SDN and application layer DDoS attacks detection in IoT devices by attention‐based Bi‐LSTM‐CNN

Abstract

The Internet of Things (IoT) is connecting more devices every day. Security is critical to ensure that the devices operate in a trusted environment. The lack of proper IoT security encourages cybercriminals to target many smart devices across the network and gain sensitive information. Distributed Denial of Service (DDoS) attacks are common in the IoT infrastructure and involve hijacking IoT devices to consume resources and interrupt services. This may specifically vandalize the application running the service that the end users are trying to access (application layer DDoS attacks) or flood the network bandwidth leading to network failure (software defined network DDoS attacks). This article proposes a hybrid attention‐based bidirectional long short term memory (LSTM) with convolutional neural networks (CNN) to identify DDoS attacks in the application layer and SDN. We deploy several other machine learning models like logistic regression, decision trees, random forests, support vector machines, K‐nearest neighbors, extreme gradient boosting, artificial neural networks, CNN, LSTM, CNN‐LSTM to evaluate the performance of our proposed model. The evaluation metrics considered for the study are accuracy, precision, recall, and F‐1 score. The experimental analysis on multiple datasets exhibits that the proposed model performs the classification efficiently with an accuracy of 99.74% and 99.98%.

LW-PWECC: Cryptographic Framework of Attack Detection and Secure Data Transmission in IoT

“…The results of the proposed ASB-IB and LW-PWECC methods are implemented. Several existing classifiers like Dual CNN [44], LSTM, Spiking Neural Network (SNN), Binarized Spiking Neural Network (BSNN) [46][59] and encryption methods like Identity-Based Encryption (IBE) [60], Advanced Encryption Standard (AES) [63], Rivest-Shamir-Adleman (RSA) [64] and ECC [63] are taken to compare the performance of the introduced approach.…”

A Comprehensive Survey of Distributed Denial of Service Detection and Mitigation Technologies in Software-Defined Network

“…Traditional machine learning SVM [31][32][33][34][35] Decision Tree [36][37][38] KNN [38][39][40][41] Naive Bayes [38,[42][43][44] Random Forest [36][37][38] Deep learning SOM [41,45,46] ANN [47][48][49] LSTM [48][49][50] DNN [51][52][53] RNN [50,53] The SVM algorithm is a binary classification model utilized for distinguishing between normal and abnormal data in the context of DDoS attack detection based on traffic characteristics. Based on the traffic characteristics observed in the SDN network environment, the SVM detection algorithm is employed to gather input feature vectors in order to develop an algorithm for detecting malicious behavior within the network.…”

Anomaly Detection of IoT Cyberattacks in Smart Cities Using Federated Learning and Split Learning

“…A smart city's security breach can have far-reaching consequences, impacting public services, citizen trust, and economic stability. Common cyber threats in smart cities include data breaches, denial of service (DoS) attacks, ransomware, phishing, and manipulation of IoT devices [3]. Given the magnitude of these threats, research and development efforts have been directed toward enhancing the cybersecurity posture of smart cities.…”