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Cracking the Code with Machine Learning in Cybersecurity

Rick Heicksen By: Rick Heicksen


In 2021 the Colonial Pipeline experienced a ransomware attack. This attack caused the Colonial Pipeline Company to suspend all operations and pay a total of $4.4 million to the hacker group. The pipeline restarted five days later. However, many states felt fuel shortages and even affected travel for some. This occurred because of the consistently evolving and sophisticated nature of cyber threats; the challenges being faced are plentiful.

With the vast majority of our modern lives being more technologically connected than before, cybersecurity has become a topic of discussion. It's no secret that we have become incredibly digitized; with social interactions, banking, entertainment, and so much more being available at our fingertips through our phones, cybersecurity is a crucial part of maintaining it all. Furthermore, the utilization doesn't end with the common end-user; key infrastructures rely on tight cybersecurity, like transportation, power grids, and healthcare facilities, among others.

How can we fight back against the evolving threats? Machine Learning (ML) makes it possible for various aspects of security to be improved, whether through anomaly or malware detection, predictive analytics, or automation.

Safeguarding Our Processes with Patterns

As it stands, cyberattacks are consistently changing and becoming more sophisticated. Some of these include ransomware attacks, insider threats, advanced persistent threats (APTs), and supply chain attacks. However, all of these leave a trail of data. Machine learning can prove useful as a tool that can analyze a colossal amount of data, identify patterns within that data, develop accurate predictions, and aid in decision-making. Below is a breakdown.

Machine Learning, since it's a subset of Artificial Intelligence, is made up of algorithms that enable systems to learn from datasets to make predictions and draw conclusions. This process requires significant computational power and expertise, which can be achieved through on-site equipment or cloud computing platforms and a team of expert software developers. Additionally, with the capability to be integrated into various established systems through an API, whether you look at healthcare, finance, or agriculture, machine learning can be the behind-the-scenes tool we've been waiting for.

Securing a Digital Future

Aside from the obvious efficiency and security buffs, consistency will also see improvements from the implementation of this technology. False positives can be lessened, alert fatigue can be avoided, evolving threats can be adapted to, and malware can be detected earlier and more efficiently. Security operations, especially now, are more fundamental and connected than we often realize. Cyber threats and hacker groups have become more frequent and sophisticated over the years, and with various fundamental infrastructures depending on technological advances, cybersecurity needs to be tighter.

With notable cyberattacks demonstrating the need for a different approach, machine learning illustrates its usefulness and viability. Thanks to technological advancements and professionals, the ability to monitor, analyze, and determine the right course of action has never been more attainable.


Chetu does not affect the opinion of this article. Any mention of a specific software, company or individual does not constitute an endorsement from either party unless otherwise specified. This blog should not be construed as legal advice.

Founded in 2000, Chetu is a global provider of custom app developer resourcing, solutions and support services. Chetu's specialized technology and industry experts serve startups, SMBs, and Fortune 500 companies with an unparalleled software delivery model suited to the needs of the client. Chetu's one-stop-shop model spans the entire software technology spectrum. Headquartered in Plantation, Florida, Chetu has fourteen locations throughout the U.S. and abroad.

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