How Machine Learning Fights Cybercrime?

Since the internet's earliest days, IT security personnel have been trying to stay ahead of the curve regarding malicious actors and other threats. Utilizing artificial intelligence (AI) and machine learning in cybersecurity has simplified this continual task by automating intricate and complex methods for detecting and reacting to attacks and security breaches. However, hackers themselves are employing the same techniques to achieve their ends.

Growing numbers of hackers and other cybercriminals are using cognitive technology and artificial intelligence machine learning to hijack Internet of Things (IoT) devices and spy on user activity.

 CSO magazine deemed 2018 as "the year of the AI-powered cyberattack." Smart malware bots are now employing AI to harvest data from vast numbers of compromised devices and can plan more insidious and harder-to-detect attacks down the road by using that information.

Cybersecurity experts must also use cognitive technology and machine learning to identify and prevent attacks accurately.

Sophisticated and Large-Scale Phishing

Human brain-modeled neural networks are a tool used in automated "spear-phishing." This technique involves the creation of phishing emails or tweets that are personally tailored to target particular users or a group of users. Research conducted by Blackhat found that automated spear-phishing had success rates between 30 to 66 percent, 5 to 14 percent higher than large-scale phishing campaigns, and on par with manual spear phishing techniques.

Automation lets attackers conduct spear-phishing operations at a troublingly large scale. However, security agents are using the capabilities of AI as a countermeasure.

A recently conducted Ponemon study shows that 52 percent of companies seek to add in-house AI experts to help them improve their cybersecurity. Additionally, 60 percent of those surveyed said AI could provide more comprehensive security than human efforts alone. That's why new security applications use machine learning to automate threat detection, which allows cyber incident investigation and response efforts to start as much as 50 times faster than before.

CAPTCHA and User Authentication

Another area in which cybercriminals are already using AI tools is breaking complex authentication codes, whether it's CAPTCHA verifications or usernames and passwords. With optical character recognition, the software can identify and learn from millions of images. It eventually achieves the ability to recognize and solve a CAPTCHA, rendering the CAPTCHA ineffective. Hackers apply the same optical character recognition combined with automated login requests to rapidly attempt stolen usernames and passwords across multiple sites in a short amount of time.

Counteracting large-scale attacks requires employing these same types of machine learning and AI. One method is for earning-enabled technology to develop recognition of what is normal or baseline system behavior, then mark strange incidents for human review. Security professionals need AI-based applications to continually monitor and provide automated help and identify which alerts pose a clear and legitimate danger.

Smart malware, which adapts and changes to become harder to detect, also poses a significant threat. Thwarting typical malware is achieved by containing and capturing the malware and reverse engineering. However, smart malware is harder to analyze since it isn't readily apparent how the neural network decides who to target.

Reverse-engineering smart malware is a challenge, but neural networks have successfully detected malicious domains created by a domain generation algorithm (DGA), which produces random domain names. Smart DGA changes constantly to defy attempts to stop it, but a smart neural network also continuously learns and adapts to the strategies utilized by cybercriminals.

Proactively Fight Security Threats

A key benefit of machine learning in cybersecurity is the ability to reveal patterns and gather knowledge from non-structured data. This grants security professionals the resources to stop attacks and awareness of new, cutting-edge threats and recommendations on how to guard against further breaches. Machine learning can also help pinpoint vulnerabilities that may elude human security teams.

While cybercriminals are already using AI to attack on a larger scale and in sophisticated ways, there is reason to be optimistic. Companies can respond by using these same technologies to their own advantage. If your company has been contemplating bringing AI into the operation but hasn't yet laid out a plan, it needs to do so as everything is now available. Cognitive technologies such as neural networks and automated security monitoring can help modernize your enterprise's defenses and give you the newest available tools to defend against emergent threats.

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