AI-Enhanced Threat Recognition and Anomaly Detection in Supply Chain IoT and IIoT

AI-Enhanced Threat Recognition and Anomaly Detection in Supply Chain IoT and IIoT

Abstract:

The integration of technologies such as the Internet of Things (IoT) in combination with logistics virtualization streamlines supply chains visibility, allowing companies to track, monitor, and analyze assets, production flows, and transport networks in real-time and with greater accuracy than ever before. While the visibility and intelligence of modern supply chains provide companies with unparalleled capability and access to data, the ever-expanding digital ecosystem is increasingly susceptible to cyber threats and creates new risks in coupled devices, autonomous systems, clouds, and partner networks. As cyber risk management, the allocation of resources becomes more automated and teleological, the role of AI in Industrial Cyber security is likely to grow more, and especially in protecting Industrial ecosystems with minimal human (e.g. operator) intervention. These systems use behavioral monitoring, pattern recognition, and predictive modeling to identify cyber threats, and even equipment tampering or data alteration, logging threats, and trends before they result in business disruption. Organizations in digitally integrated supply chains no longer can employ only reactive strategies to cyber physical threats. There is a need for proactive, automated, adaptive, and machine speed security response mechanisms. Modern AI systems, using a combination of automated reasoning, advanced analytics, and real-time data, achieve continuous inflight threat monitoring and response capabilities against ever-evolving advanced adversaries targeting the systems of critical infrastructures. This paper identifies the operational, architectural, and technological systems required to protect the IoT and IIoT-enabled supply chains and highlights the need for the design of resilient autonomous systems, and advanced processing systems. It also illustrates the increased operational dependability and cyber resilience from the safe integration of artificial intelligence systems from software security into the industrial ecosystem and the safe digital transformation of global supply chain systems.

1.0 Introduction 

The combined use of IoT and IIoT systems has made it possible for global supply chains to function with greater efficiency and perform with better operational and logistical capabilities. However, challenges remain, particularly the complexities of integrating cyber security systems (Brous et al., 2020; Boyes et al., 2018; Bauernhansl et al., 2022). The IoT has many entry points; the more of these low capability and low security components are incorporated into a tightly coupled, distributed system, the more likely these low security components are to suffer a brittle failure (Xu et al., 2018; Kashef et al., 2022; Sharma et al., 2023). The interconnectedness and the system disruption mean the consequences of a breach could escalate and propagate rapidly, the system and its disruption to a single node are rapidly replicated (Nguyen et al., 2022; Braun et al., 2021; Mahroof, 2019). The cyber security of these systems is particularly pressing and critical, as it requires the integration of AI systems that would provide these disparate IoT systems with real-time threat awareness, active threat monitoring, and predication capabilities.

Machine learning enhances how autonomous systems protect supply chain systems. It does so by understanding IoT telemetry streams and predicting operational breakdowns (Xu et al., 2018; Babiceanu and Seker, 2016; Ivanov and Dolgui, 2020). AI alters operational time frames by focusing on predicting downtimes which results in achieving near-zero downtimes. Efficient time management is achieved. (Casado-Vara et al., 2018; American Society of Mechanical Engineers, 2024; Brous and Janssen, 2020). Cyber-physical losses are reduced, and overall production losses are minimized through cloud-based predictive maintenance (Boyes et al., 2018; Nguyen et al., 2022; Braun et al., 2021). Thus, within these parameters, operational security of supply chains has improved.

2.0 Evolution of IoT and IIoT in Supply Chains

The employment of IoT and IIoT devices in transport, storage and production increases the supply chain environment’s vulnerability and the potential emergence of new attack surfaces (e.g. weak passwords, exposed firmware, and unsecured endpoint communications) (Kashef el al, 2022, Xu et al, 2018, Braun et al, 2021). IoT networks are deployed at the edge perimeter. However, relentless characteristics of IoT networks paired with the geographically distributed nature of their partners results in weak border security, such partners are heterogenous third-party vendor systems. There are documented risks users face which Brous et al (2020), Mahroof (2019), and CompTIA (2024) discuss the security of the users' devices. Moreover, the focus has shifted to chain supply DDoS bots, spoofer devices, & other ransom attacks in the realm of IIoT (Ivanov and Dolgui 2020, Boyes et al 2018, Sharma et al 2023). These developments demonstrate the gaps within the human security surveillance systems and highlight the requirements for adaptable systems to be built to automatically and effectively manage such systems on scale, velocity, and complexity.

The recognition of true and operational anomalies, the growing need for machine idle time and activity, and the offending threats stay the same, except for other automated systems (Babiceanu & Seker, 2016; Casado-Vara et al, 2018; Nguyen et al, 2022). The systems and unified smart logistics devices of discrete behavioral sensing, and the security capabilities of the systems with continuous learning are further consolidated.

