Did you know that 90% of all data was generated in the last few years? With an unprecedented amount of data being generated in recent years, organizations are looking for data science solutions in all fields including cybersecurity. The reason being organizations need to secure their and their customers’ data else they may face the wrath of angry users along with class action lawsuits, leading to millions of dollars in losses. And the enormous amount of data stored by medium to large enterprises cannot be secured without data science.
After all, data is growing exponentially with people browsing the Internet, clicking photos on Instagram, and creating videos on TikTok. And data science is used to safeguard data along with drawing information and insights from data. When cybersecurity utilizes data science, cybersecurity gains invaluable power against threats. And this joint field is popularly coined as “Cybersecurity Data Science (CSDS)”, which applies data science to detect, prevent, and mitigate new and evolving cyber threats. “CSDS is the practice of data science to assure the continuity of digital devices, systems, services, software, and agents in pursuit of the stewardship of systemic cybersphere stability, spanning technical, operational, organizational, economic, social, and political contexts. CSDS is increasingly formally recognized as a cybersecurity job specialty, for instance in the NIST NICE Cybersecurity Workforce Framework,” according to Data Science Central.
Cybersecurity Data Science (CSDS) provides a proven scientific approach to detect novel attacks utilizing data analytics and machine learning techniques. One of the main machine learning techniques utilized is anomaly detection. The reason being attacks usually utilize some malicious code or vulnerability in the system, which is usually anomalous or different from the normal or standard code. Using a machine learning model to detect anomaly in code is one of the popular methods to utilize data science in the cybersecurity field. One of the other areas utilizing machine learning is penetration testing. Since data science and machine learning can learn and adapt from past experiences and predict and create new experiences using predictive data analysis, they help execute automated penetration testing with a near human-like detection approach.
In recent years, Cybersecurity Data Science (CSDS) has gained the attention of cybersecurity vendors. After all, cybersecurity solutions must compete with the ever-evolving cybersecurity threats, which is not possible without data science. The reason being the traditional methods of detecting threats — rule-based detection and signature-based detection — do not work against modern threats. Cybercriminals have overcome traditional threat detection solutions by creating new or self-changing malware or viruses. Rule-based detection relies on looking for known effects of a threat and signature-based detection relies on looking for known file signatures for detecting threats. These detection algorithms fail to detect threats if the threat is new or self-evolving. The reason being the effects or signature of a new or updated threat is unknown. And hence, these traditional threat detection algorithms prove useless against modern or new threats.
Moreover, it is not just efficiency but speed is also improved by utilizing data science for cybersecurity. The reason being: nowadays, the digital infrastructure of organizations are heavily complex and the longer a cyberattack or security breach stays undetected, the heavier losses the organization may suffer. That is why speed is a critical factor while detecting and mitigating cyberattacks. And data science helps speed up the process of detecting attacks and vulnerabilities if compared to the traditional methods.
For example, signature-based detection works by matching every file’s signature with the signatures of known malware and viruses, which takes significant time. Lets say, the total number of files is 1 million and the known signatures count is 100 thousand, then the total number of comparisons performed by signature-based detection tools is 100 million. And it is just the tip of the iceberg: there are many more files on a single system and a medium to large enterprise usually has one or more networks of hundreds to tens of thousands of systems. That means, a traditional cybersecurity solution may take some days to weeks to perform a complete scan of an organization’s infrastructure, allowing cybercriminals to steal some if not all data while they stay undetected in the system. That is why cybersecurity vendors are integrating data science and machine learning techniques in their security products.
For instance, Imperva’s Sonar is an unified security analytics platform that brings the best of cybersecurity and data analytics to empower security teams. It helps analyze activities and data from entry points like APIs and websites to its end point — a database, data lake, or some other data store. The use of automation fuelled by data analytics and machine learning helps detect and stop zero-day exploits and security vulnerabilities. Also, Imperva integrates its products for securing applications, data, and edge locations or points in Sonar. Thus, Sonar is an all-in-one solution for detecting and stopping attacks, managing compliance and data governance, and handling sensitive data under a single interface.
