This is the time to deploy predictive models to anticipate cyberattacks before they could take place.

Owing to the increasing use of technology in the everyday mechanism, cyberattacks are becoming common. Despite the fact that businesses encrypt their data with the market’s best facility and sensitize employees to work on a cybersecurity-driven culture, the digital world is still experiencing cyber attacks. Sometimes, it feels like the cybercriminals grow along with technological improvements. Whenever a new technology is brought into the radar to make data safer, cybercriminals find a way to break into the new system. The only way out of all these chaos is to have a predictive model that can notify us with a red flag before security breaches take place.

Technology has always made its way into movies, mostly sci-fi movies. Most of the movies’ contents are soon evolved as reality. It is unsure whether movie directors have the power to foresee what is coming, but what they anticipate is reflecting on society later. One such movie is ‘Minority Report’ that pictured a pre-crime unit, a specialised police department that apprehends criminals based on foreknowledge that they have acquired from a physical source called precogs. Similarly, companies will anticipate cyberattacks before they could take place with digital techniques that are equal to the fictional precogs. Anticipating cyber attacks allows organisations to proactively defend their business rather than perform expensive and reactive incident responses.

Cybersecurity breaches take more than just money. We are familiar with cybercriminals demanding ransoms for information they take away from a company. But besides that, cyberattacks collapse the company’s reputation and trust that customers have in them. The side effects of the Covid-19 global pandemic and cybersecurity statistics reveal a huge increase in hacked and breached data from sources that are increasingly common in the workplace, like mobile and IoT devices. A recent security report also suggests that most companies have unprotected data and poor cybersecurity practices in place, making them more vulnerable to data loss. Even when the company is prepared for anything that comes their way, there is no 100% encryption against cyber attacks. According to Gartner, the worldwide information security market is forecast to reach US$170.4 billion in 2022. This involves developing predictive solutions that can signal cyberattacks before it could happen.

Developing a predictive model

Predictive models can be developed to predict different outcomes using statistical techniques. A process performance model adopts the concepts of probability to explore further building situations. Here are some of the technologies used to analyse and anticipate cyberattacks before it could take place.

Predictive analytics and machine learning: Predictive analytics uses machine learning to design models that anticipate cyberattacks. Machine learning techniques are grouped into supervised, unsupervised and hybrid techniques. They help the models to detect the upcoming threat. However, a downside is that machine learning models can themselves be attacked.

Intrusion Detection System (IDS): An Intrusion Detection System (IDS) is a device or software application that monitors a network for malicious activity or policy violations. Any malicious activity or violation is typically reported or collected centrally using security information and event management systems. IDS can perform anomaly detection beforehand. 

Vulnerability prediction: Vulnerabilities are weaknesses, flaws or deficiencies that can be exploited by threats to cause harm to an asset. The functionalities of vulnerability prediction are similar in the digital base. It focuses on software vulnerabilities and predicts any other kind of vulnerabilities that exists in a system.