Machine Learning as an Essential Tool for Cyber Security
Posted on Feb 20, 2020 7:00 AM. 4 min read time
Organizations have never been so dependent on the digital ecosystem and the Internet. They implement new technologies hastily without realizing that these new technologies could pose new cyber threats. Globally, businesses of all sizes experience reputationnal damage due to hacker attacks and suffer appalling losses that increase every year.
In 2018 alone, cybercrime revenues hit $1.5 trillion and are expected to exceed $2 trillion by the end of 2019. According to a recent study by Accenture, cybercrime could cost businesses over $5 trillion within the next 5 years. Today, only 30% of companies invest heavily in their IT security, adopting state-of-the-art technologies like offline password storage or complex data protection systems.
For that matter, are there any emerging solutions that might change this doom-and-gloom scenario?
Organizations have never been so dependent on the digital ecosystem and the Internet
Machine learning (ML) is changing the cybersecurity landscape now. It is a type of Artificial intelligence (AI) capable of learning from experience. Actually. ML is as old as computers, but it is gaining momentum due to the rapid growth of data in the digital sphere. As of today, the volume of such data is about 33 ZB (zettabytes) (one zettabyte corresponds to one trillion gigabytes). It is estimated to reach 175 ZB by 2025. Intelligent systems that utilize machine learning and deep learning can help businesses in two ways: 1) they simplify the analysis of big data, and 2) they use this data to grow their intelligence and solve complex tasks.
How can it be used to enhance cybersecurity?
In a nutshell, ML-powered tools can analyze huge sets of data, detect anomalies, and predict threats. Let’s find out how these ML capabilities help businesses:
- Spam filtering. Email remains the dominant form of communication, especially in the business sector. Being aware that human error is the biggest vulnerability, criminals are actively utilizing phishing attacks to cause data breaches. In this case, machine learning technology is the best way to detect and filter out messages with potential spyware. Advances in ML algorithms made finding malware more efficient and accurate than ever before. Automatic filtering of undesired phishing emails based on ML eliminates human factor issues and reduces the potential threat of data breach.
- User behavior recognition. A special resolution is needed when the threat appears on the user level. There is no universal algorithm to recognize all types of user behavior. Nevertheless, many companies use ML to detect anomalies in user activities and classify them for further analysis. It allows to collect data on normal user behavior and raises a red flag on abnormal behavior preventing unauthorized login.
- Network anomaly detection. ML-powered tools are capable of processing large-scale network traffic data in a fast and effective manner. Since technology revolves around self-learning, it improves automatically when new data (new attack patterns) is received. The system studies patterns, structures, and rules within this data. It then performs a thorough analysis of the traffic at every layer and detects anomalies more accurately.
- Fraud detection. Banking and commercial institutions often fall victim to fraud. Most companies still use popular tools with a determined set of rules for detecting fraud patterns. Despite constant updates to rule-based systems, they are inefficient when it comes to discovering new, sophisticated frauds. At this moment in time, given the increasing volume of transactions and information to process, ML-based systems are the best available solution for fraud detection. As mentioned before, it can determine normal and abnormal activity across a huge amount of transactions in real-time. The ability to adapt to new, unknown fraud tactics makes ML a universal tool.
Companies that already apply ML tools for security purposes
Most companies—from giants such as Google, Apple, Amazon, Microsoft, Cisco, PayPal, etc., to various startups—are moving away from rule-based technologies and introducing ML systems.
Millions of people log into Google’s Gmail every day. Using machine learning, Google easily detects unauthorized logins and tracks different aspects of user behavior during and even after a login session. It also allows for early phishing, malware, and spam detection. Over time, the accuracy of algorithms improves, and the cost of security maintenance decreases.
The future of machine learning in cybersecurity
No doubt, ML tools, and systems are reshaping the future of cybersecurity. Researchers are constantly experimenting with ML to polish the technology and make it more efficient in detecting and preventing cyber threats. Currently, however, ML alone is not sufficient to completely eliminate cyber attacks. Therefore, machine learning will likely be combined with other technologies to enhance security. Will these duos be effective?
Machine learning + Internet of Things (IoT)
IoT is a network of different interconnected devices (things) that are able to share data and process it without human interaction. It’s one of the fastest developing and popular technologies and is expected to control over 30 billion connected devices by 2020. IoT is changing the online business ecosystem a lot, but security remains the top concern.
Since IoT connects billions of devices, it’s impossible to manually identify and stop suspicious activity. This is where ML comes in handy. Processing large data sets, machine learning tools effectively detect vulnerabilities and threats at early stages, before critical situations arise. ML-powered systems automate security practices while evolving and becoming even more efficient in the long run.
Machine learning + blockchain
Cutting-edge technologies like ML and blockchain are robust on their own, but they can produce incredible results when combined together.
Blockchain is a distributed ledger (or database) protected against unauthorized tampering. Machine learning can maximize a blockchain’s potential, improve the deployment of blockchain-based apps, enhance the security of blockchain nodes, and prevent system breaches. While the experiments on combining these two technologies are carried out, it’s already obvious that they complement each other, bringing security to new levels. ML will benefit from access to big data, which it will manage and learn from, while the blockchain will take advantage of faster transaction verification and validation.
Since there is no single system that ensures ultimate security at all layers, cybersecurity somewhat remains an art, where the greatest minds compete using the most innovative technology available today.