As digital governance increasingly shapes the future of public administration, the demand for secure and efficient E-government services continues to rise. Traditional neural networks, though successful across various fields, often face challenges in scalability, security, and processing speed when dealing with governmental data. This study introduces a novel framework by embedding symmetrical principles within a neural network architecture, aiming to strengthen data protection and streamline operational efficiency in E-government systems. By integrating symmetry at the architectural level, the model reduces redundant computations, leading to faster and more resilient data processing. Moreover, this approach enhances the system’s defense against adversarial threats, a critical concern for public sector applications. The proposed model specifically addresses the unique requirements of E-government platforms, focusing on secure data transmission and robust resistance to security vulnerabilities. Our experimental evaluations highlight notable improvements in processing speeds and security performance, demonstrating the model’s practical potential for Realtime public sector operations. Beyond immediate applications, this work lays a strong foundation for further research into symmetry-driven network designs, offering promising solutions to the complex challenges inherent in managing sensitive public data.

The lack of security techniques that assures the integrity of data generated by the Internet of Things networks is one of the major stumbling blocks that hinders the whole development of these networks. As smart devices generate and transmit confidential and sensitive data, in these cases, data leakage can lead in many cases to drastic consequences. These types of attacks are called compromised attacks and the U2R and R2L subcategories are typical examples. Despite various intrusion detection systems designed for detecting compromised attacks, many of them are deficient and suboptimum due to the highly sophisticated behavior of these attacks that mimic normal ones, as well as the rarity of occurrence, and lack of training records keep this area inconclusive. Therefore, this paper presents an ensemble-based intrusion detection system to identify rare hard-to-detect attacks (U2R and R2L) in IoT networks. The ensemble is composed of two main tiers. Each tier consists of a major improved KNN classifier driven by multiple auxiliary classical KNN classifiers. the combined responses of these detection tires are fused to provide the optimal detection performance for minority attacks. The experimental analysis unveiled that the proposed system achieved a high detection accuracy reaches up to 96.65% and 100% for R2L and U2R respectively along with Cohen's kappa coefficient reaches up to 0.9778 and 0.996, which confirms the reliability and robustness of the proposed system to be deployed in loT networks.

Summary Cybercriminal activities over computer network systems are considered one of the preponderant issues that humanity will face in the coming two decades. The development steps in the design of intrusion detection systems must be carried out in analogous manner to sophistication levels of intrusions developed by hackers. This work proposes a layered hybrid intrusion detection model uses cascaded layers of Clustered Self‐Organized Map (CSOM) and Radial Basis Function (RBF) neural networks to improve the efficiency of detecting frequent and least frequent intrusions. K‐means clustered SOM was used to filter attacks as a first layer, whereas RBF‐based neural network worked as second filtering and attacked classification layer leading to significance reduction in time required to process connection records and notable improvements in the performance of intrusion detection. A new balanced version of cleansed NSL‐KDD dataset was used to validate and evaluate the proposed model. Compared with other existing schemes; the proposed model shows high detection performance in terms of accuracy 97.73% and false positive rate as low as 0.023%. In particular, for detecting least and most harmful attacks, U2R and R2L, the system achieved detection rate of 88.6% with false positive rate of 0.016. Comparative results showed that CSOM‐RBF model is more suitable for real‐life implementation than other many existing state‐of‐the‐art intrusion detection models.

Summary One desired aspect of microservice architecture is the ability to self‐adapt its own architecture and behavior in response to changes in the operational environment. To achieve the desired high levels of self‐adaptability, this research implements distributed microservice architecture model running a swarm cluster, as informed by the Monitor, Analyze, Plan, and Execute over a shared Knowledge (MAPE‐K) model. The proposed architecture employs multiadaptation agents supported by a centralized controller, which can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q‐learning network (DRQN). It is argued that such integration between DRQN and Markov decision process (MDP) agents in a MAPE‐K model offers distributed microservice architecture with self‐adaptability and high levels of availability and scalability. Integrating DRQN into the adaptation process improves the effectiveness of the adaptation and reduces any adaptation risks, including resource overprovisioning and thrashing. The performance of DRQN is evaluated against deep Q‐learning and policy gradient algorithms, including (1) a deep Q‐learning network (DQN), (2) a dueling DQN (DDQN), (3) a policy gradient neural network, and (4) deep deterministic policy gradient. The DRQN implementation in this paper manages to outperform the aforementioned algorithms in terms of total reward, less adaptation time, lower error rates, plus faster convergence and training time. We strongly believe that DRQN is more suitable for driving the adaptation in distributed services‐oriented architecture and offers better performance than other dynamic decision‐making algorithms.

Understanding and reusing complex APIs is a difficult and time consuming process, especially in legacy code and open-source software libraries and frameworks. While some primary API features are documented properly, most others lack enough documentation and design knowledge that can help developers find, understand, and reuse the needed software components and other library features. As such, the source-code itself is often the only reliable means for programmers to rely on when performing their reuse practices. In order to leverage program understanding and software reuse, this paper describes an ontology-based system named JMentor that we have developed for this purpose. Firstly, JMentor is capable of recovering the lost design decisions by reverse engineering design patterns. Secondly, JMentor provides a mechanism for locating and retrieving reusable software components. Thirdly, JMentor provides an approach for automatically recommending source-code snippets that show an example of how the user can properly reuse the retrieved components. JMentor mechanisms are based on ontology and semantic reasoning techniques. Its evaluation case studies show evidence that such techniques can improve software understanding and reuse practices.

Nowadays, traditional applications are becoming increasingly complex, so it is necessary to enhance computing competence and maintain stable environment. To meet these requirements, most of enterprises should purchase expensive hardware devices and software. As a result, cloud computing is developed to share hardware and software resources. Users are never plagued by insufficient storage space and never worry about loss of data because of disk crash. Meanwhile, the expenditure is reduced. However, cloud security is a significant problem which every enterprises and individual entrepreneur must face with. Recently, cloud computing security incidents happen frequently, and many enterprises suffer the security incidents even more than once. This paper aims to decrease risks related to cloud computing, improve security and prevent those risks from impacting business goals through assessing and managing the cloud computing environment risks. Further, this paper proposes a risk minimization framework (RMF) about risk minimization adapting to the situation and providing the algorithm of risk minimization process. The proposed frame is tested and implemented using C++ platform. Finally, the proposed RMF is compared with known risk minimization frameworks.