardware infrastructure. Applying static thresholds and their dependency on old data in previous periods reduces their flexibility for new attacks and increases the attack detection time. A new method detects DDoS attacks in SDN. This method consists of the three collector, entropy-based and classification sections. The experimental results obtained by applying the UNB-ISCX, CTU-13 and ISOT datasets indicate that this method outperforms its counterparts in terms of accuracy in detecting DDoS attacks in SDN. [ABSTRACT FROM AUTHOR]