As we know that internet is very popular nowadays, everyone is using and doing all the works like education, online shopping, marketing using with the internet, to provide a security to the network is very crucial task, there is devices and tools are available to enhance the security of the computer network and provide security to the network. As now days every organization need to have data and other critical information in a secure mode, therefore it is very important to keep the safe to individual and network based system, As a result, sophisticated new attacks emerge, endangering vital infrastructure. An IDS is essential to detect and counter these attacks. IDS can be hardware devices or software products that monitor abnormal activity or behavior of the system. Pattern-matching systems detect known attack patterns, while statistical anomaly-based systems store typical behavior patterns in the database. In this research article present an overview of intrusion detection system, review and their challenges, also discuss for a near solution to provide for network based system from unwanted user or any suspicious activity. This research work also emphasize on machine learning based deployment for intrusion detection system to detect and analysis of the behaviours of system in a network.

In recent years, cloud-native architectures have transformed how web applications are designed, deployed, and scaled. This paper presents a systematic review of the role of GraphQL, Docker, and microservices in enabling modern web applications. The survey explores the evolution from monolithic to microservices-based systems, the adoption of Docker containerization for deployment consistency, and the use of GraphQL as an API gateway for efficient data retrieval. Key performance metrics such as response time, throughput, scalability, and error rates are analyzed across multiple studies. The review highlights the advantages of microservices architectures over monoliths, identifies challenges in orchestration, security, and resource utilization, and provides directions for future research in cloud-native development.

Image denoising is a critical initial step in the analysis of image data, essential for mitigating data corruption and enhancing image quality by producing a cleaner output compared to its noisy counterpart. This paper presents a hybrid method aimed at improving gray-scale images, which are particularly susceptible to high levels of environmental noise. To achieve effective noise reduction, the paper employs a wavelet transform domain method, recognized for its effectiveness in such applications. However, this method does not account for local noise components, leading to residual noise in the gray-scale image post-denoising. To address this issue, the approach incorporates the collection of low component values using multiple sequences and employs a self-organized map network to refine the denoising process further. Overall, this hybrid methodology aims to significantly enhance the quality of gray-scale images by systematically addressing the noise reduction challenge.