AI Based Methods for Identifying Phishing Methods

By: Ameya Sree Kasa; Department of Computer Science & Engineering (Artificial Intelligence), Madanapalle Institute of Technology & Science, Angallu (517325), Andhra Pradesh. ameyasreekasa@gmail.com

Abstract:

It is noticed that the level of activity in terms of cybercrimes is on the rise – what is even worse, the hostile actors are more desperate – they are trying to lure people into providing fundamental information. As can be seen, traditional security measures – the most popular among which are blacklisting and heuristic rules – are insufficient to protect a computer from attacks from the aggressors who act at the rate of minutes. In this regard artificial intelligence revealed itself as one of the most potent tools whereby these new threats can be met. This paper aims at analyzing the application of Intelligent methods that are, machine learning, natural language processing, and deep learning in tackling the problem of phishing attempts. What it does is analyze the different approaches; their strength and weakness that center on one idea and that is the necessity for the continuous learning and updating and at the very least being ahead at any one point in time with whatever your opponents have. It turns then to some of the most important open questions: methods of avoidance, data concealing, perpetuity, and structural analysis of the model.

Keywords: Phishing attacks, Cybersecurity, Artificial Intelligence, Data Privacy

1. Introduction:

Phishing, where consumers are lured into sharing their password, credit card details and many other personal details with the scam artists, is becoming more frequent and real. This can mean large losses of your money, fake identity being used on your behalf, and you lose your information. The more sophisticated analytical blacklists, heuristic rules employed as a means of protection against phishing attacks prove to be ineffective because of the changes that attackers make in their modes of operation. As we have seen, AI has emerged as a potent tool in the fight against phishing in this regard[1]. Due to the machine learning methods and as well as natural language processing and deep learning methods of the AI solution, it can identify and prevent the phishing attack appropriately. It can make quick work of obtaining the overall trend as well as response to added techniques of phishing data with no time at all. Also, AI models were able to learn over time from new data and may be more accurate and highly resilient in the process. The self-adaptive ability is very crucial to be immune to the ever-changing tactics that the phishers employ. In adopting AI, organizations will be in a vantage position to counter attackers ─ US better defending sensitive information. So, we have the following AI identification methods concerning the recognition of phishing:

Figure : AI Techniques for detecting phishing

2. AI-Based Methods for Identifying Phishing Attacks:

2.1. Machine Learning:

• Supervised Learning: In the supervised learning approach, Phishing detection entails coming up with models that will use datasets that are labelled from both the phishing as well as the legal emails or website. Naïve Bayes, support vector machines (SVM), and neural networks are some of the models that classify new data based on learnt patterns. This technique increases detection capacity through other ingredients like; any increase in the use of some certain Keywords, the nature of the URLs involved, and other ingredients like emails. [2]
• Unsupervised Learning: However, there are several methods for applying unsupervised learning though which is what is employed to detect previously unnamed trends or patterns of emails or web traffic by which most phishing go unnoticed. Clustering methods can be equally effective in grouping similar behaviors and anomaly detection techniques can identify activities that can be potentially related to phishing attempts. From this, new and changing patterns of the phishing strategies is easily comprehensible, hence helping in early notice of the strategies. [3]
• Reinforcement Learning: Reinforcement learning for the purpose of phishing is a form of learning whereby the experiences in the form of feedback consisting of both phishing and valid information is used to profile the models used in the system. From such interactions, it is able to learn and in the course of time, improve the ability to detect and respond to new sequences of such phishing threats. It is dynamic in its approach to learning and thus enhances the ability of the system to look forward to better ways of phishing and achieve better rates of detection in the future.
• Challenges of the approach: The following are the major difficulties of implementing anti-phishing machine learning algorithms; These include the requirement to use big, clean data set and constantly update for the new mirror phishing strategies. The concept is faced with the following difficulties and challenges; high False positive and False negative rates, difficulty in selection of the features, Black Box Models are used, Scalability and Efficiency, Adversarial Attacks Challenges, and Biases affect both the accuracy and help to detect the phishing attempts. [4]

Figure 2: Machine Learning

2.2. Natural Language Processing:

Natural Language Processing (NLP) identifies and prevents phishing attacks by analyzing and comprehending the textual content of emails, messages, webpages and detects the mail is spam or not as shown in Figure 3

