Cybersecurity is an industry that doesn’t evolve at its own pace – it moves at the speed of those who seek to undermine it. There will always be those out there attempting to break through established security practices and profit from network security vulnerabilities, a worrying trend that was substantially amplified by the 2020 pandemic.
An unfortunate side effect of our global shift to remote work was the big influx of companies entering into a digital working paradigm, many of which weren’t prepared for the cybersecurity risks they would face. And as you’re about to find out, machine learning malware detection may be exactly what these companies need.
According to the Sophos 2021 Threat Report, survey results show 34% of organizations experienced malware-based security incidents in 2020, not counting security incidents related to exposed data, ransomware, account compromising, or cryptojacking.
But organizations aren’t without tools to fight back against malware and cybercrime. One of the latest areas under exploration is malware detection with machine learning – a powerful new way to analyze emerging threats.
How Machine Learning Stops Malware
All modern antivirus/anti-malware programs leverage algorithms to detect threats.
Broadly, these programs are connected to databases of known virus signatures and suspicious traits – as well as databases of benign code – that offer a reference for whether a bit of code may be dangerous. When the platform identifies a threat, the code is quarantined or neutralized.
The problem is that malware developers have gotten savvier over the years, building more sophisticated threats that evade common “red flag” signatures.
Malware detection systems have had to evolve in kind, with developers building more advanced security protocols capable of matching these bigger threats. It’s a never-ending arms race between hackers and security professionals, and organizations are prime targets.
Toward the end of 2020, the FTC issued a warning of increased ransomware attacks affecting organizations in the previous year, noting that targets weren’t exclusively businesses – other common targets included non-profits, school systems, government agencies, healthcare centers and others.
With such a large number of cyberthreats occurring every year, it’s no wonder malware detection using machine learning has become a hot topic. Machine learning virus detection offers a number of advantages over the systems most businesses may be familiar with.
How Machine Learning Malware Systems Differ
Machine learning malware analysis involves connecting malware detection platforms to cloud servers with an available pool of Big Data to draw from. They leverage heuristic algorithms to detect threat patterns and apply more advanced malware screening than you’ll find from typical consumer antivirus programs.
Machine learning algorithms are necessary for large scale malware classification. These advanced tactics could include working with a convolutional neural network and deep learning models to create a comprehensive perspective on threats that goes well beyond basic virus detection.
The cloud component is essential here, as any state of the art malware detection system requires a substantial amount of information on which to base calculations. This is a foundational element for any type of deep neural network, as client-side platforms contain much smaller data sets to draw from.
This limits their ability to process and analyze the big picture trends necessary for real world threat detection, as well as the ability to develop more advanced learning techniques (such as “random forest” algorithms) that can be baked into neural networks.
Machine learning and malware tools feature engineering components that traditional security systems simply can’t match – and the same goes for threats. With open access to Big Data and advanced algorithms, machine learning malware detection represents a powerful cybersecurity practice that professionals should keep an eye on.
Fighting Back Against Cybercrime
Malware detection and machine learning are two applications that go well together. After all, programs used to detect malware are only as good as the frameworks they’re built on, and without a way to keep virus definitions up to date, threat actors will always be one step ahead.
Machine learning malware detection represents a new era of cybersecurity: one allowing security professionals to keep pace with emerging threats and maintain a thorough, up-to-date platform for malware analysis.
Stay up to date with the latest trends in cybersecurity by keeping in touch with Quadrant Information Security! Whether you’re looking for industry updates on security trends or professional services to bolster your business defenses, our experienced team will be behind you every step of the way.