Cybersecurity experts widely acknowledge that a Distributed Denial of Service (DDoS) assault poses a grave threat, capable of inflicting substantial financial losses and tarnishing the reputation of enterprises. Conventional detection methods are insufficient for identifying DDoS attacks. Simultaneously, with their vast potential, machine learning solutions play a vital role in this field. This paper presents a distributed approach for identifying distributed denial-of-service threats using the pipeline artificial neural network method, supported by elephant herding optimization for feature selection and extraction. The proposed artificial neural network pipeline-based model for detecting DDoS attacks comprises several key stages: collecting the dataset, preparing the data, implementing a balanced data strategy, selecting relevant features using the swarm optimization method Elephant Herding Optimization (EHO), training the model, testing its performance, and evaluating its effectiveness. Experimental results demonstrate that the proposed approach effectively enhances DDoS detection accuracy while reducing false positives, making it a promising solution for network security. This model demonstrated a remarkably high ability to detect DDoS attacks with a 99% accuracy. Thorough investigations demonstrate that the model is highly skilled in implementing security measures and reducing the risks connected with emerging security threats. The effectiveness of our proposed solution, leveraging a pipeline method in Artificial Neural Network (ANN), is crucial to building a reliable model, which is evident in its ability to deliver effective results in low complexity. The proposed method achieves 99.99% accuracy, 99.80% precision, and a False Positive Rate (FPR) of 0.002%, outperforming recent models. These results demonstrate the model's superior accuracy and robustness in identifying complex attack patterns while minimizing false positives.

Lightweight block encryption algorithms, such as PRESENT, KEILN, and LED, are pivotal in securing resource-constrained devices and small-scale systems relevance for environments with limited computational resources. This work presents a performance comparison in execution time, memory usage, and implementations in single-core and multicore systems. Our approach involves executing encryption for fixed text file sizes in single-mode and multicore-mode processing. The implemented Multicore technique is notable for its efficient adjustment of text files with a fixed size, showcasing its adaptability. The evaluation criteria encompass the execution time of encryption and memory utilization, continually showcasing the performance analysis of algorithms prioritizing lightweight encryption. These algorithms exhibit significant improvements while handling Multicore execution fixed text data files, which minimally impact encryption and decryption time. These values can be influenced by factors such as the encryption technique, file content, and system resources. We performed simulations using an Arduino Nano, which operates on an ATmega328P microcontroller as the principal device, and several microcontroller assessments within the IoT sector. This research additionally established additional measurements, covering energy utilization, security robustness, and attack resilience of three lightweight encryption methods. Our findings indicate that, in terms of various metrics, the LED approach appears to be the most effective algorithm in a single model. It has the quickest encryption time and uses the least amount of RAM.KEILN seems to be the best choice for applications where memory usage is critical because of its efficient use of resources. LEDs provide competitive speeds; however, a slight increase in memory usage is allowed. Because of its large memory requirements, PRESENT might not be as enticing even though it is reasonably quick. This paper will aid IoT developers in choosing a suitable platform and encryption method for creating a secure network, considering several resource constraint factors. It facilitates educated decision-making concerning algorithm choice, microcontroller selection, and optimization methodologies to attain the required equilibrium of performance, security, and resource utilization.