Software-Defined Networks (SDN) provides more control and network operation over a network infrastructure as an emerging and revolutionary paradigm in networking. Operating the many network applications and preserving the network services and functions, the SDN controller is regarded as the operating system of the SDN-based network architecture. The SDN has several security problems because of its intricate design, even with all its amazing features. Denial-of-service (DoS) attacks continuously impact users and Internet service providers (ISPs). Because of its centralized design, distributed denial of service (DDoS) attacks on SDN are frequent and may have a widespread effect on the network, particularly at the control layer. We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. We use the SHAP feature selection technique to improve the detection procedure. By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. Fine-tuning the hyperparameters with the help of Bayesian optimization to obtain the best model performance is another important thing that we do in our model. Two datasets, InSDN and CICDDoS-2019, are utilized to assess the effectiveness of the proposed method, 99.95% for the true positive (TP) of the CICDDoS-2019 dataset and 99.98% for the InSDN dataset, the results show that the model is highly accurate.

The increasing volumes and sophistication of cyber threats, particularly Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks, pose significant dangers to contemporary network structures, particularly the Internet of Things (IoT) environment. Conventional Intrusion Detection Systems (IDS) are also becoming obsolete because they perform detection in a built-in manner and are unable to capture the time trends of dynamic changes of threats. To eliminate such shortcomings, a new hybrid deep learning architecture named the Neural Turing Machine-Gated Recurrent Unit (NTM-GRU) model is proposed in this paper that incorporates the external memory of NTMs and extra temporal learning power of GRUs. The architecture supports analysis on dual timescales, which in turn captures short- and long-term dependencies, exposing the model to unravel complex, low, slow, and zero-day intrusions with recall. Huge testing on the standard sets (UNSW-NB15 and BoT-IoT) and actual (CICIDS2017 and CSE-CID-IS2018 ) demonstrate the high effectiveness of the usage of the model, reaching an accuracy of 99.98%, F1-scores of up to 96% on unknown threats, and the low false positive rates (less than 0.4%). The proposed framework can be applied in both industrial settings and high-speed network settings, where the real-time inference speed was measured at 2.3 milliseconds. The model also incorporates interpretability aspects, making it suitable for Security Operation Centres (SOCs). This work, through the merger of complex memory neural-network structures with cybersecurity needs and requirements encountered in the world, can be realized as providing a scalable, adaptive, and interpretable intrusion detection module, establishing a new state-of-the-art standard for securing next-generation networks.

The adaptive nature of evolving cyber-threats, particularly in Internet of Things (IoT) and Software-Defined Networking (SDN) environments, necessitates intrusion detection systems (IDS) that can adjust to highly dynamic network topologies. Traditional machine learning-based IDS models are limited in their ability to adequately capture discrete temporal dynamics and complex structural dependencies, and therefore struggle to accurately detect sophisticated attacks. In this paper, we propose a new intrusion detection system that employs Graph Neural Networks (GNNs) and Neural Ordinary Differential Equations (Neural ODEs) to represent network traffic as a dynamical graph that evolves over time. To enhance efficiency, noise reduction is achieved through a smoothing-sharpening filter, class imbalance is addressed using the Synthetic Minority Oversampling Technique (SMOTE), and feature selection is integrated via the Tyrannosaurus Optimization Algorithm (TOA). Model parameters are further fine-tuned with the Capuchin Search Optimization Algorithm (CSOA) to optimize classification performance. The proposed framework achieves an accuracy of 98.5%, a precision of 98.2%, a recall of 97. 8%, and an F1 score of 98.0%, outperforming state-of-the-art baselines such as CNN, LSTM, and StrucTemp-GNN by up to 5.2% in the experimental analysis of the CIC IoT 2023 dataset, which includes multiple attack scenarios. Statistical testing confirms that these improvements are significant (p < 0.05). There are also extended experimental assessments, involving cross-dataset testing on the ToN-IoT dataset, as well as an ablation, scalability, and topology analysis, and exhibit the strength, generalization, and practicality of the proposed Hybrid Neural ODE-GNN framework. The results indicate that combining topological modeling with GNNs and temporal behavior learning through Neural ODEs provides a highly scalable and dynamic intrusion detection solution for real-time IoT and SDN environments.

Neuromorphic computing, which is based on spiking neural networks (SNNs) and event cameras, can provide energy-efficient autonomous vehicle (AV) perception, yet is exceedingly susceptible to adversarial perturbations, fault injections, and data poisoning. Conventional defences may prove inadequate on the spot scenarios with a small amount of edge resources. The systemic security solution proposed in the paper consists of a Hybrid Graph Neural Network-Long Short-Term Memory (GNN-LSTM) attack detection model with a Differential Privacy (DP) and Secure Multi-Party Computation (SMPC) solution to privacy and threat-reduction respectively. Quantization and pruning are also used to optimise the framework to support edge deployment. KITTI multimodal experiment results indicate 94.3 percent accuracy and lower the attack success rate by 30 percent. The neuromorphic N-Caltech101 experiments reach an accuracy of 92.4 percent with a drop of 27 percent. These results confirm that the proposed solution can offer substantial, privacy-conscious and resource-efficient security of next-generation neuromorphic autonomous systems against trained adversarial attacks.

Research examines Saudi Arabian cyber security knowledge-sharing programs during its digital transformation under Vision 2030 through a combination of literature reviews and expert specialist insights to analyze current cybersecurity professional information transfer systems. This analysis shows how technological developments along with organizational and cultural elements impact these practices since the constant drive for innovation aims to enhance knowledge transfer so researchers discovered that cultural obstacles from resistance to openness, lack of trust and hierarchical structures and division within organizations and insufficient workflow systems along with worry about trust and outdated technological capabilities limit successful knowledge sharing. Through analysis of knowledge-sharing programs established by the National Cybersecurity Authority (NCA) Saudi Aramco and the King Abdulaziz City for Science and Technology (KACST), researchers show that strategic programs improve national cybersecurity readiness effectiveness. The research provides actionable advice that combines the design of a national security plan and secure technology funding with does-based mentorship initiatives across sectors and integrated incident reporting along with educational programs and performance-driven reward systems for motivation. The research offers combined theory and practice-oriented guidance that helps Saudi Arabia’s policymakers along with organizations and cybersecurity practitioners to build effective strategies as they establish their leadership position in collaborative cybersecurity practices internationally.