The first few months of 2021 have seen a high number of cybersecurity devastations. The most concerning cyber-attack happened in January 2021, when the vicious “Microsoft Exchange Mass Cyber Attack” hit over nine government agencies and affected around 60,000 private companies in the US alone. Other than that, there were further attacks, like Channel nine, Harris Federation, CNA Financial, Florida Water System, Computer maker Acer, and many more. In 2018 alone, there were 10.5 billion malware attacks, whereas, in 2019 and 2020, there were 9.9 and 5.6 billion malware attacks, respectively. This massive volume of cyber-attacks can be too much for humans to handle, and that’s where applications of machine learning in cybersecurity come to the fore.
Machine learning as a cybersecurity solution
A subset of artificial intelligence, machine learning uses algorithms from previous datasets and statistical analysis to make an assumption about computer behavior and even perform functions for which it hasn’t been explicitly programmed. Machine learning algorithms depend on the way data is collected, organized, and structured. The data being collected contains not just the threats but also the information about everything that happened. It has to be rich enough to provide details about machines, applications, protocols, and network sensors. A part of the work includes stitching together all the data to get one representation with the complete picture. Then, we can build different models and aspects of the behavior and use algorithms to decide when to issue alerts and take actions in response to potential threats.
With machine learning, cybersecurity system can analyze patterns and learn from them to help prevent similar attacks and respond to changing behaviors. It can help cybersecurity teams to be more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resource more strategically.
A majority of machine learning techniques can improve cybersecurity systems. They are:
Classification: Classification can be used to divide programs into different categories, such as malware, spyware, and ransomware.
Clustering: Clustering helps in malware protection, such as email gateway and spam filter applications to separate harmless attachments.
Regression: Regression uses knowledge about existing data that is being utilized to get an idea about the new data. In cybersecurity, it is used for fraud detection. The features, such as the total amount of suspicious transactions, location, and more, help determine the probability of fraudulent actions.
Dimensionality reduction: This feature is frequently used for face detection.
Use cases of machine learning as a cybersecurity solution in different sectors
There are many use cases of machine learning for cybersecurity in different sectors. A few of them are listed below.
Machine learning facilitates predictive maintenance or condition monitoring in manufacturing. It can also be used for warranty reserve estimation and demand forecasting, along with telematics and process optimization.
Machine learning can help in predictive inventory planning in retail that helps alleviate future risks that can potentially disrupt inventory availability. It can also help in the recommendation of engines, upsell, cross-channel market segmentation, targeting, customer ROI, and lifetime value.
Healthcare and life science
Real-time patient data is vital to treat any disease, and machine learning can help to collect such real-time patient data. ML is also helpful for patient triage optimization, proactive health management, disease identification, and risk stratification.
Travel and hospitality
Machine learning algorithms can easily solve the aircraft scheduling problem. ML can also be used for the price tag of a product or service to reflect changing market conditions, in particular, the charging of a higher price at the time of greater demand. ML algorithms used in the travel industry can also solve social media-consumer feedback, interaction analysis, customer complaint resolution, traffic patterns, and congestion management.
Machine learning can be used for risk analytics and regulations in financial services. Additionally, it can help with customer segmentation, cross-selling, up-selling, sales, marketing campaign management, and creditworthiness evaluation.
Energy feedstock and utilities
Some of the use cases of ML in the energy feedstock and utility sector include power usage analytics, seismic data processing, carbon emissions, trading, customer-specific pricing, smart grid management, and energy demand and supply.
When it comes to cybersecurity, however, the potential for machine learning to have a dramatic and lasting impact is real. Machine Learning cannot solve every cybersecurity issue, but it can be used as a toolkit for solving real-world cybersecurity problems more effectively. There are many other areas where one can use machine learning. Machine learning cannot be used as a silver bullet if someone wants to protect their system completely. There are many issues with ML algorithms, but humans also can’t interpret their own decisions. On the positive side of the story, with the growing amount of data and malware attacks, ML seems to be the best remedy to handle cybersecurity in the most efficient way.