Can Data Science Prevent The Next Big Hack?

The COVID pandemic has massively escalated the surge of cyberattacks and data breaches despite having robust security controls, software, and solutions abundantly available in the market. The popular Maze ransomware sprung back to life during the pandemic and multiple Fortune 500 companies such as Cognizant, LG Electronics, Xerox amongst others have already fallen prey to this attack in 2020. A lot of this could be attributed to the vulnerability businesses offer the cybercriminals to take advantage of the situation quickly. With the advancement in technology, cyber-crimes are also increasing and getting complex. Cyber-criminals are launching sophisticated attacks that are putting modern security systems at risk. There are over 23 billion IoT devices connected to the internet today, which have created larger cracks for cyber attackers to exploit. So, the cybersecurity industry is also evolving to meet the increasing security demands of companies. But, these defensive strategies of security professionals may also fail at some point.

While experts in the industry have spoken about the need of proactive and predictive cybersecurity, businesses continue to largely depend on traditional security approaches and vulnerability assessments to measure their security posture. But with the pandemic forcing most of us to operate almost exclusively on our screens, our identities are now locked in databases making the canvas of a hacker also expand.

Data science most importantly might be the next big answer to the world’s cybersecurity woes. In fact, multiple cybersecurity companies are now investing heavily into machine learning, artificial intelligence, data science and other related fields to edge ahead of the cybercriminals. To up their game and enhance their vulnerability detection mechanisms, companies are investing in these technologies which is aiding them to safeguard their defense mechanisms. It is also assisting them in analyzing cyber crimes better.

The Growing Significance of Data Science in the Realm of Cybersecurity

The central theme of cybersecurity is data security. Most of the cyberattacks are geared towards compromising organizations’ stored data to use it for fraudulent activities. As such, regular updates and analysis of existing data are crucial in boosting organizations cybersecurity environment.

Most organizations have big data that are difficult to handle without the input of data scientists. The analysis of big data in any institution is a proven way of identifying financial risks and averting any cyber-attack plan. It offers a platform for the data scientists to make recommendations that, if adequately implemented, help in detecting the threats. Data science is primarily about bringing a logical structure to an unstructured data. With this approach, it is easier to compare normal versus abnormal patterns via machine or deep learning algorithms. From cybersecurity point of view, data science harnesses a predictive power to automatically distinguish between safe network traffic and potentially malicious traffic that can be an indicator of an active cyber attack or malware infection.

For example, with the help of data science, you can identify patterns on your e-commerce website and when it is hit with Distributed Denial of Service (Ddos) the most and take necessary steps to prevent it. Similarly, you might see that most of the network based cyberattacks in your organization happen at a certain period of time in the day.

Although majority of websites can be protected against cybercriminal activity with the implementation of secure coding principles, a reliable web hosting like Hostpresto, updating secure server software, and encrypting sensitive information, but applications of data science are relatively a new paradigm.

How Data Scientists Help In Enhancing Cybersecurity

Wondering why you should involve data scientists when making security-related decisions? Well, it’s crucial to realize that the security environment has evolved significantly over the years. The rise of technology has complicated the processes of securing your systems. The situation is further compounded by the fact that organizations are increasingly receiving, processing, and storing highly sensitive information from their customers. This has increased the urge by cybercriminals to acquire the data for criminal activities. Cyber-attacks may initially appear quite minor, but machine learning can find patterns with minor outliers that could lead to larger threats. There is a constant battle between cyber-criminals and cybersecurity teams. By 2023, it is estimated that cyber-criminals will steal an estimated 33 billion records.

Data scientists are challenged with staying ahead of threats, balancing predictive and reactive methods. In simpler terms, modern data science involves studying, processing, and extracting valuable insights from a set of information making data scientists a key figure in the puzzle of predictive cybersecurity

A recent report from Indeed highlights that there has been a 29% increase in demand for data scientists year on year and a 344% increase since 2013.Cybersecurity is one of the prime drivers for this sharp increase in demand.

Data science and cybersecurity have to function hand in hand

The adoption and implementation of data science help organizations to measure the effectiveness of their information security in better ways. Data scientists use machine learning to identify potential cybersecurity threats, working to halt them. Machine learning automation makes identifying any outliers in data much easier. This approach allows them to detect irregular trends in data reception, sharing, and storage. This helps them to identify loopholes that criminals can use to compromise the data. Their work is vital in maintaining cybersecurity, protecting businesses and the wider community from having their information stolen.

Statistical methodology is a part of data science that uses mathematical models and techniques for statistical analysis of raw data. In general, it extracts information from research data and provides different ways to assess the robustness of research outputs. From the perspective of cybersecurity, statistical methodology can provide an exciting growth area in the design of cyber defense mechanisms. This can be achieved through anomaly detection of unusual behavior against understood statistical models of normality.

Traditional antiviruses and firewalls match signatures from previous attacks to detect intrusions. Attackers can easily evade legacy technologies by using new types of attacks. Behavior analytics tools like User and Entity Behavior Analytics (UEBA) use machine learning to detect anomalies and potential cyberattacks. If, for example, a hacker stole your password and username, they may be able to log into your system. However, it would be much harder to mimic your behavior

Cyber security is really a cat-and-mouse game. Hackers and attackers use a range of tools and intrusion styles to gain access. Regression (prediction) models are a great tool that use an Intrusion Detection System (IDS) to monitor computers for such potential malicious attacks. These systems monitor users and devices on your network and flags dangerous activity. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future. On the other hand, cybercriminals are constantly trying to create innovative models and algorithms for cyber attacks of bigger scale. So to break this stalemate, the frontiers of predictive analytics can provide important insights to data scientists. This way, they can predict the type of attacks likely to occur in your organization, which helps in developing security measures to curb them before they occur.

Associate Rule Learning (ARL) is another example of where machine learning can prevent cyber-attacks. This works as a recommendation system, similar to how Netflix or Spotify suggests new media for consumers based on their past preferences. ARL generates a response for a particular risk based on its characteristics. Past threats with the same characteristics will help ARL understand what may or may not be a threat, constantly updating its database with new types of cyber-attacks.

New backup technologies are leveraging machine learning to automate repetitive backup and recovery tasks. Machine learning algorithms are trained to follow the priorities and requirements of security plans. Backup and recovery systems based on ML can help incident response teams organize workspaces and resources. For example, ML tools can access and recommend the necessary equipment and locations for a particular business recovery plan based on the company’s needs.

These analytics models are extremely dynamic and are highly valuable to enterprises. As a result of this, cybersecurity professionals will need to determine standards and methods for protecting these models and ensuring their integrity. To do so, they will need to protect these assets from the outside in and the inside out.

Final Thoughts

According to report Cost of Data Breach report that was released before the pandemic, businesses across the world stood to annually lose a whopping $3.92 million on an average. With the pandemic increasing the dependency on digital by multiple-folds, this number has only increased.

There is no denying fact that hacking is an evolving menace, nobody knows what form it will take in the future. But the promises data science has shown in the field of cybersecurity are phenomenal. The further advancements of data science will provide a huge opportunity to businesses and organizations to address their cybersecurity concerns.



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