Cyber security is a highly volatile, changing landscape by its very definition. Ensuring efficient defensive posture requires continuous threat monitoring, developing defensive strategies to meet them, and implementing new countermeasures. This challenge gets far more complicated when applied to the evolving field of IoT. Traditional security controls are simply not effective enough in the IoT landscape that connects a myriad of devices of different capacities and performing feats of different strengths.
Internet of Things (IoT) can be broadly understood as a global network of computing devices equipped with sensors and an IP address to communicate via the Internet. What makes security particularly challenging for IoT devices is that these devices can be incredibly versatile which defies the scope of any industry-wide security solutions. Also, these devices are designed to be low cost, energy-efficient, and often armed with nothing more than a simple password (if that). This makes IoT devices incredibly prone to hacking attempts.
How AI can help in IoT cyber security with data analysis
If IoT devices excel at one thing, it’s gathering information with its myriad of sensors. AI can help IoT devices in parsing through an unimaginable amount of data in very short timeframes. The combination of AI and IoT can give organizations greater visibility and control even with its huge array of devices and sensors that communicate through the Internet. In other words, AI can enable businesses to transform the collected data into valuable and more importantly, actionable information with IoT. This is especially important when it comes to safeguarding the devices and the network from unauthorized access and penetration attempts.
Security Concerns in IoT
There are multiple factors that go into making cyber security a challenging proposition for IoT devices. The field is incredibly vast in its size and scope and IoT is made up of an incredible variety of devices, including old and new ones – each with their own operating systems and many security vulnerabilities. This heterogeneity makes IoT networks incredibly difficult to cover with a single source of defense as can be implemented when it comes to operating systems like Windows or Mac. Moreover, since IoT devices are designed to be affordable, they are often very low-powered, energy-efficient devices with no or minimal security frameworks built into them. Each network consists of thousands, if not millions of these devices feeding it data through the Internet – making the whole security proposition a virtual nightmare with incredible operational complexity. Even at the bare minimum, the networks need to ensure regular updates for all the operating systems, network applications, keeping stock of new assets, gauging security risks, detecting potential targets etc. This is what has made security professionals turn to AI for help in combating against IoT cyber security threats. Know more about how to protect your IoT network against highly evolving threats by reaching out to a local security services provider in IT consulting Vermont.
AI in IoT Cybersecurity
The base step towards building any sort of security framework for IoT lies in the identification of all the devices on the network. For larger networks with millions of sensors and devices, this can be a stupendous task. With AI, however, the discovery process becomes much easier and provides thorough, detailed information on the nature of the devices. Effective network security is a result of identifying and monitoring every node in a network and this identification and asset management capacity of IoT makes it highly effective in IoT cyber security.
Secondly, AI can also help in IoT cyber security through data analysis. AI does not tire or fall asleep at the wheel and is much more effective than humans at continuous surveillance of vast IoT networks in its search for anomalies in activity. Unfortunately, this also leads to many cases of false positives as any anomaly can be thought of as a potential breach. This is addressed through the use of ML and teaching AI to recognize attack patterns and reduce the load of false positives through other irregularities. Unfortunately, our ability for modeling effective attack patterns is rather limited as actual breach data from real attacks is rarely revealed due to privacy concerns. This makes our capacity for quality of analysis somewhat limited. You can assess you’re the security vulnerabilities in your IoT network by reaching out to a local provider in IT Services Vermont.
Applying Machine Learning to IoT Networks
Machine learning is incredibly useful in identifying potential threats, discovering vulnerabilities in the network and identifying systemic IoT vulnerabilities, such as, lack of or weak password protection on IoT devices, and addressing network configuration to build in defenses. ML works on the basis of massive cyber security datasets and IoT device profiles, which makes zero-day threats a worrying issue for many companies. But zero day threats aside, ML has proved to be highly effective in combating against DDoS attacks and improving the overall security profile of IoT networks. With early threat identification facilitated by ML, it can also help manufacturers design devices that are more secure by design and to send out security patches in a timely and effective manner.
To further improve cybersecurity for IoT, data from machine learning also helps IoT developers create more secure devices. By identifying vulnerabilities early, developers send out security patches, if possible, or create new versions of the devices to better protect users.
With most existing IoT devices lacking in any or effective encryption and security frameworks, ML can be highly effective in providing adaptable and flexible IoT security at a network level. This is a far cry from the asset management nightmare of IoT when trying to implement device-specific security and also comes at a much more manageable cost outlook for companies deploying IoT frameworks. IT support providers in Vermont can help you with efficient single-source ML-based implementation of IoT security frameworks. The same approach can even be adapted for home usage or smaller deployments of IoT to recognize threats early and warn users of any anomalies in their network.