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

Abstract

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.

Time‐based DDoS attack detection through hybrid LSTM‐CNN model architectures: An investigation of many‐to‐one and many‐to‐many approaches

“…These methods use deep belief networks (DBN) and probabilistic neural networks (PNN), 27 or train DBN as a classifier. 28,29 CNNs have also been proposed as a novel network intrusion detection model. 30 Another study investigated an artificial intelligence (AI) intrusion detection system using a DNN and tested it on the KDD Cup 99 data set.…”

“…Research has shown that deep learning has superior performance in image and speech recognition, leading to the proposal of deep learning‐based traffic anomaly detection methods for intrusion detection. These methods use deep belief networks (DBN) and probabilistic neural networks (PNN), 27 or train DBN as a classifier 28,29 . CNNs have also been proposed as a novel network intrusion detection model 30 .…”

An efficient approach to detect distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization

“…The experiments part was conducted using KNN, XGBoost, SVM (RBF), SVM (linear), MLP, Decision Tree (DT), RF, Adaptive Boosting (AdaBoost), Naïve Bayes (NB). Also, Dahiya in Reference [38] introduces a model for identifying DDoS assaults where the overall derived features are processed and selected using the gain ratio technique. This paper uses a hybrid approach combining BiLSTM and an Optimized Deep Belief network (DBN) to detect DDoS threats.…”

“…Also, Dahiya in Reference [38] introduces a model for identifying DDoS assaults where the overall derived features are processed and selected using the gain ratio technique. This paper uses a hybrid approach combining BiLSTM and an Optimized Deep Belief network (DBN) to detect DDoS threats.…”

K‐DDoS‐SDN: A distributed DDoS attacks detection approach for protecting SDN environment

“…A DDoS attack is a significant menace for Internet-based systems, web services, devices (IoT and non-IoT), and traditional and modern types of networks, such as SDN. [5][6][7] The attackers send a massive amount of network traffic to the engaging resources of the victim. The impact of this type of attack can be victim networks or services down for a long time.…”