By: Jampula Navaneeth1
1Vel Tech University, Chennai, India
2International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan Email: navaneethjampula@gmai.com
Abstract
As a Branch of computer science that is rapidly developing, cybersecurity has seen the incorporation of machine learning as a game-changer in the protection of cyber threats. And, among all the types of threats, phishing is considered to be one of the most topical and global. Thus, with the help of machine learning, the defense of an organization can be improved and it becomes easier for an organization to protect information from being leaked to hackers. This article will show how the future of cyber defence is going to be there in between machine learning and phishing attacks.
Keywords: Cyber security, Machine Learning, Phishing, Cyber Defence
Introduction
As different and new technologies appear the threats to cyber space are also coming up presenting different challenges to the users of the internet and organizations. These threats are very common, and despite the seen advancement in the cyber world, phishing attacks act as bright evidence. But with the today ML emergence, there is the new promising direction in fight against phishing and in general against the malicious activities on the Internet. Phishing is a network attack that incorporates both social engineering and computer technology in order to defraud the users by getting their personal details. Phishing is where the attacker makes a request to a person to click the phishing link by sending him/her an email, SMS or even spared a message on the social network account [1].
What is Phishing
Phishing is a kind of cyberattack where the attackers pretend to be bona fide entities to gain information from individuals such as passwords, credit card, and identity numbers. These attacks common in the email, but can also be done through SMS, social media platforms, or through suspicious websites [2]. Phishing attacks are based on certain psychological features and therefore cannot be effectively countered with the help of program-related measures [3].
Machine Learning in Cyber Defense
AI widely defines as the integration of machine learning that enables computers being taught on how to learn from the data they receive. However, in as much as cybersecurity application is concerned, the ML can be effective as the traditional methods in identifying the threats of phishing. Here’s how ML is transforming the fight against phishing: The following is how this ML is revolutionizing the fight against phishing:

1.Pattern Recognition: The previous strategies used in IT security are those that involve the use of ML algorithms, which are able to study big data sets and look for correlations between these and the nature of the phishing attacks. For instance, the headers of the mails, the body of the mails, links and attachments of the mails are some of the aspects that can be critiqued Although through pattern matching, these algorithms can be able to detect mere signs of phishing which even human beings, other securities measures cannot even detect [4].
2. Behavioural Analysis: It is noted that because of the possibility of ‘watching for’ users’ actions ML is capable of setting baseline activity levels, regular for a user. Any deviation from this base behaviour is considered as exception and thus things like login from different locations or a different time of the day when sending an e-mail can be seen as tries at presenting a phishing message. These measures allow for the prediction of attacks before they lead to a loss of much value [4].
3. Real-Time Threat Detection: Spear phishing has different heuristics used and the most frequent one includes the blacklist/signature-based systems that detect already known threats. The SP ML technique in turn can identify in real time new techniques that the phishers could be using. Since the ML models are trained from new data, the systems are able to cut the rate of threats that are developed, making it preferable [4].
4. Automated Response: Besides the detection, it is also applied in the response to the threats, which are involved in the phishing. For instance, user can categorize potentially risky mails and bounces them to another folder and notify the user of suspected risks. This assists in minimizing the time interval within which a possible attacker can launch an attack as well as the impact of a phishing attack [4].
Challenges
While ML has shown its usefulness in countering phishing repeatedly, it does come with challenges. ML models are successful as they learn from data, the accuracy and generality of ML model is entirely dependent on the quality diversity in their training. False positives or missed threats may occur with poor/biased data In, addition, the cyber criminals are using these AI and ML technologies to produce more complex phishing attacks respectively this results in an ever-tilted balance of power between attackers and defenders [5].
Conclusion
By Concluding, incorporating machine learning into cybersecurity strategies is among the most effective ways to fight against phishing. When examining these techniques, it is clear that the use of ML capabilities in pattern recognition, behavioural analysis, real-time detection, and automated response can significantly improve an organization’s security. Still, new versions of ML models must be developed through algorithm tuning so as to adapt to current threats. In relation to the long-term development of defense against evolving threats and technologies for protecting digital assets in organizations through machine learning; its relevance will continuously increase.
References
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Cite As
Navaneeth J. (2024) The Future of Cyber Defense: Machine Learning and Phishing, Insights2Techinfo, pp.1