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Machine Learning is a subset of Artificial intelligence that uses knowledge from pre-existing data sets to perform new actions. In the field of cybersecurity, especially research, machine learning is used to achieve tighter protocols and standards.
With advancements in machine learning, cybersecurity is likely to become stronger against the detection of threats, alert fatigues, and zero-day exploits. It will also help systems respond to threats swiftly and prevent large-scale attacks, all while minimizing manual tasks. Machine learning and cybersecurity combined are sure to propel professionals and researchers further into developing profound cybersecurity systems.
axiusSoftware, a proponent of AI/ML research and development, believes that machine learning can be utilized significantly to protect individual and organizational data. The complete potential of machine learning and cybersecurity is yet to be fully explored, but we expect the collaboration to unlock whole new possibilities.
Let us take a look at the ways in which machine learning and cybersecurity can be optimized for better results!
Machine Learning and Advanced Protection Against Cybersecurity Threats
Machine learning can analyze large volumes of data. As a result of this ability, it can detect any anomaly that might be originating in a new data set. This anomaly, when identified as a potential threat, can be eliminated to provide advanced cybersecurity protection.
Researchers are aiming to implement machine learning techniques to help detect threats faster. Based on basic machine learning technology, Cybersecurity is likely to get more efficient.
Here are 4 primary aspects that researchers think will get better when cybersecurity and machine learning are used together.
Reduced Alert Fatigue –
This event occurs when emergency responders become desensitized to a serious threat due to fatigue. This fatigue is caused by responding to multiple false alerts that lead to professionals overlooking actual emergencies. Machine learning and cybersecurity together will enable a channel of steady alerts without the concept of fatigue venturing in.
Identify Zero-Day Exploits-
Zero-day attacks occur when hackers insert malicious software into a system. Upon identifying the vulnerabilities in the system, a hacker can draw out sensitive information. Basic machine learning can help train systems by feeding them the necessary information on previous cases of zero-day exploits and also help them identify new anomalies to help identify, manage, and terminate zero-day exploits.
Prevent Large-Scale Attacks –
One of the most significant advantages of machine learning is its capacity to process large volumes of data. With almost no maximum threshold, basic machine learning can analyze data faster and identify not just the existing gaps in the system but also the possible loopholes that can lead to large-scale cybersecurity attacks.
Faster Response to Cybersecurity Threats –
Cybersecurity Researchers are vying for machine learning integration citing the latter’s shorter turnaround time. Machine learning is quick to gather data and determine whether a case of cybersecurity lapse has occurred. It can then prompt systems to immediately take reactive measures optimally and efficiently. It is likely to make the partnership between machine learning and cybersecurity a greater success.
How Can Machine Learning Help Cybersecurity Professionals?
Cybersecurity researchers are certain that machine learning will reduce manual work by a greater margin than it has done for other sectors. With the introduction of language models such as ChatGPT, professionals with IT and other relevant backgrounds, already working in the cybersecurity field, can prompt such Large Language Models (LLMs) to run the required script.
How do cybersecurity professionals stand to benefit from ML:
Machine Learning Can Help Write Required Syntax-
Under traditional circumstances, data analysts generate queries and command cybersecurity programs such as SIEM to develop protections. This requires specific knowledge and a considerable amount of time. Machine Learning and resultant AI language capabilities will mean that both cybersecurity and IT professionals can simply ask the system to write the needed expressions. Automation codes too can be generated and adjusted as per security requirements through Machine Learning.
ML Can Read Text-Based Data –
Basic machine learning technology can analyze text-based data. This can be utilized to scan electronic communication and login details to identify any possible scenario of a cybersecurity threat. Ethical protocols can be implemented to disable the monitoring of private communications unless absolutely necessary.
ML Can Identify Threats Before They Occur –
Machine Learning can analyze and be subsequently trained to read codes that can foresee possible breaches. Machine learning and cybersecurity combined can prevent breaches with catastrophic consequences. Cybersecurity analysts currently use networks such as Central Threat Intelligence (CTI) to identify various threats. Although effective, these networks fail to identify a threat that has not been exposed to them earlier.
Machine Learning and Cybersecurity – A Changing Landscape
Machine learning and cybersecurity integration are bound to benefit cybersecurity research. With its swift approach to identifying and managing cybersecurity threats, machine learning can minimize data leaks significantly.
With greater computational power and near zero possibilities of oversight or fatigue, machine learning will help researchers and professionals, in full proofing systems, from any external attack.
We at axiusSoftware continue to research further and are deeply invested in the integration and development of AI/ML into automation, hardware, and firmware development. With the help of analytics, axiusSoftware aims to convert data into actionable insights. Visit axiusSoftware.com to know more!
