The world of the Internet of Things (IoT) is already within reach, but this brings both advantages and disadvantages. Machine learning can protect IoT -enabled devices from cybersecurity threats.
With the advancement of the digital revolution, many personal and commercial devices are becoming intelligent through internet access. Building Internet of Things (IoT) networks offers numerous advantages to both consumers and businesses, but it also introduces new cybersecurity vulnerabilities. Many IoT device manufacturers lack experience and knowledge in cybersecurity, even as IoT devices collect sensitive personal data more extensively, in greater detail, and more frequently than ever before.
What makes IoT security a challenge ?
Traditional security and privacy methods often perform poorly on IoT networks. The dynamic nature of IoT connectivity introduces a unique set of security-related complexities:
Heterogeneity: IoT devices come in a variety of shapes and forms, creating a wide range of hardware and software solutions.
Scale: There are already billions of IoT devices in use.
Interconnectivity: Access the network anytime, anywhere.
Proximity: Networks may rely on local devices for short-distance communication.
Latency: Sensitive applications such as surgical equipment, assembly line production, and traffic monitoring require ultra-reliable low-latency communication (URLLC) .
Cost: Most devices require low cost and low power consumption.
Structure: Vulnerabilities to distributed denial-of-service (DDoS) attacks are increasing on large, self-organizing IoT networks .
Dynamic configuration: As devices are constantly removed and added, network reconfiguration must be adaptive.
Privacy: Consumer and proprietary data must be protected, especially in healthcare applications.
Intelligence: For many IoT applications, complex decisions must be made in real time.
While many Internet access points share some of these pain points, the limitations of IoT devices and the complexity of the environments in which they operate extend these concerns beyond the scope of conventional security features.
What is machine learning ?
Machine learning (ML) encompasses many modeling techniques related to artificial intelligence. Using statistical data, machine learning models can predict outcomes for any digital dataset by identifying key features. Models can be trained on large, complex datasets ; they can also continue to improve automatically without software updates or supervision. Classic examples of ML applications include processing voice commands ( such as Siri or Alexa) or searching for features in images ( such as specific faces or certain animals ) . Where many text-based search algorithms fail, ML is able to isolate very large patterns in pixels and phonemes to find meaning.
How can machine learning improve cybersecurity ?
ML can rapidly adjust models by changing parameters, enabling IoT security systems to adapt in real time to changing environments. Technology pioneers have already applied ML to general cybersecurity practices; Google uses ML to protect its Android system, while Apple uses ML to protect your phone through facial recognition. ML has also proven its ability to identify malicious code in applications and software.
ML can be helpful in both known and unknown attack types. For known attacks, ML can predict whether certain events are part of an attack by learning patterns from attack examples. To combat widespread, everyday attacks such as Distributed Denial-of-Service (DDoS) attacks, ML models have been created that can predict >99.9% of DDoS attacks.
However, some risks remain unknown until they occur. In so-called "zero-day" attacks, digital systems are exploited through previously unknown vulnerabilities. Those trying to protect the system have zero time to prepare or patch the vulnerability. Zero-day attacks are rare, dangerous, and unpredictable. Websites like Zerodium even offer bounties of up to $2,500,000 to prevent hackers from maliciously using zero-day attacks. Cloud-based unsupervised ML technologies can mitigate the threat of zero-day attacks by detecting anomalous behavior. ML is well-suited for cloud applications spread across many tools and devices – ML systems can act quickly and automatically eliminate zero-day threats to vulnerable users.
What's next ?
ML has proven its value in general cybersecurity applications and is well-suited for addressing many IoT -specific problems. Given the rapid response times and flexibility of these ML -based systems, they can mitigate many vulnerabilities in IoT networks. Machine learning is gaining momentum across a wide range of applications and promises to demonstrate its value as an emerging technology.
