Introduction:
When two power houses join forces, surely the anticipation gets high! And that is the case between Data Science and Cybersecurity.
Cybersecurity data science is fast becoming a necessity in business organizations. It entails the amalgamation of both technologies to analyze, predict and prevent malicious activities from causing injury to institutions.
What is Cybersecurity?
Cybersecurity is the practice of protecting internet based hardware/software, networks and data from malicious activities and unauthorized access using technologies and processes designed to protect sensitive information from digital attacks.
What is Data Science?
Data science is a superset of other disciplines such as Artificial intelligence, Machine Learning and Deep learning, as they are all data driven. It entails using mathematics, statistics, programming and advanced analytics in uncovering data from a particular source.
What then is Cybersecurity Data Science(CSDS)?
Cybersecurity Data Science(CSDS) is an emerging profession which uses analytics from data science to prevent systems and electronic data from cyber-attacks and detect malicious activities. The constant evolution of Information Communication Technology(ICT) over the past decade has given rise to all sort of data and information. Therefore, safe guarding the vast amount of data organizations have has become a top priority.
SOME APPLICATIONS OF CYBERSECURITY DATA SCIENCE.
Data Protection: Data scientists in government agencies often collect, store, exchange or dispose relevant data which may be used in certain situations. With adequate cybersecurity knowledge, they will be able to design hardware/software solutions which can protect the data either when stored in the database or when undergoing an exchange.
Improved Intrusion Detection System:Hackers come up with new technologies and methods to intrude systems and steal or corrupt sensitive data. As such, early intrusion detection is of importance to organizations. With machine learning algorithms, systems can often find loopholes before hackers do and alert individuals or organizations. Also, with data science analytics, historical background of breaches/malicious activities can be recorded thereby predicting future attacks before they happen.
Risk Assessment: Cybersecurity teams can explore external and internal data records in organizations and come up with precise analytics on how secure their systems are, detect vulnerabilities and develop strategies on how to better protect the organizations' data.
Automated Detection: We can use plagiarism checkers as an example here. Systems can easily detect malicious activities without human intervention and then alert security experts of a certain reoccurring data breach.
IMPORTANCE OF CYBERSECURITY DATA SCIENCE.
- Data breaches
It helps curb prevailing data breaches using machine learning/deep learning algorithms which can easily predict the type of virus/malicious software being used and its target.
- Data Protection
After training data science algorithms repeatedly in the art of data protection, an automated system can be created which will benefit both individuals and organizations.
- Cyber-attack Evaluation
It evaluates types of cyber-attacks which in turn detects and classifies attacks, develop incident response strategies for systems and computer networks to reduce further damage and provide precise analysis as to what caused the data leakage/breach.
CONCLUSION
In other to be properly secure in this modern data age, small and large scales business, individuals not excluded, right measures should be in place to guarantee a client's data/information.
Although cyber-attacks has become more sophisticated, the combination of the technologies which Cybersecurity and Data Science provide would be a force for hackers to reckon with.
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