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2020 is the Year We Use Machine Learning to Detect Network Intruders


This article is the second in a series of insights for IoT in 2020. As 2019 comes to a close, Aeris is making an assessment of the IoT industry, looking toward the year ahead, and highlighting the innovators in IoT and M2M connectivity.

The 2010s have seen rapid advancements in machine learning techniques. Bolstered by the Internet of Things (IoT), connected smart devices are enabling automation and predictions at scales never imagined before. According to a report from Zion Market Research, spending around machine learning is projected to reach $20.83 billion in 2024, at a sharp annual growth rate of 44%.

The key drivers of this uptick in machine learning are the contributions being made by IoT in the form of data generation and technological advancement. Companies in multiple industries have begun to notice the benefits of smart infrastructures and machine learning, leading to heightened demand for intelligent business processes and increased adoption of modern applications.

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What is Machine Learning?

Machine learning is a subset of artificial intelligence (AI) that uses algorithms and statistical models to perform a specific task without using explicit instructions, relying instead on patterns and inference to discover themselves. The more information that a machine learning algorithm is exposed to, the more effective it is at performing its assigned task without the need for human intervention. Therefore, machine learning algorithms are continually learning and making more accurate predictions as time goes on. The primary aim of machine learning is to allow computers to learn without human assistance and adjust their actions accordingly.

Machine learning technology has several applications that are in use today. For example, the average consumer interacts with machine learning algorithms when they visit social media platforms and retail sites. Virtual assistants like Siri, Alexa, and Google Home utilize machine learning to better serve their users, but these applications barely scratch the surface of machine learnings current applications.

The Value of Data and the Issue of Cybersecurity

Data is one of the most valuable resources in the modern age. According to MarketWatch, the Global Big Data market is expected to reach almost $120 billion by 2022, with a calculated annual growth rate of 26%. A popular catchphrase that is often repeated by economists, university professors, and CEOs is that data is the new oil a precious resource that provides unlimited potential when extracted and utilized properly.

While the comparison is slightly flawed, there is no denying the power of data. When gathered and processed by an IoT infrastructure, data has a measurably significant impact on the efficiency and effectiveness of a companys operations, which is why it must be protected with the highest levels of security infrastructure.

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2019 was one of the worst years on record for data breach activity. According to a study from Risk Based Security, 3,813 breaches were reported through the end of June 30, exposing over 4.1 billion records. Compared to the midyear of 2018, the number of reported breaches was up 54% and the number of exposed records was up 52%. Three data breaches in 2019 made the list for the ten largest breaches of all time. As data continues to grow exponentially in both abundance and value, cybercriminals will continue to seek ways to access and compromise data for their own benefit.

Protecting Data with Machine Learning

In order to protect a digital network, companies employ an Intrusion Detection System (IDS) to scan the environment for unauthorized access. An IDS monitors network traffic for suspicious activities and issues an alert when it detects a potential attack. The IDS detects these intrusions by comparing them to a pre-existing database of known attack patterns, which is called signature-based intrusion detection. While this type of IDS has been effective in the past, it is less dynamic and often obscures real attacks due to its high rate of false alarms. To better detect authentic attacks and adapt to the kinds of attacks that are common in todays data-rich environments, an IDS must take a more active and accurate role in rooting out intruders.

Cybersecurity experts view machine learning as a viable avenue for the development of a dynamic IDS. These algorithms are tasked with learning the typical pattern of a network reporting any anomalies that are detected. When bolstered by information provided by signature-based detection methods, a machine learning algorithm that understands the structure of common attacks can detect intrusions with greater accuracy than before. The goal is to use machine learning to automate the process of intrusion detection, enabling businesses to target the source of attacks, block further attempts, and optimize their network.

Aeris Protects Your Company Data

All current IDS developers are switching to machine learning techniques to combat ever-increasing security threats to networks. In 2020 and beyond, machine learning will take a central role in detecting and preventing cybercriminals from gaining access to valuable company data. With proper attention paid to cybersecurity bolstered by machine learning, the tools we use in 2020 to fend off cyberattacks will continue to grow in sophistication along with the types of attacks we will encounter.

Aeris believes that IoT needs security at the design stage, not as an afterthought. We have developed the tools and expertise to mitigate security risks with responsible development of IoT applications. With our cloud-based Aeris Fusion IoT Network, we make it easier than ever for companies to securely manage and optimize their IoT/M2M deployments.

To learn more about our secure IoT services, connect with Aeris today.