Today’s businesses gather large amounts of data. Data serve as the heart of business-critical systems you can ever think of. It includes infrastructure systems as well. The high-tech infrastructure of this modern world, which includes cybersecurity and network systems, gathers huge amounts of analytics and data on many key areas of mission-critical systems.
Even though human beings still offer the primary intelligent insights and operational oversight into today’s infrastructure, machine learning now gains huge momentum in many areas of the systems used today, whether positioned in the cloud or on-premise.
What is Machine Learning?
This term refers to an application of AI or artificial intelligence that gives systems the ability to automatically improve and learning from experience without being openly programmed. The focus of machine learning is on the development of computer programs with the ability to access and use data to learn for themselves.
The learning process starts with data or observations, like direct experience, instruction, or examples, to search for data patterns and come up with better decisions in the near future depending on the said examples. The main goal is to let the computers learn automatically with no assistance or intervention from humans and adjust the actions accordingly.
Why Machine Learning is Used in Cybersecurity
The predictive analytics of machine learning (ML) offers a powerful case for cybersecurity and network applications. Today’s organizations are swamped with myriads of traffic flows and network connections and cybersecurity events that need analysis and possibly remediation.
The large volume of events and traffic and the complexity of the hybrid cloud networks of today makes it unrealistic to have human beings trying to analyze all cybersecurity data being gathered and coming up with decisions depending on the data.
Machine learning in the world cybersecurity allows cybersecurity systems to do amazing things. Today, for the most part, has the ability to accurately pick up on and determine anomalies in connections, traffic patterns, user activity, and other network aspects.
Powerful algorithms of ML can filter through the traffic patterns as well as learn what normal network activity fingerprint activity looks like and make decisions according to the ML algorithms.
For several years, traditional firewalls were able to create a baseline of what usual activities are across the network and use some traffic pattern rules to decide if certain network flow or traffic pattern fits this heuristic analysis or rule or not. The powerful platforms of cybersecurity have moved far beyond what the usual on-premise firewall devices accomplished.
Machine Learning and Its Benefits
It offers numerous benefits for various applications:
1. Continuous improvements
Algorithms in machine learning serves as agents that can constantly enhance the performance of a certain machine learning system with the use of historical data. There are a lot of practical applications of continuous improvement with the help of it.
Try to think of a ML system that helps predict consumption patterns for a certain market, for instance. This system will not just reference the historical data for making predictions for a certain period but will also continue to reference newly gathered data to improve its way of analyzing consumption patterns.
2. Supplementing data mining
The process of examining database or some databases to analyze or process data as well as generate information is called data mining. Take note of today’s digital information’s pervasiveness has lead to big data generation at faster rate, which makes manual data interpretation and analysis impossible.
3. Task automation
More practical benefit of utilizing machine learning involves development of software programs, autonomous computers, and processes, which can result to task automation. Through continuous improvement and supplementing data mining, machine learning system has been deployed and developed to perform some tasks automatically.
Automation may complement human activities. Experts mentioned that automating 1 or 2 steps within the process through machine learning might mean freeing up the humans to focus on more critical aspects.
3.1. Google automation example
There are some notable task examples being automated through machine learning. Google used this technology to rank and index websites in search engines. Both Facebook and Google also use some proprietary algorithms to deliver different online advertisements. Intelligent personal assistant including Apple’s Siri and Google’s Google Now use machine learning for answering questions, perform actions, and make recommendations.
Machine Learning and Its Limitations
The advantages of machine learning basically translate to the innovative applications, which may improve the way tasks and processes are accomplished. But, in spite of its countless benefits, there are some challenges and risks. Below are some of the limitations of it:
1. Time Constraints
It’s impossible to make an immediate accurate prediction with machine learning system. Take note that it learns through the historical data. If the data is bigger, the longer it would be exposed to such data and the better it’ll perform.
For instance, using the system to play the games and beat the human opponents would need feeding the system using historical data and exposing it to the newly acquired data continuously to make better decisions or predictions.
2. Error Correction and Diagnosis
A known machine learning limitation is its susceptibility to the errors. The actual problem with this fact is that once they make errors, correcting, and diagnosing them can be hard since it’ll need going through the algorithm’s underlying complexities as well as the associated processes.
3. Limitations of the Predictions
Experts reminded that when compared to humans, computers aren’t good storytellers. Machine learning system can’t always give rational reasons for a certain decision or prediction. Moreover, they’re limited to answering questions instead of posing them.
Aside from that, such systems don’t understand the context. Depending on the date provided and used for training, machine learning can be prone to unintentional and hidden biases.
4. Verification Problems
Another machine learning limitation is lack of variability. Experts stated that machine learning deals with the statistical truths instead of literal truths. In cases that aren’t included in historical data, it’ll be hard to prove with certainty that predictions made by machine learning systems are ideal in every scenario.
So here you have it. Are you pro or con ML? We definitely think that this will be the rising trend in next decade. What to do next? Feel free to look around our website! If you enjoyed reading our article, please comment and share it. 🙂