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Artificial Intelligence AI and Machine Learning ML in Cybersecurity


Artificial intelligence (AI) and machine learning (ML) are present in every second of our lives, whether we realize it. Smart devices decide when to turn on our heaters and lights the moment we wake up, social media uses complex algorithms to determine what news to promote to us, and Google Maps guides us through our day. Even while we sleep, AI monitors our sleeping patterns, identifying when we have had a good night’s sleep and even monitoring our health, thanks to the proliferation of smart devices like Google Home and Apple Watches.

Machine learning and artificial intelligence have gradually altered how we interact with technology in our daily lives. We put technology to good use by using virtual chatbots to assist the elderly, prevent poaching, and provide real-time translation for migrants.

In the last decade, the cybersecurity industry has been at the forefront of this technology, employing artificial intelligence and machine learning in various applications, including combating large volumes of malware, detecting spam and business email compromises, analyzing network traffic, and using facial recognition, among others. Nowadays, it’s difficult to avoid hearing about a vendor’s machine learning and artificial intelligence (ML and AI) during their presentation. This blog will demystify “ML-enabled” security solutions and hopefully provide some new perspectives to our readers in their decision-making.

What are Machine Learning and Artificial Intelligence?

Let’s start with some essential machine learning and artificial intelligence definitions. Allowing computers to learn how to do something is known as machine learning. AI is the goal of applying the knowledge learned, and it requires input such as training data and expertise. AI tries to solve data-driven business or technical problems by assisting users in making decisions for them (if we programmed it in such a way). It can be used to quickly analyze large sets of data that no human brain could possibly process and make AI-assisted decisions and conclusions on a given issue when necessary.

Is there such a thing as perfect artificial intelligence? Not all of the time. Any computer program is only as good as its author, and any machine learning or artificial intelligence system is only as good as its fed data. There are well-known examples of programmatic biases in AI algorithms and models of chatbots going rogue after being trained with incorrect data. While there is still work to be done, these algorithms have the potential to outperform even the most fallible humans.

AI-based Malware – Reality or Myth?

There is little evidence to support the belief that criminal cyber gangs are already using AI to help generate new strains of malware, despite a lot of hype and clickbait. However, there is evidence that artificial intelligence and machine learning are being used in other areas to circumvent protective security measures.

  • Deep fake videos and images are created to phish users and circumvent security measures. Creating fake identities is especially common on social media sites.
  • They are bypassing authentication protections by solving CAPTCHAs.
  • To target attackers, open-source intelligence on organizations is gathered.

Consider the use cases you’re trying to achieve when deciding which security solutions to invest in. Understand how threats evolve and the tactics and techniques used by black-hats. Then inquire why you were unable to prevent these attacks with the resources you had at the time. It’s easy to get swept up in the AI/ML craze. On the other hand, customers are beginning to think creatively about practical use cases, such as detection, forensics, hunting, and mitigation.

Heuristics and adaptive malware were the significant changes in the malware industry that prompted the need for AI. The industry experts went to handle from a volume of malware that could be manually processed to a situation where the number of samples was growing at an exponential rate almost overnight. Malware analysts had to adapt and use artificial intelligence and machine learning.

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