Cybersecurity 20

Artificial Intelligence (AI), machine learning and cybersecurity

On 15 October 2020 ThreatPost.com reported that Broadvoice, a VoIP provider serving small- and medium-sized businesses, has leaked more than 350 million customer records related to the company’s “b-hive” cloud-based communications suite. Cyware.com reports on 20 October that Office 365 with more than 250 million monthly active users is ‘A Treasure Trove for Cybercriminals’.

These are only two of several reports of recent cybersecurity vulnerabilities and events which underscore the urgent need for increased vigilance by companies depending on connected technologies.

Although cybersecurity is a crucial area of concern for most IT companies, it is the expertise gained from the latest technologies associated with artificial intelligence (AI) and Machine Learning that will provide a competitive lead in terms of information security and data safety. Cybersecurity is one of the most important beneficiaries of these new technologies.

AI and machine learning contribute to cybersecurity

AI cannot replace human intelligence and the understanding of a problem and finding solutions despite mimicking human intelligence. However, it can reduce errors and faults in the operational tasks and finding anomalies and irregularities, and thus surpasses human capability. AI adds a robust security layer AI by evaluating the mistakes and all the errors that human intelligence is prone to commit.

Machine learning can analyse data from the past and evaluate the use of cases for the future, can address the user needs in the most befitting manner. Thus, machine learning algorithms can predict future occurrences and user behaviour and can suggest proactive measures accordingly.

Timeous measures are crucial to redress security vulnerabilities and all kinds of cybersecurity threats. Rather than allowing hackers sufficient time to launch threatening malware, security systems must act proactively and block security gap. Thus, AI and Machine Learning-based tools allow application developers, security experts to stay ahead of the security threats and challenges.

Machine learning for cybersecurity: Crucial challenges and data sets

The biggest challenge is to detect potential security threats or malware for machine learning technology to play a role in cybersecurity. Hence, timely detection of security threats and serious malware is the key to gain a competitive and proactive lead in providing security safeguards. Some of the challenges and problems involved in realising the promise of utilising machine learning in cybersecurity, include:

Accessibility of datasets

Appropriate and accessible datasets are needed to investigate the cybersecurity issues in IT systems since, in its absence, security risks and threats cannot be evaluated.

Limitations associated with uses and effects

The use of machine learning is very limited for increasing information security and has been restricted to the understanding of user inputs, user behaviour, and user interactions. Researchers working on different ML projects are of the opinion that the cyber community should be more active and engaging to help reap the benefits of cybersecurity measures. However, few global cybersecurity experts have the necessary knowledge and skills to work with AI and machine learning based security algorithms.

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Quality of life

AI and ML play a powerful role in improving cybersecurity and enhancing the quality of human life. They are part of security tools, surveillance camera systems, remote monitoring systems, modern home security systems and the detection of unknown faces, threatening sounds, and irregularities to send instant alert notification measures to the homeowners.

Cybersecurity systems based on machine learning are y useful in detecting security threats and cyber-attacks. A machine learning algorithm can reveal the security risks and threats that are developed by recognising similarities and anomalies between various security threats over a period.

The potential impact of AI and machine learning in cybersecurity

Globally, most companies and organisations are not yet prepared to deal with major threats although AI and machine learning may be the answer to cybersecurity for the future. Businesses need to embrace AI and ML-based tools and security mechanisms and have a real understanding of how machine learning based algorithms work and how they can enhance security. AI and ML are the most promising and era-defining technologies for dealing with cybersecurity threats and issues of all types.

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