Distributed denial of service (DDoS) attack is a subclass of denial of service attack that performs severe attack in a cloud computing environment. It makes a malicious attempt to disturb the usual services of any network or server by using botnets. Hence, an efficient intrusion detection system (IDS) is essential to detect this attack. Some limitations in the existing IDS models for DDoS attack detection are delayed convergence, local stagnation issues, and local and global optimal trapping issues. These limitations are met by the recurrent neural network (RNN) and deep learning- (DL-) based proposed models that can utilize the previous states of the hidden neuron. The proposed research has used a long short-term memory (LSTM) recurrent neural network and autoencoder- and decoder-based deep learning strategy with gradient descent learning rule. The network parameters like weight vectors and bias coefficient are tuned optimally by employing the proposed a hybrid Harris Hawks optimization (HHO) and particle swarm optimization (PSO) algorithm. The proposed hybrid optimization algorithm selects the essential attributes, and the results obtained confirmed that the proposed LSTM and deep learning model outperformed all other models developed in the literature.

The buck converter is one of the DC/DC converters that are often used as power supplies and high energy conversion. These controllers are used in electronic devices to regulate the output voltage against change in the input voltage and load current. This leads to the requirement of an efficient control methodology to meet real-time demand. It is found in many works of literature that satisfactory performance in buck converter’s dynamic behavior using genetic algorithm controlling techniques, but the appearance of the transient with large overshoots and the ripple output voltage is not focused. The main reason for such a problem is the improper selection of capacitance values. In this work, to address such issues, along with the PID controller gain values and capacitance selection also be considered in multi-objective problems. The optimization is carried out using the NSGA-II and modified NSGA-II algorithm. This paper also presents a comparison of these algorithms’ performance in terms of time-domain specifications and ripple voltage. Simulated outputs exhibit that the controller based on MNSGA-II for buck converter gives better performance than NSGA-II-based controller and results compared with the existing wok. The MNSGA–II-based PID controller for buck converted validated in the real-time environment also.

This study presents a novel and resilient intrusion detection system (IDS) designed to address the various challenges posed by Internet of Things (IoT) systems. The proposed model utilizes a deep belief network (DBN) for feature selection (FS) in conjunction with mean shift clustering (MSC), enhancing detection accuracy. Additionally, the system utilizes adaptive drift detection to efficiently manage high-dimensional and dynamic IoT networks. Evaluation of the IoT-20 dataset demonstrates that the proposed DBN IDS achieves a root mean square error (RMSE) of 0.025, with a precision of 97.8% and a recall of 98.1%. The model also boasts an $F1$ score of 97.9% and a specificity of 98.7%. Qualitative analysis reveals an area under the curve (AUC) of 0.98 for the receiver operating characteristic (ROC) curves, indicating near-perfect classification performance. Other performance metrics of the IDS are favorable, with reconstruction error limiting factors reported at 0.022, suggesting a reliable model for normal behavior and accurate anomaly detection. The model’s accuracy for inference and evaluation stands at 96% and 97%, respectively, illustrating the consistency of the studied model across different data segregations. These results highlight that the proposed DBN-based IDS is a reliable solution that goes beyond current threats in the IoT landscape, effectively detecting both existing and emerging threats.