3.0 The Role of Artificial Intelligence-Driven Threat Intelligence in Supply Chains

The Artificial Intelligence systems incorporated into this class gather data from consortiums and evaluate cross-contacts data in real-time. They detect and assess vulnerabilities in real-time. The systems implement machine learning algorithms to evaluate vast sets of data and classifications. The systems monitor and attempt to detect adversarial attacks in isolation and fragmented supply chains (Babiceanu & Seker, 2016; Casado-Vara et al. 2018; American Society of Mechanical Engineers, 2024). Unlike previous versions of threat-embedded systems, these systems do not depend solely on pre-timed triads of adversaries, control rules, and flow. They implement a fully integrated, adversarial, AI ecosystem to monitor contextual flows, detect and triangulate specific contextual flows that lead to alerts and supply a risk score (Boyes et al. 2018; Kashef et al. 2022; Mahroof 2019). The overall operational resilience is enhanced by these attributes of the mechanisms that reduce downtime and consequently, safeguard system operators from losses.

Artificial intelligence (AI) enabled systems to shift from performing threat intelligence to automating the assessment of the IoT and industrial systems configuration and the evaluation of credential and firmware vulnerabilities (Nguyen et al. 2022; Braun et al. 2021; Sharma et al. 2023). AI can also analyze supply chain and transactions- inventory- order- and shipping- processes to detect anomalies indicative of fraud and cyber-attack activities (Brous et al. 2020; Ivanov & Dolgui 2020; Bauernhansl et al. 2022). Organizations can analyze operational and cyber defense data to detect patterns of suspicious operational behaviors caused by a business process reengineering flow and/or indicative of cyber-attack (Casado-Vara et al. 2018; Boyes et al. 2018; Mahroof 2019). The various functionalities of AI can be harnessed and applied to counter the numerous threats posed to the contemporary supply chain. It is therefore not surprising that AI is widely applied.  

4.0 AI Based Anomaly Detection for Supply Chain IoT IIoT    

Unusual patterns or behaviors become the foundation for most IoT cyber systems. Also, the detection of anomalies is a critical task, especially when systems undergo disruptive changes, as cyber-attacks tend to follow such changes. Alterations can include deviations in the functionality of devices and in the operational performance metrics (Xu et al, 2018; Kashef et al, 2022; Nguyen et al, 2022). Artificial intelligence (AI), statistical learning, clustering, and deep neural networks are particularly built for this function. They establish behavioral baselines for the IoT and IIoT supply chain elements (Braun et al, 2021; Babiceanu et al, 2016; Sharma et al, 2023). After a system establishes a baseline, the systems are prepared to inform and warn relevant stakeholders and decision-makers of any behavioral outliers, including but not limited to anomalous energy consumption, unrecognized modifications of black box firmware, and activity on the network that is out of the ordinary (Brous et al, 2020; Casado-Vara et al, 2018; Boyes et al, 2018). Such systems are crucial because they measure the time an operational disruption can be sustained in seconds, not hours or days.

The application of artificial intelligence to recognize deviation from the norm enhances the safety and the effectiveness of the surveillance and control of the irregular behavior of industrial machines, warehouse and transportation bots (American Society of Mechanical Engineers, 2024; Bauernhansl et al., 2022; Mahroof, 2019). Vision prescription ML models assist in the estimation of the cyber-attack malfunctions predicted from the study of patterns in the industrial equipment failures’ vibrations, temperature, and pressure data (Ivanov and Dolgui, 2020; Xu et al., 2018; Kashef et al., 2022). Identifying trends moving in an opposite direction to the expected pattern is the primary focus of the analysis of transported and warehouse logistic systems of a given cargo. This involves the detection of unapproved and unauthorized access to the cargo, irregular and unauthorized just-in-time delivery, and the diversion of control systems in a manner that permits unauthorized alteration of the system (Nguyen et al., 2022; Boyes et al., 2018; Braun et al., 2021). Adaptive systems give organizations the capability to utilize new systems for modeling to provide autonomous flexibility throughout the entire supply chain.

5.0 AI-Driven Security Frameworks in Contemporary Supply Chain 

The infusion of AI in protective technologies to address modern supply chain challenges is fundamental given the growing utilization of IoT and IIoT systems to automate and integrate control of production, logistics, and asset management (Brous & Janssen, 2020; Sharma et al. 2023; Braun et al. 2021). It is also critical to recognize that the conventional defensive measures to manage cybersecurity risks will be ineffective, since static policy implementations have no actual bearing on the placement of an industrial asset in the network (Casado-Vara et al., 2018; Boyes et al. 2018; Nguyen et al., 2022).