• Text Classification: Non-structured data can be further analyzed by text categorization which involves analyzing the content of an e-mail or communication with the aim of deciding whether or not the e-mail/communication is a phishing one or not. This involves searching for specific keywords and phrases associated with phisher activities, and, in addition, analyzing the text in the context to be able to understand the meaning and intent behind the message, to distinguish between genuine and fake messages.
• Sentiment Analysis: Phishing mails can contain a call to hurried action or an appeal to fear: these techniques are noticed by NLP-based sentiment analysis. Promising NLP models can screen bad messages through deciding if the tone is threatening, urgent, or very praising. [5]
• Phishing link analysis and Spam filtering: NLP in the context of the phishing link implies extracting information about fake links to find the signs of phishing and disparities between the descriptions of the links and the addresses themselves. Moreover, we also find that the natural language processing techniques used for spam filtration can be deployed to identify phishing attempts by analyzing the content components and regularities like those in spam and phishing mails. [6]
• Challenges of the approach: Phishing techniques are constantly adapting; therefore, it is a problem to update models with new data and methods when using NLP for phishing detection. One problem remains unsolved – how to guarantee that the context is properly recognized, particularly in complex/baffling situations. In addition, the concept of multilingual support concerns the application of NLP models that are robust and adaptive to a host of multiple linguistic conditions which applies in handling of the phishing attempts in various languages.[7]

2.3. Deep Learning:

Thus, from Figure 4 below it is evident that with the help of the deep learning model based on the neural network, there are high prospects in the identification of the phishing attacks.

• Recurrent Neural Networks (RNN): LSTM based RNNs are quite good at text sequences, for example rich body text of the email to detect phishing events. They can however be somewhat weak in overemphasizing sequences and a few of them are not very fast to compute.
• Convolutional neural networks (CNN): As has been evidenced, CNNs can analyze texts, and that they also can analyze pictures. Text analysis may discover local trends in emails’ texts, to picture analysis may show that there are local practices in the look of the web sites. Two of them are Text style is hard to distinguish: and Image categorization requires large sets of tagged images.
• Generative Adversarial Networks (GAN): While the two are related, the predatory GANs make fake phishing samples that will enhance the generation of specific phishing samples, boosting the algorithms’ efficiency of identifying the same. However, the GANs are challenging to train, and sometimes the synthetic instances generated may not very much reflect the actual real-life phishing attacks. [8]
• Challenges of the approach: Part and parcel of deep learning models, some of the challenges include high computation needed, amount of data required in training, labelling of data, a problem with overfitting and more critically, lack of model interpretability. Apart from it, since there are different types of phishing, it becomes necessary to modify these models and also to ensure that these are useful. [9]

2.4. Hybrid Models:

Combining multiple AI algorithms can greatly improve phishing detection systems by harnessing their complementary strengths.

• Ensemble Techniques: Ensemble approaches, such as Random Forests and Gradient Boosting, improve accuracy and robustness by combining predictions from multiple models. These strategies improve performance, but they are complicated to implement and computationally intensive, necessitating careful model diversity and integration management.
• Multiple-Modal Learning: Multi-modal learning combines data from a variety of sources, including text, photos, and metadata, to provide a comprehensive analysis of probable phishing attacks. While this technique provides a more comprehensive perspective, it poses issues in organizing and synchronizing various data kinds, as well as assuring the smooth integration of disparate information.
• Challenges of the approach: Hybrid models confront issues such as higher computational and resource requirements, complexity in training and tuning, and integration of data from many sources. Balancing these elements while maintaining high detection accuracy and system efficiency is critical.

3. Issues and Future Directions:

There are several challenges that phishing detection systems face: evasion techniques where the attackers can devise different methods through which the AI models cannot detect the phishing attacks implying that the models need to be updated continually. Some privacy challenges arise from the process of scraping and analyzing a large amount of email and traffic data, and it becomes challenging to balance privacy while at the same time achieving the best model. It is important to detect them in real time, so that consumers do not become victims of phishing attacks, and therefore AI models should be efficient and fast. Moreover, the black-box nature of deep learning models is another challenge to understand how the models arrived at the decisions, which therefore requires methods to explain the predictions in order to retain users’ trust and to adhere to the regulative requirements..[10]

4. Conclusion:

Using artificial intelligence or more advanced technologies like machine learning, natural language processing as well as deep learning, the important and powerful tools for countering various forms of phishing attacks are received. The concept shows how these technologies can take large amounts of data, analyze data, make decisions and learn while on the move so that the impact of phishing is greatly minimized for the individual as well as the company. The machine learning approach uses a set of patterns of previous scams to identify future scams, and other approaches such as natural language processing (NLP) deal with text and contexts to identify manipulations. Pattern analysis recognition writes the enhancement of the detection accuracy into complicated neural networks that are adequate in handling big and hard data patterns. Nevertheless, there are some issues, for example, there are methods elaborated because of attackers that provide them with an opportunity to conceal the fact of the attack for a longer time, that is why the model is updated much more frequently. With every email and web traffic that is collected and monitored, especially when it is done in large quantity, there are likely to be some violations of data privacy hence there must be control on the quantity accumulated. However, that the feed processed is a feed in real time is important to prevent phishing attacks which in turn implies great efficiency and fast processing. The process of decision making is still a big problem with the deep learning models and therefore failing to meet the interpretability aspect because the models are deep and complex. These barriers can be pulled therefore allowing the full realization of AI based technologies while boosting cybersecurity against phishing attacks.

5. References:

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Cite As

Kasa A.S. (2024) AI Based Methods for Identifying Phishing Methods, Insights2Techinfo, pp.1

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