Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.

The widespread adoption of microservices-based architectures in native cloud systems has amplified the need for robust observability strategies to ensure system reliability and performance. Microservices, while enabling scalability and flexibility, introduce complexities such as distributed failure points, performance bottlenecks, and resource mismanagement. This paper provides a comprehensive review of state-of-the-art observability frameworks and tools designed for microservices architecture. It focuses on key aspects of observability at the system, service, and network levels, utilizing logs, traces, and metrics as core data sources. A thematic taxonomy is proposed, classifying the diverse approaches to monitoring microservices based on implementation paradigms, deployment platforms, and architecture patterns. The study compares existing solutions in terms of their capabilities, limitations, and effectiveness in addressing observability challenges in cloud, edge, and fog environments. Furthermore, open research issues are identified, and practical recommendations are provided to optimize observability in complex, distributed microservices ecosystems. This work aims to serve as a valuable resource for system architects, DevOps engineers, and researchers striving to enhance the reliability and performance of modern cloud-native systems.

Containerization is a rapidly advancing technology in cloud computing, facilitating the seamless development, deployment, and management of applications across diverse computing environments. This technology offers advantages such as lightweight operations, portability, efficiency, and scalability, applicable to developer workstations, mission-critical web servers, and the public cloud. However, unlike Virtual Machines (VMs), containers share the underlying machine’s operating system (OS) kernel, which introduces unique security challenges alongside speed and efficiency benefits. These challenges include the risks of container escape attacks, privilege escalation, and exploitation of kernel vulnerabilities. This paper comprehensively reviews state-of-the-art containerization security solutions, with a particular focus on various kernel isolation approaches. It proposes a thematic taxonomy of containerization security, highlighting essential parameters to help developers understand the security needs within a shared kernel environment. This paper describes the current landscape of container security by examining critical developments, challenges, and trends in the existing literature—from system calls to kernel isolation. Additionally, it identifies open research issues and discusses industry best practices and emerging developments in container security, aiming to guide future research and implementation strategies.

The internet of things (IoT) is a complex system that includes multiple technologies and services. However, its heterogeneity can result in quality-of-service (QoS) issues, which may lead to security challenges. Software-defined network (SDN) provides unique solutions to handle heterogeneity issues in large-scale IoT networks. Combining SDN with IoT networks has great potential for addressing extreme heterogeneity issues in IoT networks. Numerous researchers are investigating various techniques to resolve heterogeneity issues in IoT networks by integrating SDN. Our study focuses on the SDN-IoT domain to improve QoS by addressing heterogeneity. Heterogeneity in SDN-IoT networks can increase the response time of controllers. We propose a framework that can alleviate heterogeneity while maintaining QoS in SDN-IoT networks. The framework converts m heterogeneous controllers into n homogeneous groups based on their response time. First, we examine the impact of the controller’s bandwidth and find that the system throughput decreases when the controller’s bandwidth is lowered. Next, we implement a simple strategy that considers both the bandwidth and service time when selecting the peer controller. Our results show some improvement in the framework, indicating its potential to alleviate heterogeneity while maintaining QoS and other metrics.

Containerized applications offer lightweight and scalable deployment but remain exposed to security risks due to a shared kernel. We present DeSFAM (Dynamic eBPF-driven Syscall Filtering and Anomaly Mitigation), a real-time security framework that enforces least-privilege syscall usage and detects behavioral anomalies. DeSFAM integrates: 1) hybrid syscall profiling through static analysis and dynamic eBPF tracing; 2) SyscallAD (System call Anomaly Detection), a low-latency anomaly detector combining Variational Autoencoder (VAE) and Isolation Forest (iForest); 3) contextual risk scoring based on MITRE ATT&CK mappings and CVE correlations; and 4) adaptive syscall enforcement using eBPF maps and LSM hooks. Evaluations using the DongTing dataset and real-world CVE attack scenarios show DeSFAM achieves 94% precision, 90% recall, sub-millisecond enforcement latency, and less than 1% performance overhead. DeSFAM effectively blocks privilege escalation, container escape attempts, and syscall injection attacks in modern container environments.

The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus’s spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days.