How Can AI and Machine Learning Help Prevent Cyber Attacks?

Posted by narayana on December 26th, 2019

AI systems and deep learning algorithms are already helping cybersecurity professionals develop effective solutions to fight against cyber-crime. If it weren’t for artificial intelligence and machine learning, the cybersecurity landscape would be very different than it is right now.

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As cyber threats evolve, and the attacks become more complex and widespread, conventional defense tools are often not enough to detect and stop them on time. Therefore, security solutions that are powered by machine learning are the next big thing in cybersecurity.

Thanks to their ability to learn and adapt over time, such tools can promptly eliminate well-known threats, as well as respond to new emerging risks before they do any harm, by recalling and processing data from prior attacks.

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Another benefit of artificial intelligence is the ability to perform specific tasks on its own, this way saving time and reducing the risk of human error. Unlike people, AI systems don’t make mistakes as they handle threats according to a standardized playbook, this way responding to each threat in the most effective way.

With the AI systems on their side, security experts can spend less time performing routine tasks and focus on building a stronger defense that would allow stopping sophisticated cyber-attacks before they even occur. Therefore, implementing machine learning and AI systems is crucial to stay one step ahead of cybercriminals. And yet, no technology is a silver bullet, and AI is just a tool, which can only do what criminals or security experts command it to do.

AI and ML may become new paradigms for automation in cybersecurity. They enable predictive analytics to draw statistical inferences to mitigate threats with fewer resources. Applications for automated network security include self-encrypting and self-healing drives to protect data and applications.

In the current world of data deluge, it is nearly impossible for humans alone to analyze the billions of logs generated from the existing infrastructure components. Integrating AI into the existing systems including Security Monitoring Solutions, SIEM, Intrusion Detection Systems, Cryptographic technologies, and Video vigilance systems can help in addressing many of these challenges to a larger extent. Application of AI-based technologies into the existing systems will bring in much-enhanced systems that help in better decision making. Some of the key areas wherein the functionalities of AI makes a difference are:

  • Data Mining
  • Pattern Recognition
  • Fraud Detection
  • Analytics
  • Fuzzy Logic

Development of expert Systems

Within the Cybersecurity sector, these attributes of AI can bring in tremendous benefits, out of which some of them are already in place and there are huge opportunities yet to explore. Machine learning-based antivirus systems and tools can help in quickly and accurately identifying malware like Polymorphic virus based on its continuous learning capabilities. Such systems can detect suspicious files based on the behavioral or structural analysis and it helps in detecting threats at an early stage. It can easily determine the likelihood of a malicious virus attack by analyzing and breaking down the DNA of each file.

Along with AI and ML, another aspect of security that CISOs are concerned about is compliance. Every organization needs to be compliant with numerous regulations and non-compliant to any of these can lead to heavy fines. For example, General Data Protection

Regulation (GDPR) which will be a reality in a few months can cost €20m or 4% of annual global turnover if the organization is found non-compliant. AI and ML with support of cognitive computing are enabling the enterprises to keep a track of their compliance status to avoid any legal issues.

As the digital world is moving fast, we can expect completely automated Cyber-attacks orchestrated by intelligent machines. These expert systems will have the potential to analyze the DNA of past attack models, strategies and utilize its acquired knowledge for organizing new attack models attacks that have higher success rates and larger impact. As human resources alone won’t be enough to combat this, the need of the hour for global organizations, Government and defense agencies is to suit up their existing Cybersecurity and defense environment with AI and its underlying technologies.

“Cybersecurity solutions that rely on ML use data from prior cyber-attacks to respond to newer but somewhat similar risks.”

In this way, an AI system powered by ML can leverage what it knows and understands about past attacks and threats to identify other attacks in the same vein or style.

Because hackers are consistently building upon older threats – including new abilities or tweaking previously used samples to build out a malware family – utilizing AI and ML systems to look out for and provide notification of emerging attacks could be incredibly beneficial to stemming the tide of zero-day threats.

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AI and ML have made it a bit easier to detect the proliferation of malware and identify early on in the lifecycle if a file/resource is showing signs of belligerent behavior. This level of automation has been possible with pattern detection, behavior-based anomaly detection and advanced use of heuristics – all based on Machine-learned solutions – to keep the intruders out.

Types of Artificial Intelligence Applications Being Used in Cyber Security Solutions:

It is up to the human imagination. For the sake of clarity, the following application categories can be examined:

  • Spam Filter Applications (SpamAssassin)
  • Network Intrusion Detection and Prevention
  • Fraud detection
  • Credit scoring and next-best offers
  • Botnet Detection
  • Secure User Authentication
  • Cybersecurity Ratings
  • Hacking Incident Forecasting

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