In the digital age, the concept of cyber resilience has become a cornerstone for industries aiming to safeguard their operational integrity, data security, and service continuity against the backdrop of an ever-evolving cyber threat landscape. Businesses need cyber-resilient solutions that can anticipate, withstand, recover, or adapt to cyber-crisis situations affecting their operations. This paper provides a comprehensive view of cyber resilience concepts and techniques, offering a foundational understanding for both researchers and security experts. Additionally, it addresses prevalent discrepancies and conceptual ambiguities in resilience literature, specifically focusing with respect to cyber security i.e., cyber resilience. As the concept of cyber resilience is currently shrouded in ambiguity due to the absence of in-depth thorough consensus on its definition, this paper establishes a consolidated scientific definition of cyber resilience, refining theoretical constructs and operational paradigms in the field. By exploring key actors in cyberspace, delineating the characteristics, goals, and objectives, of cyber resilience, and distinguishing it from similar concepts, this paper enhances the foundational understanding of cyber resilience. Furthermore, it delivers critical insights, identifies limitations and challenges, and outlines future research directions in the domain of cyber resilience. This can guide organizations to comprehensively assess, measure, and build their cyber resilience to anticipate, withstand, recover, or adapt to cyber crises that threaten the survival of their organization across a broad range of business processes.

Classifying fluctuating operating wireless environments can be crucial for successfully delivering authentic and confidential packets and for identifying legitimate signals. This study utilizes raw in-phase (I) and quadrature-phase (Q) samples, exclusively, to develop a low-order statistical feature set for wireless signal classification. Edge devices making decentralized decisions from I/Q sample analysis is beneficial. Implementing appropriate security and transmitting mechanisms, reducing retransmissions and increasing energy efficiency are examples. Wireless sensor networks (WSNs) and their Internet of Things (IoT) utilization emphasize the significance of this time series classification problem. Here, I/Q samples of typical WSN and industrial, scientific and medical band transmissions are collected in a live operating environment. Analog Pluto software-defined radios and Raspberry Pi devices are utilized to achieve a low-cost yet high-performance testbed. Features are extracted from Matlab-based statistical analysis of the I/Q samples across time, frequency (fast Fourier transform) and space (probability density function). Noise, ZigBee, continuous wave jamming, WiFi and Bluetooth signal data are examined. Supervised machine learning approaches, including support vector machines, Random Forest, XGBoost, k nearest neighbors and a deep neural network (DNN), evaluate the developed feature set. The optimal approach is determined as an XGBoost/SVM classifier. This classifier achieves similar accuracy and generalization results, on unseen data, to the DNN, but for a fraction of time and computation requirements. Compared to existing approaches, this study’s principal contribution is the developed low-order feature set that achieves signal classification without prior network knowledge or channel assumptions and is validated in a real-world wireless operating environment. The feature set can extend the development of resource-constrained edge devices as it is widely deployable due to only requiring received I/Q samples and these features are warranted as IoT devices become widely used in various modern applications.