Smart farming enhances sustainable communication practices through the deployment of energy-efficient Internet of Things (IoT) devices, low-power wireless technologies, and edge/fog computing to reduce data transmission and energy usage. Smart cities represent a concept in which technology, data, and innovation enhance urban efficiency, sustainability, and livability. Smart farming has the potential to facilitate the sustainable development of smart cities. However, integrating smart farming into smart city systems presents significant challenges, particularly with respect to the security of their interconnected components. The vulnerability of agricultural information and the likelihood of cybersecurity threats necessitate the development of targeted security solutions for smart farming. To address these challenges, we propose a Big Data Analytics-envisioned secure smart farming scheme for sustainable cities (in short, CSSF-SC). It is an efficient authenticated key agreement mechanism that enables secure mutual authentication, session-key establishment, and dynamic key management among smart farming devices, drones, and cloud servers. Using the Scyther verification tool, security is formally verified under the Dolev–Yao and CK adversary models, demonstrating resilience to various potential attacks. A comparative performance analysis shows that CSSF-SC outperforms current schemes in terms of computation and communication costs while offering stronger security and additional functionalities. Finally, a practical implementation is done using the MangoLeafBD dataset and an EfficientNet-B7 architecture. It achieves 99.16% accuracy, validating the framework’s effectiveness for secure crop-data collection and analysis in real-world scenarios.

Critical infrastructure is a collection of physical and cyber systems, which are essentially required to support the day-to-day operations of our daily life. In the critical infrastructure, the computing systems (i.e., Internet of Things (loT) devices) communicate through the Internet. Therefore, most of the time, critical infrastructures are targeted by hackers by launching some cyber-attack, i.e., ransomware. Hence we require some security mechanisms to protect the data and systems of critical infrastructures. This paper proposes a scheme for the detection, analysis and mitigation of ransomware attacks to protect Internet of Things (loT)-enabled critical infrastructure (in short, RADM-ICI). We also practically demonstrated RADM-ICI and computed essential performance parameters, i.e., accuracy and F1-score under different machine learning models. The conducted security analysis of RADM-ICI proved its excellent security for the ransom ware attacks. During the performance comparison of the proposed RADM-ICI and other similar competing existing schemes, it has been observed that the proposed RADM-ICI achieved better accuracy than the other existing competing schemes.

Aerial Computing is one of the applications of Internet of Things (IoT) which makes use of autonomous aerial devices, such as drones and unmanned aerial vehicles (UAVs). The ubiquitous nature of IoT and aerial computing is poised to revolutionize our daily lives by enabling seamless real-time information sharing among interconnected objects. However, ensuring the safety and security of such network is crucial in preventing potential threats and attacks. The purpose of this study is to develop a sophisticated intrusion detection system that is effective, efficient, and intelligent using complex machine learning models trained on relevant intrusion detection datasets. In this article, the multiclass classification approaches for intrusion detection in IoT-driven aerial computing environment are presented (in short, MCA-IDAC). In the comparative study, it has been observed that proposed MCA-IDAC performs significantly better than the other existing competing schemes, in terms of important performance parameters.

Internet of Things (IoT)-based smart factories offer the manufacturing sectors a great opportunity to embrace the fourth industrial revolution (Industry 4.0). The real-time monitoring of manufacturing operations in an Industry 4.0 needs to be ensured by the deployed technologies, like Artificial Intelligence (AI) and Big Data analytics. The overall purpose is to improve the outcomes of the production process. However, Industry 4.0 becomes vulnerable to different potential attacks as the communication takes place via public environments. In this article, an authentication and key agreement method has been suggested to secure the communication that can occur in a Fog-based Industry 4.0 environment. The security proposal provides secure mutual authentication along with key establishment between various smart industrial devices and fog servers, as well as between fog servers and cloud servers. The security analysis and comparative study reveal that the proposed method can mitigate various potential attacks, and it also offers important security and functionality attributes as compared to those for other competing schemes.

There has been exponential growth in the field of Internet of Things (IoT)-enabled e-health domain, where several technologies are interconnected with each in order to provide low-cost and efficient services to users. The smart healthcare devices monitor the physiological conditions of a patient and then send data to connected servers (i.e., health server). The smart healthcare devices, servers and associated software applications come under one umbrella to provide live tracking, monitoring and analysis of the the healthcare data to the concerned users. It is also considered as Internet of Medical Things (IoMT) communication environment. All medical records are considered critical and sensitive in nature and therefore any leakage of medical data could potentially turn out to be a the lethal to patients. With the innovation in technology, there is a constant security threat to all smart healthcare devices, which is a main issue in e-health system. Cyber threat actors are actively trying to break into the system or network to gain unauthorized access and privileges to manipulate the data. Therefore, there is a strong urge for cyber security in this domain to secure all computing devices from any computer attack. Proper authentication, authorization, a secure session key exchanges, etc., are required to create a secure channel among the communicating devices. In this paper, an authentication and key agreement scheme to secure the communication of e-health system (named as ASKA-EH, in short) is proposed. The provided security analysis of ASKA-EH proves its security against various attacks. The comparative performance analysis of proposed ASKA-EH and other existing schemes of ehealth system reveals that ASKA-EH is superior than the existing schemes.

In an Internet of Things (IoT) enabled e-health system, smart health devices sense the health data continuously and share the collected data with the neighboring controller device (i.e., personal server) via some wireless communication mechanism (i.e., bluetooth and zigbee), and finally the data is stored on some health server (i.e., a server over the cloud). The health data is then accessible to healthcare service providers (for example, doctors, nursing staff, relatives of patient) for tracking and monitoring of health conditions of the patients for their better treatment at the earliest. In an IoT enabled health system, the smart healthcare devices communicate over public channel, which causes various types of threats and attacks on the ongoing communication. Therefore, we need a powerful privacy-preserving security mechanism to secure the communication happens in an IoT enabled e-health system as the health data is strictly private and confidential. In this paper, we propose a new privacy-preserving access control and key management scheme for the secure communication of IoT enabled e-health system (SPCS-IoTEH). We also conduct informal security analysis of the proposed SPCS-IoTEH to show its robustness against various types of active and passive attacks. The performance of SPCS-IoTEH is also shown to be better than other existing competing schemes.