Internet of Things based Agriculture (Ag-IoT) is widely adopted by farmers and agriculturists worldwide. Ag-IoT involves technologies such as the internet, sensors, and communication technologies that help automate agricultural labour and improve monitoring, efficiency, and cost. The survey was conducted during 2021 to 2024 with the objective to find out the farmers' readiness for adopting Ag-IoT ecosystems in India using qualitative research methodology, and data were collected through a questionnaire and one-to-one interviews with 100 farmers from six states of India. The findings indicated that 86% of respondents expressed willingness to adopt Ag-IoT technologies. 61% of respondents have no concern of security and privacy in Ag-IoT. However, only 24% of farmers were aware of Ag-IoT, indicating a significant awareness gap about Ag-IoT solutions, security and privacy. Also, the outcome score of 46.19 from the correlation analysis indicated the positive influence of study parameters on the conclusions drawn from our study. The outcomes of this survey can be used by policymakers, researchers, and technology developers to design strategies to promote the secure and effective adoption of Ag-IoT solutions in agricultural communities. Specifically, security researchers need to develop suitable digital forensics and incident response management solutions for Ag-IoT to support its growth and adoption.

The Internet of Things (IoT) has been revolutionizing the agricultural industry by providing farmers with unprecedented opportunities to monitor and control their crops, livestock, and farm equipment in real-time, which is named as IoT based Agriculture (Ag-IoT). Ag-IoT relies on the use of communication technology, internet, and other wireless technologies which makes it prone to various cyber attack and cyber crimes. To address the growing security and forensic challenges in Ag-IoT, we propose a Digital Forensics and Incident Response Management Model (DFIRMM). The proposed model focuses on the identification, analysis, and mitigation of security incidents, along with support for the investigation of digital forensics tailored to the unique requirements of Ag-IoT. The proposed model is validated through a case study on MQTT enabled smart agriculture network with machine learning based analysis. We believe this proposed model will redefine how security incidents are handled in smart agriculture industries and impact their growth.

The integration of Internet of Things (IoT) technology into agriculture has significantly enhanced farm management and productivity. The Message Queuing Telemetry Transport (MQTT) protocol, pivotal in this transformation, facilitates communication among IoT devices and decision making systems. Being a cost-effective and time-effective system, Ag-IoT is prone to attacks due to its inherent vulnerabilities and weak security policies. When security incidents happen, robust response mechanisms help to contain the impact of the incident to prevent severe impacts on the network. This article presents an incident response model for a smart agriculture system to address incident response challenges. We conducted a case study on MQTT based agriculture system with the help of two publicly available IoT datasets based on the proposed model. We conducted a series of experiments to statistically analyze the datasets for more insights and also used four widely used machine learning algorithms to detect malicious traffic. This study also proposes a set of Quality of Service based recommendations for effective incident response. Finally, we believe that this work provides an insightful approach to incident response in smart agriculture systems to address the prevailing security incidents.

Provenance has been instrumental in networked systems to solve issues related to data trustworthiness, network diagnostics, security, and forensic analysis. Existing provenance based solutions for the Internet of Things (IoT) are either device-centric or platform-centric, which address only application layer attacks. Whereas any stealthy attacks performed at MAC, network, and other sub-layers which alter the behavior of IoT devices often go undetected. Provenance based forensic solutions offer effective mechanisms for investigating link and network layer attacks in IoT networks. Realizing this potential, we propose ProvLink-IoT, a novel provenance model for link-layer forensic analysis in IoT networks. ProvLink-IoT employs PROV-DM and PROV-TEMPLATE standards to model the provenance of the network. Provenance graphs are generated under both benign and attack scenarios based on the provenance logs and network traffic collected from the network. ProvLink-IoT is implemented in a simulated environment with a case study on the 6TiSCH protocol stack. We implemented three link-layer attacks on TSCH and the 6top layers of the 6TiSCH network to study their impact and perform forensic analysis. Link-IoT, a comprehensive link-layer dataset, is generated from the network provenance, which can be used in the further incident and forensic analysis. The performance impact of ProvLink-IoT on IoT network is analyzed in terms of provenance growth rate and storage overhead. Experimental results showed the efficacy of the proposed solution in correlating evidence during incident analysis and its relevance to real-time scenarios.

