The rapid advancement of Artificial Intelligence (AI) in recent times has led to its widespread integration into everyday life, primarily due to the progress in Machine Learning (ML) technology. By leveraging ML, businesses can effectively analyse and leverage data, further strengthening their edge in a competitive landscape.
Computers can recognise patterns and execute tasks independently with the help of Machine Learning. In other words, after training the algorithm, it can utilise mathematical modelling and statistics to make autonomous predictions.
There are two primary techniques used in machine learning, namely supervised and unsupervised learning. In supervised learning, the user labels the data before feeding it into the algorithm, whereas in unsupervised learning, the system draws its own conclusions without any input from the user.
In the business realm, Machine Learning is gaining ground daily, being employed extensively to develop new processes and enhance existing ones. Cybersecurity is a prime example of its use, with organisations implementing custom projects such as Darktrace Antigua and Blue Hexagon to fortify their defences. The application of Machine Learning in cybersecurity is often referred to as ‘cybersecurity data science’.
Forbes speculates that the aggregate monthly sales revenue generated from ransomware will reach $102.3 million in 2023. Additionally, statistics indicate that cyberattacks target a new business roughly every 14 seconds. Consequently, more businesses are investing resources in Machine Learning (ML) to improve their cybersecurity measures.
The use of Machine Learning algorithms helps businesses to anticipate and prepare for systemic issues with greater efficiency whilst also enabling the accurate and reliable execution of routine security tasks.
Thus, how can Machine Learning be utilised in enhancing cybersecurity?
Data manipulation and analysis capabilities
Cybersecurity systems often generate large amounts of valuable data which can aid companies. However, analysing such immense quantities of information on a regular basis can be an arduous task that requires significant time and effort.
The use of Machine Learning algorithms enables organisations to swiftly and effectively analyse vast amounts of data and extract valuable insights. This technology can be employed to identify potential risks, supervise usage patterns, and develop a better comprehension of customer behaviors.
Enhanced safety for mobile devices
With the rise in remote working, ensuring mobile security has become a crucial concern. Home networks often have inadequate security measures, making them vulnerable to attacks. Furthermore, many businesses overlook incorporating mobile security into their cybersecurity strategies, which has resulted in a rise in malicious cyber activity through mobile devices.
To combat spam emails, businesses are increasingly relying on Machine Learning (ML) technologies that can filter such emails effectively. These ML algorithms can detect phishing or malicious emails that have made it past the spam filters, notifying the intended recipient. Additionally, the algorithms are designed to scrutinise and comprehend text messages and other digital communications. Upon identifying harmful content, the algorithms will blacklist the sender’s information, thus blocking any future correspondence from them.
As virtual assistants with access to data storage are becoming more prevalent, it is imperative to implement appropriate security measures to avoid unauthorised access to devices. Machine Learning (ML) systems can be trained to recognise typical user behaviours, detecting and notifying of anomalous activities that occur when interacting with voice assistants.
To prevent attacks that exploit voice recognition software, it is crucial to protect speech-controlled virtual assistants like Siri and Alexa. Modern AI assistants have advanced speech detection modules that allow them to recognise and respond according to the user’s voice. Additionally, they contain systems designed to identify any abnormal activity.
Security Scanners and Virus Detection Capabilities
Although antivirus software is commonly utilised, systems continue to be vulnerable to cyber-attacks due to the constant creation of new viruses and malware. To address this issue, numerous organisations have adopted Machine Learning (ML) algorithms to enhance their anti-malware products. Computers integrated with ML algorithms can recognise security vulnerabilities, perform penetration testing, and accurately detect potential risks. Additionally, these algorithms can permit remote installation of security updates and updated models.
Machine Learning-based algorithms can be employed to observe systems and network behavior, detecting possible issues. Businesses can leverage these algorithms to spot possible malicious websites and authenticate online financial transactions, which is a more economical solution in comparison to human operators.
Hurdles in implementing machine learning for cyber security
Although there are several benefits to using Machine Learning (ML) algorithms for cyber security, there are also numerous challenges that may hinder businesses from adopting them. The creation of a threat profile requires extensive data and information, including details on data transmissions, data endpoints, application logs, and the regarding cloud environments and containers of the system. Unfortunately, most businesses lack the necessary data to start implementing ML algorithms.
Despite a company’s endeavours, there may be other variables to consider. Is there a chance that the statistics will remain unchanged? How frequent are the updates on the logs? Assuming they were counted, how many sources of data are there? Has there been any effort to merge information from various sources into a unified set? Before any machine learning or decision-making system can be applied, data processing is required. However, many organisations lack a systematic approach to gather, cleanse, and analyse their data.
In addition, evaluating the feasibility of the final dataset is crucial. After machine learning algorithms are implemented, is there a satisfactory amount of data to analyse? In numerous instances, the lack of information may preclude informed decision-making.
What Information Would be Beneficial for a Company Owner?
The initial phase consists of establishing the level of integration of Artificial Intelligence (AI) and Machine Learning (ML) in the system. To what extent is the system automated?
Maintaining the record of data is crucial when performing any machine learning activity. Dashboards and data visualisation tools can be utilised for this objective, as well as for monitoring the user’s data points.
Ultimately, computers are unable to perform all tasks. Despite the complexity of algorithms, human input remains critical. The most efficient cybersecurity approaches entail a partnership between machine learning experts and human experts. Informed decisions based on data require both ML algorithms and human analysts.