AI security systems address the problem of real-time threat analytics, employing neural systems to monitor and react to abnormal changes in the supply chain control nodes (Xu et al., 2018; Kashef et al., 2022; Babiceanu & Seker, 2016). Such systems also enable organizations to better cyber disruptions and adapt operational rapidity to manage more cybersecure responsive supply chains (Brous et al., 2020; Ivanov & Dolgui, 2020; Bauernhansl et al., 2022). Nguyen et al. (2022), Boyes et al. (2018), and Braun et al. (2021) provide examples of the incorporation of AI technologies, including edge and federated learning, to achieve real-time threat detection while avoiding the use of cloud computing. Sensitive industrial data must be processed instantaneously for safety before the potential for operational downtime or complete shutdown occurs (Kashef et al. (2022), Sharma et al. (2023), American Society of Mechanical Engineers (2024)). The AI security models further improve the transparency and traceability of the supply chain for devices by bolstering the blockchain audits and immutable logs with supply chain devices (Casado-Vara et al. 2018, Babiceanu \& Seker, 2016, Brous \& Janssen, 2020). The increased transparency and trust attributed to the combination of AI and blockchain technologies to supply chain participants derived from the inability for fraudulent data manipulation to occur, providing sustainable control measures (Bauernhansl et al., 2022, Ivanov \& Dolgui, 2020, Boyes et al. 2018). Sharma et al. (2023) and others (2021; 2020), automated systems are described as integrated to technologies that quarantine infected endpoints, change system settings, recover from active threats, and perform system actions without human delays. Overrides system delays (Braun et al. 2021; Brous et al. 2020). These systems self-optimize to protect production and logistics systems balance loss to protect essential elements (Xu et al. 2018; 2022; Babiceanu and Seker 2016).

Mahroof (2019) describes systems that analyze supplier networks to assess and know exposed points within unprotected third parties that endanger the entire supply network (Kashef et al. 2022; Boyes et al. 2018). Szolezsi (2023), Brous and Janssen (2020), Braun et al (2021) The primary channel of the supply chain operations has digitized. The digitization of supply chain operations has raised the demand for the systems to be cyber safe and provided them with flexibility and resilience to ensure operational dependability within the cyber-physical system.

6.0. Ethical, Regulatory, Governance Concerns Regarding IoT and IIoT

The capacity of AI-enabled Internet of Things Systems to enhance accountability and transparency in supply chain management is pervasive. However, Brous and Janssen (2020), Boyes et al. (2018), and Sharma et al. (2023) mention that this impact has begun provoking some ethical and regulatory issues. Systems that are designed to capture and analyze operationally critical information should incorporate robust safeguards to prevent data repudiation, and other forms of cyber espionage, and to enhance data governance (Kashef et al., 2022; Braun et al., 2021; Nguyen et al., 2022). Moreover, the impact of the risk that will inevitably prevail in this field will be required to be justifiable, especially in relation to the impacts of automated decision-making that will revolve around issues of safety, control of major resources, or the continuance of essential activities (American Society of Mechanical Engineers, 2024; Mahroof, 2019; Ivanov and Dolgui, 2020). These issues will require a balance of governance that encompasses the need for innovation as well as for safety.

To achieve responsible and compliant systems design, the interdisciplinary building of Artificial Intelligence systems must include governing international Cyber-Physical Systems, Data Protection, and The Internet of Things, Security Standards (Brous et al 2020; Xu et al 2018; Boyes et al 2018). In the supply chain, the actors must authenticate the models embedded within the AI systems, to avoid operational noise and identify the specific chain scenarios (Braun et al 2021, Nguyen et al 2022, Casado- Vara et al 2018). These systems are part of a network and are meant to automate the operational flow of the supply chain AI. Attributes of the systems hyper- surveillance, and the controlling of PY (Babiceanu and Seker 2016; Mahmood 2019; Bauernhansl et al 2022) are confidence builders. The operational agility of supply chains with Artificial Intelligence Systems and the Internet of Things, is a hallmark of an organization within effect governance. The Impact of Artificial Intelligence on Cybersecurity in the Supply Chains of the Internet of Things and the Industrial Internet of Things. The necessity of AI cyber real-time counter threat and anomaly detection and mitigation systems is exacerbated with the increasing complexity and challenges.

Conclusion

There will be a need for advanced adaptive AI cyber defense systems in the digital supply chain. Digital supply chain ecosystems need to manage the paradox of increased transparency versus increased risk. Therefore, the ethically governed advanced technologies need to focus on automated cyber risk control systems and cyber threat mitigation. Self-learning and adaptive AI will be transformative technologies for supply chain ecosystems and digital intelligence and automation. With increased IoT and IIoT connectivity, digital supply chains will become more advanced. With increased visibility in a supply chain, more cyber risks will be encountered. AI threat intelligence enabled cyber threat reduction, mitigation in real-time, and identified anomalies. This paper discusses the need of the integrated supply chain to utilize cyber advanced cyber resilience to safeguard from sophisticated threats. 

Refences available on request