Internet of Things is rapidly changing the human lives to bring convenience in domestic, public and industrial environments spanning across multiple application domains. At the same time, increasing security attacks on these networks raised alarms for timely response by forensic investigators to avoid severe consequences of the attacks. Major network forensic approaches proposed so far for IoT are based on recording and analyzing the network traffic to produce suitable evidences. One of the greatest challenges in this process is the identification and correlation of suitable artifacts among volumes of network packets to reconstruct the attack scenarios during forensic investigation. To address this challenge, we propose ProvNet-IoT, a novel provenance based forensic model for investigating network level attacks in IoT environment. The interactions between different nodes at network layer are depicted using information, functional, and event modeling techniques. We use progressive network provenance to explain different events pertaining to various attack scenarios and to provide forensically sound evidences. ProvNet-IoT is validated using two publicly available labeled IoT datasets with a corpus of different attacks. Experimental results showed the benchmark performance of ProvNet-IoT in identifying selective artifacts to produce reliable evidences during forensic investigation.

Internet of Things (IoT) networks are often attacked to compromise the security and privacy of application data and disrupt the services offered by them. The attacks are being launched at different layers of IoT protocol stack by exploiting their inherent weaknesses. Forensic investigations need substantial artifacts and datasets to support the decisions taken during analysis and while attributing the attack to the adversary. Network provenance plays a crucial role in establishing the relationships between network entities. Hence IoT networks can be made forensic ready so that network provenance may be collected to help in constructing these artifacts. The paper proposes Ready-IoT, a novel forensic readiness model for IoT environment to collect provenance from the network which comprises of both network parameters and traffic. A link layer dataset, Link-IoT Dataset is also generated by querying provenance graphs. Finally, Link-IoT dataset is compared with other IoT datasets to draw a line of difference and applicability to IoT environments. We believe that the proposed features have the potential to detect the attacks performed on the IoT network.

Leakage of sensitive e-health data would severely cause threats leading to tampering of health and person related information. So preserving the privacy of the patient information is an essential feature in e-health systems. In this paper, we first explore the security limitations of the existing authentication schemes. Most of the schemes fail to provide privacy of the credentials of users when session ephemeral secrets are revealed to an adversary. To address the drawbacks found in the existing schemes, we propose a privacy preserving device authentication scheme for wireless body area networks. This proposed scheme provides robust security even if ephemeral secrets are revealed to the adversary. Additionally the proposed scheme avoids the management of large number of public-keys of application providers by the client device. Using Java Pairing-Based Cryptography Library (JPBC), we performed simulations to show that our proposed scheme provides reduced computational overhead for both the client device and the application provider.

The explosive growth of the Internet of Things offers numerous innovative applications such as smart homes, e-healthcare, smart surveillance, smart industries, smart cities and smart grids. However, this has significantly increased the threat of attacks that exploit the vulnerable surfaces of Internet of Things devices. It is, therefore, immensely important to develop security solutions for protecting vulnerable devices and digital forensic models for recovering evidence of suspected attacks. Digital forensic solutions typically target specific application domains such as smart wearables, smart surveillance systems and smart homes. What is needed is a holistic approach that covers the diverse application domains, eliminating the overhead of employing ad hoc models. This chapter presents a holistic forensic model for the Internet of Things that is based on the ISO/IEC 27043 international standard. The model has three phases – forensic readiness (proactive), forensic initialization (incident) and forensic investigation (reactive) – that cover the entire lifecycle of Internet of Things forensics. The holistic model, which provides a customizable and configurable environment that supports diverse Internet of Things applications, can be enhanced to create a comprehensive framework.