Utilizing Data Science for Cybersecurity and Fraud Detection in Apps
The severe proliferation of mobile applications has led to an immense rise in the number of security breaches happening across the globe. No wonder tightening security has become a huge concern as well as a responsibility for app developers. Also, security issues have been a major concern for the end users as well. Since most people these days tend to conduct several activities such as banking, shopping and social interactions via mobile apps, safeguarding their sensitive data should be the primary concern.
The following post focuses on how data science can be helpful for fraud detection and cybersecurity. But before we delve into that let us understand what is data science.
As the name implies the concept incorporates math and statistics and the use of disruptive technologies such as advanced analysis, artificial intelligence, machine learning and a lot more. All these concepts are used to generate actionable insights that can certainly lead to appropriate decision-making and planning. Also, not to mention it turns out to be one of the most promising and in-demand career paths for skilled professionals.
Data is an immense collection of raw information which can be stored, and processed with the help of a wide range of sources including sensors, social media, and a lot more. Whereas science, on the other hand, is a systematic study of different phenomena using different scientific methods. Everything takes place here from forming different hypotheses to conducting different experiments, analyzing data and drawing out conclusions.
How Data Science Can Help to Minimize Cyberattacks?
Cyberattacks are constantly happening, not to mention that in a quite innovative way. So what you need to do is monitor whether the applications aren’t being misused and keep tabs on how the end user interacts with the application. Fortunately, cybersecurity and fraud detection are the two main areas where data science plays a pretty crucial part and that’s what the entire blog post is all about.
One of the common practices is analyzing similar patterns in financial transactions and user behavior, this can provide a great help in detecting potential fraud. Also, machine learning algorithms are used to classify potential threats. Apart from these, there are numerous ways in which data science can be used such as:
Network Traffic Analysis - One of the best practices is to analyze the traffic data of the network. With the help of data science, it is easily possible to identify network intrusion and malicious activities happening around. For example, any kind of malware malware infection or network intrusion, or insider threats can be determined. So here you are bound to know the source and different types of protocols and ports being used.
Assessing Vulnerability - Data science can also be considered to analyze data from vulnerability scanners, what for? Of course, to identify potential vulnerabilities among network systems and devices. Of course, here you can decide which vulnerability to address first and which one later on so that there is no scope for any long-term damage.
Security Configuration Management - It may quite interest you to know that data science is also used to analyze data, all thanks to security configuration management. And do you know what is the best part, here changes can be applied automatically even for the devices that aren’t compliant.
Security Information and Event Management - Lastly, data science can be used to analyze data from security information and event management systems. What happens next is automagical security alerts are generated whether the threat is from network traffic or system logs any other events.
Tips and Techniques to Consider
Any kind of data which shows false behavior can be instantly detected. This technique is called anomaly discovery.
Machine learning algorithms can be used to a great extent. For example, logistic relapse, decision trees, random woodlands, support vector machines (SVM), and neural networks can be considered. These algorithms can certainly assist in classifying false or non-fraudulent transactions or events.
Have you ever thought of analyzing social networks? From customer accounts to your business accounts, fraud activities can be seamlessly detected using Social Network Analysis.
Incorporating fraud scorecards, the score can be assigned to each exchange depending on the different risks evaluated. Here suspicious exercises can flagged.
Another way is behavior analytics. Keeping regular tabs on the client's behavior can be quite helpful. One unusual behavior can definitely help in detecting fraud.
Apart from data science, you can think of different practices such as secure coding, solid encryption, user authentication, secured data storage, secured network communication, secured offline storage, app permissions, penetrating testing and a lot more. Securing your mobile app is pretty much at paramount so choose any cybersecurity practice you want, the ultimate objective is to implement security measures and that too in the nick of time before it gets too late.
Conclusion
There is no denying the fact that we are residing in a time when personal data is willingly made public, all thanks to an ever-increasing number of websites, platforms, media and networks. As a result, gaining your customers' trust can be pretty challenging. Henceforth, data science featuring smart algorithms, models and innovative approaches is a must!
The objective of incorporating data science in cybersecurity and fraud detection is to safeguard crucial information of valuable clients. Yes, technology is dangerous but if used in the correct manner, it can definitely act as a pure bliss.
I hope you did find the short post worth the read. If yes, do help us in reaching out to more and more readers. Again do not take security as an afterthought. It is something that has to be considered right from the very beginning. Good luck with protecting your treasured possession (mobile app) from any security vulnerability and potential breaches. Have a nice day!