HomeCyberSecurityApplication Examples Of Machine Learning In Cyber Security

Application Examples Of Machine Learning In Cyber Security

The following examples serve to explain further and underline the importance of machine learning for cybersecurity:

Privacy Classification And Compliance

Avoiding violations of data protection laws have probably been a top priority in your organization for the last few years. Management of the aggregated data of your customers and users must comply with these laws, which usually means that the data must be available for deletion upon request. Failure to comply with these laws could result in significant fines and damage your organization’s reputation.

Data classification allows you to separate user data from anonymized and de-identified data. In particular, large or older organizations can save themselves the trouble of manually separating vast amounts of old and new data.

Security Profiles Based On User Behavior

Behavioral profiles based on network personnel could result in a security solution tailored to your organization. In this model, the description of user behavior can then be used to record what an unauthorized user might look like. Subtle quirks like keystrokes can become a predictive threat model. By pointing out the possible consequences of any unauthorized user behavior, ML security can recommend how open attack surfaces can be reduced.

Security Profiles Based On System Performance

Similar to the concept of user behavior profiling, a healthy PC can be used to create a customized diagnostic profile for your computer’s overall performance. Processor and memory usage, along with specifics such as exceptionally high online data usage, can indicate malicious activity. Nevertheless, some users can experience high data consumption regularly due to video conferences or frequent downloading of large media files. But by learning what system performance is typical, you can spot when it’s deviating, similar to the user behavior rules we provided in an earlier ML example.

Behavior-Based Bot Blocking

Bot activity can severely impact bandwidth for inbound traffic to websites. This applies above all to those sites whose business model depends on Internet-based business transactions, such as dedicated online shops for which there are no physical stores. Serious users then experience the website as highly sluggish, which leads to a drop in traffic and business transactions.

ML security tools classify such activities and can block the botnet independently of the tools used, such as virtual private networks (VPN), which can anonymize. Behavior-based data points about the malware can help a machine learning security tool build predictive models for that behavior and proactively block new web addresses that exhibit the same activities.

The Future Of Cyber Security

Despite the much-heralded future of cybersecurity, there are still numerous hurdles to overcome. ML requires records, but this could conflict with applicable data protection laws. Software training systems need vast data points to build accurate models, which is not always compatible with the “right to be forgotten.” Some data that gives clues to human identity can lead to data protection violations, for which potential solutions must be sought. A solution could be found in systems that make it virtually impossible to access the original data after training the system. There are also already considerations about anonymizing data points. However, further investigations are also necessary to avoid a distortion of the program logic.

The industry lacks AI and ML cybersecurity experts capable of programming at this scale. Network security based on machine learning could make great strides if it could be maintained by skilled people and adjusted as needed. However, the worldwide pool of qualified, well-trained experts is smaller than the huge global need for employees who offer these solutions.

Human teams remain indispensable. And ultimately, critical questioning and creativity become an essential part of decision-making. As mentioned, ML isn’t capable of either, and the AI technology ​​isn’t any better. To move further along this path, one would have to leverage these solutions to expand the existing teams.

Three Tips For Dealing With The Cyber Security Of The Future

There are several steps on the road to an AI security solution that will bring you one step closer to the future:

  • Invest In The Future Viability Of Your Technology

As threats become more sophisticated, a successful hack that results from outdated technology or the use of redundant manual labor costs you far more. Whoever stays ahead in this race can already contain the risk. 

  • Add AI and ML To Your Teams, Not Replace Them

There are still vulnerabilities; no system on the market can offer 100% protection. Because clever attack methods can fool even these adaptive systems, you should ensure that your IT team learns how to work successfully with this infrastructure.

  • Regularly Update Your Data Policies To Stay Compliant With Emerging Laws

Privacy has become a vital issue for government agencies around the world. Therefore, data protection will be at the top of the agenda for most companies and organizations in the foreseeable future. Be sure to follow the latest guidelines.

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