Data breaches cost companies money, a lot of money. In 2018, nearly two million cyber-attacks cost $45 billion worldwide (source) Not to mention, the attacks ruin a company’s reputation and lead customers to lose their trust. In 2019, there were 2,013 confirmed cases of data breaches alone that exposed billions of customer data.
Data science has emerged as an impressive tool to fight against cybercrimes. Amid the soaring benefactors of the data science industry, fighting cyber-attacks is fairly new.Using data science, helps security teams distinguish between potentially malicious network traffic and safe traffic, allowing companies to keep their data safe.
What did the cybersecurity look like before data science?
Cybersecurity has always been on top of the list of priorities for enterprises and companies that hold sensitive customer data. Though organizations have safety guidelines and practices in place for internal practices that play a key role in safeguarding data and prevent data leaks, external technical risks remain a challenge for companies.
So far they have relied on FUD approach –Fear, Uncertainty, and Doubt to tackle cyberattacks. These approaches are based on assumptions about where and how an attack can occur. Data science changes all of this and allows security experts a better way to tackle these challenges based on facts, instead of assumptions.
The connection between data science and cybersecurity
Cybersecurity aims to prevent intrusions, attacks, and identify threats like malware and prevent fraud. Data science does this by using machine or deep learning algorithms. Security teams collect data from several sources and identify threats. This analysis aims to reduce false-positive and identify intrusions and attacks.
One of the common techniques is User and Entity Behavior Analytics (UEBA). It identifies anomalies in user behavior that show signs of an attacker. There is a correlation between abnormal user behavior and security attack. Techniques like these can alarm security experts who can then take preventive measures and prevent attacks or stop the intrusion.
A similar process is used in finance to prevent fraud. Experts use statistical data analysis to detect abnormalities in credit card purchases. Any anomaly found in the purchase behavior resounds fraud, which can tell experts that a user might be using somebody’s else card.
The current practices changing cybersecurity
Intrusion, Detection, and Prediction
Hackers and scammers are always a step ahead of security professionals. They are constantly trying to come up with new intrusion methods and tools to game a system, while security professionals use detection systems based on previous attacks to detect attacks.
Data science techniques overcome this problem by using historical and current information to predict attacks. Further, machine learning algorithms have the potential to detect vulnerability in the information security environment and help organizations strengthen their security strategy.
Commonly used security systems – anti-viruses and firewalls — match signatures from previous attacks to detect intrusions. Hackers and perpetrators can evade this by using new technologies and easily breach security systems.
Behavior analytics techniques like User and Entity Behaviour Analytics (UEBA) use machine learning algorithms to understand attackers’ behavior and detect potential cyberattacks.
Build robust DevOps Cycles
DevOps is a critical part of enterprise operations. It is also an extremely vulnerable part of enterprises and an easy entry spot for attackers. A constant feedback loop is maintained between data scientists and DevOps as machine learning models are put to production with the help of DevOps. Data science professionals frequently use DevOps’ support to build automated workflows for their projects.
Many data scientists have even transitioned to looking after the DevOpsthemselves. Since data scientists are familiar with DevOps workings,DevsecOps can work with data scientists to secure the infrastructure.
Data protection and Associate Rule Learning
Associate Rule Learning (ARL) is a popular machine learning algorithm in data science that discovers relationships among objects in large databases. This method is commonly used in market research and analysis. ARL can detect the relationship among items that people generally buy.
In Cybersecurity, ARL can recommend data protection measures by understanding the relationship among various factors responsible for attacks. ARL learns the characteristics of existing data and notifies experts as soon as it detects any unusual activity. ARL keeps improving itself to detect even the minutest deviations in the data.
Data recovery and back up
Back-ups are a crucial part of security protocols. Back-up technologies use machine learning to automate their backup and data recovery tasks. These backups and recovery systems are based on machine learning algorithms that are trained to follow the priorities and needs of the security plans.
These back-up and recovery systems help incident response teams to organizing workspaces and resources in times of security crisis. An ML tool, for instance, can recommend necessary equipment and locations for a particular business recovery plan based on the company’s needs.
Technology is advancing at a much faster pace than ever before. No one knows what techniques and technologies hackers and cybercriminals will come up with. Data science techniques like UEBA can predict threats based on historical data. Intrusion Detection Systems (IDS) can predict malicious attacks. Amid raging cyber-attacks, data science can protect against companies against data and financial losses.