What Machine Learning Contributes to Cybersecurity Today

In recent years, Artificial Intelligence (AI) has made remarkable progress, with its integration into the daily lives of most people, largely as a result of advances in Machine Learning (ML) technology. ML has become increasingly popular amongst businesses, providing them with a means to better analyse and utilise data for a competitive edge.

Machine Learning allows computers to identify patterns and complete tasks autonomously. This means that, once an algorithm has been trained, it is able to make predictions independently using mathematical modelling and statistics.

Supervised and unsupervised learning are two approaches employed in machine learning. With supervised learning, data is labelled by the user prior to being inputted into the algorithm, while in unsupervised learning, the system deduces its own outcomes without any user input.

Machine learning is becoming increasingly prevalent in the business world, both in the development of new processes and in the improvement of existing ones. Of particular note is its usage in the field of cybersecurity, with custom projects such as Darktrace Antigua and Blue Hexagon being employed by organisations to bolster their defences. This application of machine learning to cybersecurity is also referred to as ‘cybersecurity data science’.

According to Forbes, it is estimated that monthly ransomware sales will reach a total of $102.3 million in 2023. Furthermore, statistics show that a new business is targeted by cyberattacks every 14 seconds. This has led to an increasing number of businesses investing resources into Machine Learning (ML) for enhanced cybersecurity.

By leveraging Machine Learning algorithms, businesses can anticipate and prepare for systemic issues more effectively. Additionally, they can be used to execute routine security tasks reliably.

Therefore, how can ML be used to cyber defense?

Common applications of machine learning include:

Data manipulation and analysis

Cybersecurity systems produce a large amount of data which can be beneficial to a company. However, analysing such vast quantities of information regularly can be a demanding and time-consuming task.

Machine learning algorithms enable organisations to quickly and efficiently analyse large amounts of data and extract useful insights. This technology can be used to identify risks, monitor usage patterns, and gain a better understanding of customer behaviour.

Mobile safety

As remote working has become increasingly commonplace, mobile security has become a paramount issue. Home networks are often inadequately protected, leaving them open to attack. What’s more, many businesses overlook mobile security when developing their cybersecurity strategies, leading to an increase in malicious cyber activity via mobile devices.

Businesses are increasingly using Machine Learning (ML) to filter spam emails as an effective way of dealing with the issue. ML algorithms are also able to identify phishing or malicious emails that have bypassed spam filters, alerting the sender accordingly. Furthermore, these algorithms are able to analyse and interpret text messages and other electronic communications. If they detect malicious content, they will mark the sender’s details as spam, thus preventing any future contact with them.

Electronic helper

It is essential to implement security measures to prevent unauthorised access to devices due to the increasing use of virtual assistants with access to data storage. Machine Learning (ML) systems can be taught to recognise typical user behaviours and alert if any unusual activities are detected when users interact with voice assistants.

It is essential to safeguard speech-controlled virtual assistants such as Siri and Alexa from attacks that utilise voice recognition software. Nowadays, AI assistants are equipped with sophisticated speech detection modules, enabling them to learn the user’s voice and respond accordingly. Furthermore, they possess systems intended to detect unusual activity.

Scanners and Virus Detection

Despite having antivirus software, systems remain at risk of cyber-attacks as new viruses and malware are created every day. To counter this, many organisations have turned to Machine Learning (ML) algorithms to improve their anti-malware products. Computers utilising ML algorithms can be used to detect security vulnerabilities, as well as for performing penetration testing. ML algorithms can accurately identify potential risks and allow for remote installation of security patches and updated blueprints.

Machine Learning-based algorithms can be used to monitor systems and network behaviour for potential issues. Businesses can utilise these algorithms to identify potential malicious websites and verify online financial transactions. This is a more cost-effective approach than employing human operators.

Barriers to using machine learning in cyber security

There are numerous advantages to employing Machine Learning (ML) algorithms for cyber security, however, there are also several challenges which can impede businesses from adopting them. Constructing a threat profile necessitates a considerable amount of data and information, including data transmissions, data endpoints, application logs, and information regarding the cloud environments and containers employed by the system. Unfortunately, most businesses lack the necessary data to begin.

Despite the company’s efforts, there may be other factors to consider. Is there a possibility that the figures will not change? How often are the logs updated? How many data sources are there, if they were to be counted? Has there been any attempt to combine data from various sources into a single set? Data processing is necessary prior to the use of any machine learning or decision-making system. However, many organisations lack a structured approach to the gathering, cleansing, and analysis of their data.

It is also essential to assess the viability of the final dataset. Is there a sufficient amount of data to be analysed once machine learning algorithms have been implemented? Often, there is not enough information to base any decisions on.

From the viewpoint of a company owner, what information would be helpful to me?

The initial step is to determine the extent to which Artificial Intelligence (AI) and Machine Learning (ML) will be included in the system. How automated is the system?

It is essential to keep track of data when carrying out any machine learning activity. Dashboards and data visualisation tools can be used for this purpose and to monitor the user’s data points.

Finally, computers are not able to manage all tasks. Human input is still essential, regardless of the sophistication of algorithms. The most effective cybersecurity strategies require collaboration between human and machine learning experts. Data-driven decisions cannot be made without the use of both ML algorithms and human analysts.

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