In today's highly interconnected digital age, threats in the cyberspace are multiplying in number as well as sophistication. Hackers continuously evolve new ways of breaching networks, extracting sensitive information, as well as sabotaging the system. Conventional cybersecurity measures cannot keep pace with the changing threat horizon. Here is where machine learning (ML) is stepping in. ML is revolutionizing cybersecurity with the power of detecting threats more rapidly as well as more effectively. In this article, we'll see how ML is being applied for the enhancement of cyberspace defenses as well as touch upon a number of actual uses.
The Need for Security through Machine Learning
Cybersecurity professionals have their plates full with sheer volumes of events as well as alerts every day. Manually sorting through the mountain of data for threats is finding the needle in the haystack. Further, computer threats have also become subtle as well as advanced, easily falling under the radar of traditional signature-based detection. Machine learning is the answer because machine learning automatically processes large volumes of data identifying subtle patterns as well as outliers posing threats. By being pre-trained from experience as well as new data, ML models can identify threats humans as well as traditional security appliances miss.
How Machine Learning Can Detect Cyber Threats
Machine learning models work well when detecting patterns as well as outliers from normalcy. In the field of cybersecurity, ML models also train with large databases of normal system traffic, normal system use, as well as recognized threats. ML models can be trained as benign versus malevolent activity classifiers. Deployed models can scan continuously, alerting when suspicious events occur. Some popular ML-based techniques used in the field of cybersecurity include:
Anomaly detection: ML models detect the normal use patterns of the devices as well as the people. Any deviation from normal use, such as when the user is accessing from a new or non-familiar location at non-standard times, is considered suspicious event.
Malware discovery: ML models can identify the distinctive characteristics of the malware from the code as well as the pattern of the recognized specimens. It makes them identify new as well as unknown strains of malware evading traditional signature-based detection.
Network intrusion detection: ML can identify indications of a would-be attack through traffic pattern analysis in the network, such as port scanning, DDoS attack attempts, and attempts at exfiltration. Having the awareness of the normal traffic pattern of a network, ML models can identify minor abnormalities as indications of the presence of an attack.
User and entity behavior analytics (UEBA): It uses machine learning for the creation of normal behavior models of the devices as well as the user. It monitors any abnormality reflecting a hacked account or inside attack.
Real-World Applications of ML in Security
Most of the vendors apply machine learning in their solutions for advanced threat detection as well as responses. For example:
Darktrace applies the capability of unsupervised machine learning to build a "pattern of life" for each user as well as each device within the organization. Darktrace's Enterprise Immune System is then able to detect threats as well as respond to them in real-time without prior knowledge of the threat.
Cylance's endpoint security tool, CylancePROTECT, uses ML to scan the file DNA for benign or harmful characteristics. It is thus able to prevent new, as-yet-undiscovered malware from evading traditional antivirus.
Securonix's SNYPR incorporates machine learning-driven advanced UEBA. It builds behavior baselines for all the entities as well as iteratively learns from end-user feedback in a bid to continuously update threats detection.
Challenges and Future Directions
While machine learning is a great tool for cybersecurity, there is no panacea. ML models can be as great as the set of data upon which they have been trained. In the event the set of training is marred with biases or gaps, the model's attack detection won't be as great. ML models can be deceived with cleverness through blind spots as well as biases. Security vendors have the ongoing task of refreshing as well as honing their ML models in light of changing threats.
As ML continues to grow, there are possibilities for even greater applications in the field of cybersecurity. More sophisticated machine learning techniques such as RNNs would be capable of capturing sophisticated long-duration patterns of network and user activity. Reinforcement learning would be capable of enabling the autonomous learning of the skill of responding to threats as they occur. Cross-organizational cooperative learning would be capable of creating more robust, more transferable threat detection models.
Conclusion
Machine learning is the cybersecurity game changer. It is the equaliser when coupled with automated threat discovery and active defence from more advanced threats. As threats keep changing, ML will be the enabler of security teams being able to have the upper hand. But achieving the optimal use of ML for cybersecurity is going to be about ongoing research, advancement, and collaboration between the academy and the sector. It is sure: the future of cybersecurity is with the people who leverage the power of machine learning.
References
- Cylance. (n.d.). CylancePROTECT: AI-powered endpoint protection. Retrieved from https://www.cylance.com/en-us/products/our-products/cylance-protect.html
- Darktrace. (n.d.). The Enterprise Immune System: Self-learning cyber AI technology. Retrieved from https://darktrace.com/en/enterprise-immune-system/
- Securonix. (n.d.). SNYPR: Next-generation SIEM with user and entity behavior analytics. Retrieved from https://www.securonix.com/products/snypr/
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