As early as 1994, cyberspace was being threatened by hackers such as Kevin David Mitnick who turned from being the world’s number 1 hacker into a renowned computer consultant. By 2018, the list of FBI’s most wanted hackers had grown to 41, which shows how important it is to act quickly and seal every loophole that grants them a leeway to hack our cyberspaces.
Since it is estimated that cybercriminals will steal 33 billion records by 2023, data scientists have risen to the occasion to help keep the attacks as low as possible. Let’s take a look at how data science is enhancing cybersecurity.
Prevent Credential Theft
As long as someone has your login details, getting into your system and wreaking havoc is not complicated; hence employees are advised never to write down passwords or use the same password on more than one account. However, cybercriminals are conniving and use credential stuffing, hoping to strike it rich.
Credential stuffing has become the new cybersecurity threat that has significantly increased in 2020, despite its low success rate. With credential stuffing, cybercriminals can use basic web automation tools and hope to find at least one login match detail to access a real account. It is challenging to detect since even your IT team cannot distinguish between the authentic account holder logging in and the hacker.
Machine learning steps in to help your cybersecurity tram by detecting the slightest anomalies. Unlike your team that cannot keep up with real-time login details, AI monitors every website visit, flags suspicious IP addresses, keep an eye on employee account activity, and detects if any has been used in credential stuffing.
AI can tell if it is a bot signing in or the actual account holders through the digital signature. Machine learning detects anomalies in patterns such that if the site visit is not in line with a user’s behavior, it flags it down so you can investigate.
Detecting and Classification Malware
In 2020, there was a significant increase in malware attacks and exploitation events because cybercriminals leveraged newly disclosed vulnerabilities. Being proactive is among the best ways to reduce a business’s chances of being infected with malware, and machine learning has played a critical role. No longer can companies rely on anti-spam filtering and other protection security programs.
Techniques such as remote analysis and local analysis are used in detecting malware. Deep learning is now utilized as a more advanced machine learning approach in detecting malware because it is more accurate. After collecting malware samples, automated malware analysis is used to examine the malware.
Malware analysis is grouped into three: static, dynamic, and memory, depending on if the malicious codes will be executed. Classifying the malware is vital to know the threat level they pose on your system and how best to protect yourself against them.
Enables Constant Database Updates
Just like Facebook will recommend you follow certain pages based on what you have been viewing, AI will recommend you update your database based on the threats it has noticed in the past. Associate Rule Learning (ARL) generates responses for particular risks based on the characteristics. Therefore should it detect threats with characteristics as those in the past, it will update the database with the new types of cyber-attacks, enabling you to stay in charge of your cyberspace.
Fraud Prevention
The fintech industry is growing, and digital online payments have evolved to provide many platforms through which customers can transact without hard cash. Unfortunately, that has opened a window of opportunity for cybercriminals to commit fraud since all they need is to hack personal financial details to steal money.
Businesses have long relied on a rule-based approach to detect and prevent fraud, but it has the main drawback of being manual. Therefore, it does not identify implicit correlations to flag potential threats and vulnerabilities in the system.
Machine Learning (ML) is much more accurate by allowing the creation of algorithms that process large data sets that show the correlation between user behavior and the probability of fraudulent action. Unlike the manual approach, machine learning uses artificial intelligence, which is fast and provides real-time data processing for more accurate results. One fintech company, Feedzai, claimed that a fine-tuned ML solution could detect 95% of all fraud, thus minimizing the cost of manual reconciliation.
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