attack detection

The Internet of Things is a new technology that communicates with the surrounding objects through the Internet and is used for the purpose of remote measurement and control. In the field of Internet of Things (IoT) network security, it is very important to accurately identify the types of attacks on these networks that are launched by zombie hosts under the control of the attacker. In this article, a new neural network is proposed to improve the detection of intrusion into the Internet of Things network based on the ALEXNET convolutional neural network and chaotic krill optimization algorithm (MONANET). In the MONANET network, in order to improve the accuracy in detecting intrusion into the IoT network and not need to manually adjust the parameters, the hyperparameters of the neural network are dynamically selected using the chaotic krill algorithm. The value of the loss function of the validation set obtained from the first training of the neural network model using the Danmini doorbell dataset is considered as the CKH fitness value. The comprehensive performance of the proposed network and GRU, ANN, SVM, LSTM, R-CNN, and APSO-CNN algorithms have been compared in five evaluation indices and 12 times independent experiments. The obtained results show the improvement of intrusion detection to the Internet of Things network. The proposed algorithm has been able to accurately detect %99.89 attacks on the Internet of Things network. The experimental results show the superiority of the proposed method over other knowledge boundary methods in terms of improving classification accuracy.

Cloud computing is an emerging technology that is widely used to provide computing, data storage services and other remote resources over the Internet. Availability of cloud services is one of the most important concerns of cloud service providers. While cloud services are mainly transmitted over the Internet, they are prone to various attacks that may lead to the leakage of sensitive information. Distributed DDoS attack is known as one of the most important security threats to the cloud computing environment. This attack is an explicit attempt by an attacker to block or deny access to shared services or resources in a cloud environment. This paper discusses a hybrid approach to dealing with DDoS attack in the cloud computing environment. This method highlights the importance of effective feature-based selection methods and classification models. Here, an entropy-based approach and particle swarm optimization to counter these attacks in a cloud computing environment is presented. Classification on high-dimensional data typically requires feature selection as a pre-processing step to reduce the dimensionality. However, effective features selecting is a challenging task, which in this paper uses particle swarm optimization. Here, the proposed classification model is developed based on the use of a balanced binary search tree and dictionary data structure. The simulation is based on the NSL-KDD and CICDDoS2019 datasets, which prove the superiority of the proposed method with an average detection accuracy of 99.84% over the AGA and E-SVM algorithms.