The explosive growth of the Internet of Things (IoT) has highlighted the urgent need for strong network security measures. The distinctive difficulties presented by Internet of Things (IoT) environments, such as the wide variety of devices, the intricacy of network traffic, and the requirement for real-time detection capabilities, are difficult for conventional intrusion detection systems (IDS) to adjust to. To address these issues, we propose DCGR_IoT, an innovative intrusion detection system (IDS) based on deep neural learning that is intended to protect bidirectional communication networks in the IoT environment. DCGR_IoT employs advanced techniques to enhance anomaly detection capabilities. Convolutional neural networks (CNN) are used for spatial feature extraction and superfluous data are filtered to improve computing efficiency. Furthermore, complex gated recurrent networks (CGRNs) are used for the temporal feature extraction module, which is utilized by DCGR_IoT. Furthermore, DCGR_IoT harnesses complex gated recurrent networks (CGRNs) to construct multidimensional feature subsets, enabling a more detailed spatial representation of network traffic and facilitating the extraction of critical features that are essential for intrusion detection. The effectiveness of the DCGR_IoT was proven through extensive evaluations of the UNSW-NB15, KDDCup99, and IoT-23 datasets, which resulted in a high detection accuracy of 99.2%. These results demonstrate the DCG potential of DCGR-IoT as an effective solution for defending IoT networks against sophisticated cyber-attacks.

This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. This work focuses on proposing a novel filtration learning approach for underwater sensor nodes. This model was created by merging two adaptive filters, the finite impulse response (FIR) and the adaptive line enhancer (ALE). The FIR integrated filter eliminates unwanted noise from the signal by obtaining a linear response phase and passes the signal without distortion. The goal of the ALE filter is to properly separate the noise signal from the measured signal, resulting in the signal of interest. The cluster head level filters are the adaptive cuckoo filter (ACF) and the Kalman filter. The ACF assesses whether an emitter node is part of a set or not. The Kalman filter improves the estimation of state values for a dynamic underwater sensor networking system. It uses distributed learning long short-term memory (LSTM-CNN) technology to ensure that the anticipated value of the square of the gap between the prediction and the correct state is the smallest possible. Compared to prior methods, our suggested deep filtering–learning model achieved 98.5% of the sensory filtration method in the majority of the obtained data and close to 99.1% of an adaptive prediction method, while also consuming little energy during lengthy monitoring.

As institutions expand, and grow, surging interference malware and attacks; large portions of the Internet at a time impinge and create large amounts of service disruption. So, their IT infrastructure needs to be updated and expanded to accommodate the changing demands of their business. The project's main objective is to improve and enhance the network infrastructure design for communication institutions. In this work, we tried to present a meliorating plan for the infrastructure of enormous communication systems inside institutions. It implemented the network topology so it has established an on-premises infrastructure constituting two sites with three different networks: LAN, data center, and DMZ. The LAN network was reserved for client devices. In contrast, the data center network is intended for servers such as the domain controller, additional domain controller, file storage server, network-attached storage, DHCP server, DNS server, and WDS server (used for deploying operating systems on devices). The DMZ network included servers for hosting public websites (IIS server) and public DNS, as well as a DFS server for replicating files between the two sites. To ensure the security of the infrastructure, a Fortigate firewall is used to separate the DMZ network from the LAN and data center networks. It connected firewall A to Firewall B to enhance security and filter traffic. It connected firewall B to a router, which is then connected to the ISP network.The on-premises infrastructure provides a reliable, secure, and scalable solution for institutions. The layered security approach with Fortigate firewalls helps to ensure that data and resources are protected from unauthorized access. Also, we performed the trap and IP security technology on the sites. The infrastructure is flexible enough to accommodate the organization's future growth and expansion needs. The combination of on-premises infrastructure and layered security measures provides a solid foundation for critical business operations. The infrastructure is well-suited to meet the needs of a modern enterprise with its advanced threat protection capabilities and ability to scale up or down as required.

The increasing prevalence of marine pollution and the escalating impacts of climate change have intensified the need for advanced underwater monitoring systems capable of operating reliably in harsh subsea environments. However, underwater environmental monitoring remains a challenging task due to factors such as high hydrostatic pressure, limited visibility, dynamic water conditions, and the inherent constraints of underwater communication channels. To address these challenges, this study proposes a Hybrid Underwater Wireless Sensor Network (UHWSN) that integrates acoustic and optical sensing modalities with advanced deep learning techniques to enable efficient underwater debris detection, tracking, and classification. The proposed framework combines mean-shift-based object tracking, lossless arithmetic coding, Convolutional Neural Networks (CNNs), and Deep Belief Networks (DBNs) within a hierarchical processing architecture. At the sensor-node level, mean-shift tracking is employed for region-of-interest (ROI) localization, while arithmetic coding provides efficient lossless data compression, achieving a data-volume reduction of approximately 55–60% without compromising image fidelity. At the cluster-head level, CNN-based feature extraction and DBN-based classification facilitate robust high-level inference and anomaly detection under challenging underwater conditions. The performance of the proposed UHWSN framework was evaluated using a combined TrashCan and J-EDI dataset comprising 7,212 images, augmented to simulate realistic underwater degradations, including turbidity, low-light conditions, and color distortions. Experimental results demonstrate a tracking Average Precision (AP) of 98.0% and a classification accuracy of 99.30% on the test set. These results compare favorably with recent state-of-the-art approaches evaluated on similar underwater debris detection benchmarks, which typically report detection performance in the range of 65–90% mAP under comparable conditions. Furthermore, the proposed framework achieves enhanced energy efficiency and reduces communication overhead by approximately 30–40% relative to conventional underwater monitoring architectures, thereby extending network lifetime and improving operational scalability in resource-constrained environments.