How will Machine Learning be the Next Great Cybersecurity Threat?

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Written By Nipun Singh

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It has been called a transformative technology in cybersecurity. Machine learning (ML), however, can also be dangerous when it is used by malicious threat actors. It can also pose a danger to business security. There has been a sharp rise in cyberattacks across industries around the globe. News reports about ransomware and new breaches are making headlines with alarming regularity. ML is no longer a viable solution to advancing cybersecurity threats. However, it is now the preferred tool to bypass or break into environments we thought were safe.

We predicted in November 2021 that adversarial AI and machine learning would be used by attackers within 18 months. This prediction is still valid. It means that threat actors will be equipped with adversarial AI in one year. This technology is being used by threat actors in nation-states and is now available to common criminals. Organizations are advised to be prepared. It is impossible to remain neutral in this arms race.

How do Attackers Use Machine Learning?

Adversarial AI is a machine learning technique that we are most concerned about. It exploits the decision-making and analytic capabilities of machine learning-based security software to evade detection. Adversarial AI can convince security systems it is benign. An adversarial AI, which looks like a wolf disguised as a sheep, can slip past security systems and cause havoc within the network. While remaining relatively hidden and under the radar, it is able to fly invisibly.

There are Three Types of Adversarial AI Attacks.

AI-based Cyberattacks - We already see this technique in the wild. Although it isn't yet widespread, we believe it will soon. Threat actors deploy malware using ML algorithms to attack their targets in AI-based cyberattacks. ML-powered malware is capable of automating activities that used to require manual guidance. This malware is fast, aggressive, and can act independently of the threat actor who deployed it.

AI-facilitated Cyberattacks - this involves installing malware on an attacker's endpoint and using AI algorithms on its client-side server. The attacker can then automatically scan through data and identify at high speeds to plan and optimize further automated attacks. Info-stealer malware is an example of this. It can steal large amounts of personal data using an AI algorithm.

Adversarial learning - To identify patterns, traditional ML tools need to be trained with data sets. Threat actors may use false data sets to make the algorithm incorrectly classify data. This is known as adversarial learning, or "Data Poisoning". This is currently a theoretical threat. However, adversarial learning will make ML-powered security systems obsolete as it will teach them how to classify malware harmlessly. These threats are real and serious, but they aren't yet widespread in the wild. It is possible to put together a defense, and technology is available to help you do so. It is important to act quickly today in order to lay the foundation for greater security in the future.

Cybersecurity: The Problem with Machine Learning

Security systems that use ML will not be able to stop the rise of adversarial AI attacks in the future. Basic ML-based security tools can slow down business operations and hit defenders with constant tsunami false positives, commonly known as "alert fatigue". They are also slow and inefficient at protecting against unknown malware and zero-day threats. This is a problem that sophisticated threat actors are using at an increasing rate.

ML solutions can be trained to recognize patterns. This is done by feature engineering, where the tool is manually fed pre-labeled data and is taught to differentiate between benign and malignant activity. ML is used often to analyze threat data and deal with routinely expected threats. This frees up time for security teams and allows them to concentrate on more complex tasks that require human attention. However, ML can only be trained with limited data sets that are quickly outdated. It crumbles when confronted by a new or unknown threat. It is difficult to train legacy ML to recognize a threat that has not been seen before.

Malware can now execute before ML notices that it is a threat. Ransomware that encrypts files in under 15 seconds is the fastest and activates within minutes. Using adversarial AI to feed ML false data also teaches them how to ignore threats and misclassify malicious software as benign. Attackers who use ML in cybersecurity defense will soon have a monopoly on defenders who use it.

Deep Learning

Organizations should use advanced AI of deep learning to combat ML-based attacks. This technology is becoming mainstream with Amazon, Google, and Tesla investing heavily in it to power applications like medical research, self-driving vehicles, or deep analysis of user behavior.

Deep learning uses powerful neural networks that are inspired by the human brain. Deep learning tools can be trained independently, so they can process larger datasets than traditional ML programs that require manual input. Deep learning tools can process large amounts of raw data by themselves during the training process. This data is then classified as benign and malicious, much like a human brain. When done correctly, deep learning can be resistant to data poisoning, unlike legacy machine learning, which is highly vulnerable to this type.

This enhanced training methodology is able to deep learn to recognize unknown threats, reducing the number of false positives that security teams experience. This technology is capable of identifying more complex patterns than ML at higher speeds. The fastest example of this revolutionary tech can detect and block malware in less than 20 milliseconds. Advanced technologies can even identify advanced malware before it enters an IT environment. Deep learning is fast enough to allow organizations to go beyond just mitigating attacks to change their mindset toward prevention. This is exactly where we need to get.

As traditional ML-based security tools lose their effectiveness and fail to keep pace with the sophisticated attack vectors of today, adversaries are using ML and adversarial AI. This is a critical moment in cybersecurity and requires fundamental thinking shifts. Deep learning is the answer to the problems caused by ML - it will be a major threat to tomorrow. Deep learning is the future of cyber security.

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