Distributed IoT Edge Environments – Embedded Computing Design

May 26, 2023


Image provided by Serhiy Protsenko

At Embedded World 2023 there were numerous announcements on high-performance rugged edge modules, strategic collaborations for new edge computing solutions and software solutions for machine vision and object recognition applications. One critical aspect that has remained constant and serves all customers across multiple industries including automotive, retail, manufacturing, healthcare, agriculture and energy is edge security.

In the digital age, where edge computing has become an indispensable part of many industries, it is important to protect endpoint sites, distributed edge resources and networks from evolving cyber threats. AI Edgelabs is an edge security platform that uses artificial intelligence and deep learning algorithms to detect and respond to cyberattacks in real time.

AI EdgeLabs security platform leverages machine learning algorithms deployed on the IoT edge gateway device to continuously monitor traffic patterns and telemetry of other operating systems, such as CPU, memory, and disk I/O. This enables early detection and mitigation of potential zero-day attacks, providing an additional layer of security for edge computing systems.

Zero-day attacks are a major concern in the edge computing ecosystem because security teams were previously unaware of them. These attacks can lead to unauthorized access to devices, communication channels and networks that traditional security solutions cannot prevent.

This article emphasizes the importance of edge security for embedded systems. Customers who choose edge modules from different vendors need to be aware of the need to protect their distributed IoT edge infrastructure from malicious actors.

Multi-access edge computing (MEC) has opened new business opportunities for enterprises by providing a distributed resource infrastructure with nodes in remote locations, and it has also brought with it a major security challenge.

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For example, the telecom industry relies heavily on 5G connectivity and MEC to reduce delays, but these technologies are also vulnerable to Domain Name System attacks. Such attacks could result in a complete loss of connectivity that could last for days, which would be extremely damaging to any business that relies on these technologies.

AI EdgeLabs is providing its services to a telecom company that was the target of a DNS-based attack that gave the hacker control of multiple edge devices. AI EdgeLabs’ security solution helped protect the telco from attacks on its MEC networks that resulted in unauthorized data access, privilege escalation, and cloud intrusion.

In most cases, the malicious actor exploits software vulnerabilities in the MEC network to gain access to edge infrastructure and exploit other MEC components and internal interfaces. The telecom company chose to work with AI EdgeLabs for quick integration via a Helm chart to integrate the solution with the Kubernetes cluster installed on different nodes in multiple clusters in different regions. The integration process between the AI ​​EdgeLabs security solution and the customer’s system was completed in less than 24 hours.

The integration of edge computing, 5G connectivity and AI algorithms has fueled explosive growth in the automotive industry. With thousands of IoT edge devices and servers connected, automotive companies can process edge data and update networks in real time. However, should any of the IoT servers fall victim to a ransomware attack, companies could experience significant financial losses and sensitive data breaches.

Deploying thousands of IoT edge devices can lead to the challenge of identifying managed and unmanaged devices, which in turn can pose a cybersecurity threat. With the growing threat landscape, it is critical for organizations to monitor and manage network traffic and IoT edge devices in unmanned remote locations. To address this challenge, AI EdgeLabs provides advanced network visibility capabilities that strengthen the security of the IoT edge infrastructure.

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In addition to network visibility, AI EdgeLabs provides early prediction of potential threats using machine learning and reinforcement learning algorithms. This allows the system to detect threats before they occur and provide an extra layer of security against cyberattacks. In addition, the company offers risk assessment capabilities that enable organizations to identify potential vulnerabilities in their edge infrastructure and take proactive measures to remediate them.

At Embedded World 2023, AI EdgeLabs received the Best in Show award for AI and Machine Learning. This award recognizes AI EdgeLabs’ expertise and innovation in edge security solutions that address the growing security challenges in embedded systems.

Organizations undergoing digital transformation and leveraging edge computing will have an even greater competitive advantage as it offers advanced machine learning models to quickly detect and respond to emerging cyber threats.

Serhiy is a doctor of science and a professor of physics. He has more than 20 years of theoretical and practical experience in the field of data science and has implemented numerous projects based on AI technology.

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