Cannabis: How To Leverage Machine Learning to Fill IoT Security Gaps

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Technology is helping the cannabis and hemp industries evolve. From cultivation monitoring systems to comprehensive seed to sale solutions and retail automation, the internet of things is changing the landscape.


The Internet of Things technology boasting of a connected ecosystem of devices, sensors, applications, and services is all set to help us with connected interconnections for a variety of contexts. As per the reports published by Statista, by next year, the number of IoT devices all over the globe is destined to reach a whopping 30.73 billion comprising all kinds of connected devices and systems at home, workplaces, public services, transport facilities, and industries.

In spite of the huge promises offered by the IoT ecosystem of devices, the concerns over data security and privacy are increasing with the proliferation of these connected devices.  In the recent past, some cyberattacks that caused huge damage and disruptions to IoT systems and app security gave such concern a real basis. Just in 2016, only the IoT botnet Mirai disrupted many reputed web services around the world.

So, the security threats with the IoT devices is a reality now that no security experts and industry strategists can look over. To ensure a safer and secure connected world, making connected devices secure has become almost imperative. In recent times, Artificial Intelligence (AI) and Machine Learning (ML) technologies have emerged to offer security solutions for modern IoT systems and connected devices.

Here through the length of this post, we are going to explain the key ways Machine Learning can enhance security and safety for the IoT devices and the entire ecosystem.

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Learning from user behavior and interactions is the greatest trend these days for detecting anomalies and abnormalities in the system. No wonder, Machine Learning is viewed with so much promise as this technology mainly focuses on learning from user behavior and interactions. But since this technology is still at the nascent stage, consolidation of the user behavior data and insights is required for better analytical output. This is precisely where cloud computing offers the right apparatus for consolidating intelligence and insights.

There are companies like Bitefinder that uses cloud computing platforms for recognizing patterns in user behavior and consolidating intelligence. Apart from this consolidation of intelligence through the cloud computing network, the analysis of local networks through endpoint security software solutions also remains important. Through cloud computing, companies can ensure enterprise-grade protection and security to the users.


Though the promise of Machine Learning for a distributed ecosystem of IoT devices is huge, in case of a cyberattack, the security responses need to be prompt and in real-time. But since Machine Learning technology is still at its nascent state, for real-time or prompt responses and security measures the role of human security experts is still irreplaceable.

In this respect, we must understand how the vast majority of Machine Learning based systems actually work to enhance security. The ML systems, by doing the user behavior analysis, actually gather insights and relevant information. Now based upon this information any unusual behavior is screened as abnormal or vulnerable from a security point of view. As the threat is triggered with an alarm, appropriate security measures are taken.  Now, sometimes common anomalies are detected as threats and such triggers can disrupt the system function.

To prevent this and to augment security output without falling prey to such false alarms, now security experts are in favor of analyzing the triggers and alarms by human security analysts and experts before taking any decisive action. With this approach, Machine Learning technology is utilized to process the raw intelligence about abnormalities and anomalies that will be further analyzed by the human security expert before actually taking any step.


IoT boasts of a huge and diverse landscape of devices and sensors of various types. This huge diversity makes it extremely difficult for the network administrators and security experts to monitor all Machine 2 Machine interactions. To add more complexity to this, there are several network communication protocols out there, ranging from Bluetooth, Zigbee, WiFi, LoRaWAN, MQTT, etc.

For tackling such challenges of extreme complexity and scale, now security experts are considering to use Machine Learning technology to scan the behavior of the device traffic and their interactions. This is also helping the experts to create proper behavioral profiles for the users. Whenever any interaction or user behavior just crosses the parameters and limits set by such behavioral profiles, a security alarm is triggered. Apart from this, the device level ML algorithms through unsupervised learning capabilities can also detect deeper anomalies, previous attacks that remained unnoticed, and other flaws.


As we just mentioned, in 2016, the Mirai botnet by infecting more than 600,000 IoT devices could make the biggest DDoS attack possible. Since IoT botnets by silently working can infect any device without even causing any performance problem, from security perspectives they are often considered as hard nuts to crack.

This is why Machine Learning technology is being looked upon as the next smarter solution for detecting botnets. The earlier signature-based detection methods may not always work as botnets with just a little different signature can remain completely unnoticed. Now, researchers are working on the use of deep autoencoders or a type of unsupervised neural network that, by learning complex patterns can detect silently active botnets in the network.


It doesn’t take any visionary understanding to assess the pace at which the number of connected devices is growing. But with the growth of these connected devices across different use scenarios, premises, environments, and contexts, the security threats are also growing exponentially. Since the IoT ecosystem always works as a loosely dependent and connected set of devices spread across an environment, without proper security protocol in place, they always remain vulnerable to attacks and threats. This is where sophisticated technologies like Machine Learning can really a massive proactive role.


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