Enhancing IoT Security with ML and AI

The number of connected devices is expected to touch 20.4 billion by 2020, of these, 7.5 billion devices are expected to be used by enterprises. This validates the potential of the Internet of Things (IoT) as an enabler for automation, intelligence, scale, and efficiencies across businesses. The possibilities and applications of IoT have been growing over the last few years as it facilitates connectivity and transfer of data between everyday devices. IoT is present across sectors, from communications, healthcare, hospitality and manufacturing to transportation, covering the way for smart life, smart city, smart mobility, and smart industries.

The rapid adoption of IoT devices globally has made them vulnerable to significant risks. With the number of connected devices increasing, enormous IoT data is being generated that is transferred between physical and cloud-based network environments. The main question at this point is data security. According to a survey conducted by professionals participating in risk oversight activities of IoT devices, the proportion of organizations reporting data breach incidents specifically due to unsecured IoT systems surged from 15% to 26% between 2017 and 2019.

The dynamic nature of IoT connectivity presents a unique set of security-related complexities and those are because of Heterogeneity, Scale, Interconnectivity, Proximity, Latency, Cost, Structure, Dynamic Configuration, Privacy, Intelligence.

To protect IoT devices, technology upgrades to security solutions based on AI and ML are required. As AI and ML involve minimum human intervention in identifying and investigating abnormal activities, this would reduce the downtime and improve operational efficiency. AI security solution analyzes patterns, detects abnormal behaviors and makes error-free predictions based on datasets. It can collect information from all endpoints in the organization and run a mathematical algorithm to analyze the data, facilitating informed decision-making. ML can quickly adapt models with changing parameters, enabling IoT security systems to make real-time adjustments in changing environments. Tech leaders have applied ML to general cybersecurity practices. ML has also proven that it can identify malicious code in applications and software. ML can help both in cases where the type of attack is known and where the type of attack is unknown. For known attacks, ML can predict whether certain events are part of an attack by learning patterns from attack examples. To face daily, widespread attacks like Distributed Denial of Service (DDoS), ML models have been created that can predict DDoS attacks with >99.9% accuracy.
To secure IoT devices, companies are integrating IoT with AI and ML technologies as these facilitate real-time situational awareness, continuous monitoring and analyses, and accurate decision-making with the least human interference. In the near future, IoT devices would be a game-changer in digital transformation, powerful security providers to adopt advanced mechanisms to reduce cyber threats. This has led many global players to invest in AI-driven IoT security and upgrade their legacy security solutions.