IoT Edge Distributed Environments
May 26, 2023
There was a plethora of announcements at Embedded World 2023 regarding high-performance rugged edge modules, strategic collaborations for new edge computing solutions, and software solutions catering to machine vision and object recognition applications. One critical aspect that has remained constant and serves all customers across various industries, including the automotive, retail, manufacturing, healthcare, agriculture, and energy sector, is edge security.
In the digital age, when edge computing has become an indispensable part of many industries, it is important to secure endpoint locations, distributed edge resources, and networks from novel cyber threats. AI Edgelabs is an edge security platform that leverages artificial intelligence and deep learning algorithms to detect and respond to cyber-attacks in real-time.
AI EdgeLabs’ security platform leverages machine learning algorithms which are deployed on the IoT edge gateway device to continuously monitor traffic patterns and other operating system’s telemetry data, such as CPU, memory, and disk input/output. This allows for early detection and mitigation of potential zero-day attacks, providing an extra layer of security to edge computing systems.
Zero-day attacks are a significant concern in the edge computing ecosystem, as they are not previously known to security teams. These attacks can result in unauthorized access to devices, communication channels, and networks, which traditional security solutions are unable to mitigate.
This article emphasizes the importance of edge security for embedded systems. Customers who opt for edge modules from different manufacturers must be aware of the need to secure their IoT edge distributed infrastructure against malicious actors.
Is edge security crucial for the embedded industry?
Multi-access edge computing (MEC) has opened new business opportunities for organizations by providing a distributed resource infrastructure with nodes located at remote locations, it has also brought a huge security challenge.
In the telecommunication industry, for instance, 5G connectivity and MEC are heavily relied on to reduce delays, but these technologies are also vulnerable to Domain Name System attacks. Such attacks could cause a complete loss of connectivity that could last for days, which would be highly detrimental to any organization that relies on these technologies.
AI EdgeLabs is providing its services to a telecommunication company that was targeted by a DNS-based attack, whereby the hacker had control over several edge devices. AI EdgeLabs’ security solution helped to protect the telecommunication firm from attacks on its MEC networks that led to unauthorized data access, the elevation of privileges, and cloud intrusion.
In most cases, the malicious actor exploits software vulnerabilities in the MEC network to gain entry into the edge infrastructure and exploit other MEC components and internal interfaces. The telecommunication company chose to work with AI EdgeLabs due to its quick integration through a Helm Chart to integrate the solution with the Kubernetes cluster installed across various nodes in multiple clusters spanning different regions. The integration process between the AI EdgeLabs security solution and the client’s system was completed in less than 24 hours.
The integration of edge computing, 5G connectivity, and AI algorithms has led to explosive growth in the automotive industry. With thousands of IoT edge devices and servers connecting to each other, automotive companies can process edge data and update networks in real time. However, if any of the IoT servers fall victim to a ransomware attack, companies may suffer significant financial loss and sensitive data breaches.
The deployment of thousands of IoT edge devices can lead to the challenge of identifying managed and unmanaged devices, which in turn can pose a cybersecurity threat. In light of the growing threat landscape, it is crucial for companies 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 IoT edge infrastructure.
In addition to network visibility, AI EdgeLabs features early prediction of potential threats using machine learning and reinforcement learning algorithms. This allows the system to identify threats before they occur and provide an extra layer of security against cyber-attacks. Furthermore, the company provides risk assessment capabilities, which enable businesses to identify potential vulnerabilities in their edge infrastructure and take proactive steps to mitigate them.
Cybersecurity solution for IoT edge distributed environments
At Embedded World 2023, AI EdgeLabs received the Best-in-Show award for AI and machine learning. This award serves as a testament to AI EdgeLabs’ expertise and innovation in edge security solutions that address the growing security challenges in embedded systems.
Businesses undergoing digital transformation and utilizing edge computing will have an even greater competitive advantage as it offers advanced machine learning models to detect and respond to novel cyber threats quickly.