Cyber Security & Network Forensics

Social Engineering Phishing Detection

By: Achit Katiyar1,2

1South Asian University, New Delhi, India.

2International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan. Email: achitktr@gmail.com

Abstract

In the age of technology, when information is what matters and faith is the means of business, social engineering phishing attacks have become an advanced and common danger to both individuals and companies. These attacks, which are based on the art of fraud and manipulation, target human beings rather than technological faults, making them very harmful and difficult to defend. Cybercriminals create a web of fraud by acting as reliable institutions, resulting in individuals into revealing critical information or engaging in security-compromising behaviours. The article goes into the hidden techniques of social engineering phishing, looking at the behavioural factors that make us vulnerable, attacker’s developing strategies, and the vital necessity for an integrated approach to security. Through real-world examples and expert views, we aim to shine a light on the human aspect at the core of these cyber dangers, providing readers with the information and tools they need to strengthen their defenses against unknown competitors covering their inboxes. As we navigate the dangerous oceans of the internet world, knowing the human aspect of cybersecurity becomes not just beneficial but also necessary.

Introduction

Phishing attacks use social engineering strategies to trick people into giving sensitive information [1]. Its manifestations have become more numerous, and, correspondingly, the requirements towards the potential detection means. There is an indication that different types of phishing are on the rise, and therefore, a society needs to have a strong army to help counter such actions [2]. Before this, one of the effects of a typical phishing attack included draining lots of money from an organization and adversely affecting the company’s reputation. As a result, there is a great significance that should be accorded to the knowledge and combating of intents involved in phishing as far as the safety of cyberspace is concerned.

Social Engineering Phishing Techniques

Some of the strategies that the phishers use in the course of defrauding the victims include email forgery, links to malicious website, and fake websites [3][4]. Malicious links drive consumers to fake websites that collect sensitive information. Deceptive websites imitate reputable websites in order to trick visitors into entering their credentials. Understanding these strategies is critical for creating efficient detection systems [5]. Recent research have shown that phishing strategies are becoming more complicated, making old detection methods less successful [6].

Machine Learning-Based Detection

Phishing attacks can, therefore, be described as a chief threat in computer networks, and the research findings of the current studies show that machine learning (ML) is an effective way of detecting it. In more detail, ML algorithms analyze data that is most likely to hold records of potential phishing schemes. Other global supervised approaches that have been proven to be efficient in the identification of phishing email include decision trees and support vector machines [3]. Unsupervised learning algorithms may detect novel phishing patterns even without labeled data [7]. Deep learning approaches, such as neural networks, improve detection performance by learning complicated characteristics from data [8]. Integrating machine learning with current security infrastructure may greatly enhance phishing detection. Methods for detecting phishing are available in the operators of machine learning mentioned in the following Figure 1.

Figure 1 : Machine Learning Workflow for Phishing Detection

Human-Centric Detection Approaches

While machine learning allows for automated detection, human-centric techniques remain important [9]. Educating users about phishing strategies and raising awareness can dramatically lower their vulnerability to assaults [10]. Employee training programs can help them spot and respond to phishing efforts [2]. Implementing security procedures that require caution while handling emails and links can reduce risks [11]. User feedback techniques can enhance detection systems by giving real-world information about phishing attempts. Therefore, as a result of incorporating human observation along with the support of algorithms, the process of countering the problem of phishing stands to benefit from greater efficiency [12].

Recent Advances and Challenges

New studies have also suggested that integrating the heuristics of the algorithms with ML will help improve detection accuracy [13]. These models bear the qualities of the two and provide highly intricate programs to locate the most subtle form of phishes [5]. However, there are such concerns as the change in strategies of carrying out phishing attacks and the major availability of labeled data necessary for training the ML models. Another research direction might be dedicated to the development of models describing the appearance of new forms of phishing attacks and enabling their real-time identification. Besides this problem, there is another problem of the trade-off between the detection rate and the time needed for the computation [3], [14]. Another significant factor is privacy, safety, and likeness of a user while using the data of a specific user for training of an ML model [5]. The identity and security of the patients that utilise the safe types of authentication, have the modes of encrypted communications and have consistent dependability of access to the PHRs [15]. Traditional farming, which is based on physical labor, animal power, and primitive agricultural practices that are susceptible to environmental conditions, has long struggled with efficiency, scalability, and sustainability [16].

Conclusion

Detecting social engineering phishing attempts necessitates a multidimensional strategy that combines machine learning and human-centered tactics. Considering that the new emerging phishing strategies need to be fought, it is crucial to add constant progress in technologies, including the analyzed machine learning; as well as improvement in the user’s awareness on the right procedure [11]. Thus, it is necessary to outline future research in accordance with developing new ML algorithms that can detect new, previously unknown types of attack in real-time. From such observations, one can conclude that to reach the ultimate optimality of the solutions in the sphere of the detection of phishing, it is required to have several more efforts of the scholars on one side and representatives of the industries interested in computer vulnerabilities and, on the other side, lawmakers, who are to encode reasonable responses to the challenges maintained by phishing at one and the same phase. That is why it is necessary to have preventive measures in the case of this type of attack, as this will help in minimizing the threats.

References

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Cite As

Katiyar A. (2024) Social Engineering Phishing Detection, Insights2Techinfo, pp.1

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