Hook, Line, and Sinker: AI-Powered Phishing Defense of Digital Communications

Harsh Rathod; Pooja Purohit; Rishika Singh; Niki Modi

doi:10.20944/preprints202506.1525.v1

Submitted:

17 June 2025

Posted:

19 June 2025

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Abstract

The rapid evolution of phishing attacks targeting email, chat, and social media platforms poses a significant threat to digital security, with a reported 667% surge in spear-phishing during the 2020 COVID-19 crisis [1]. Current AI-based detection systems face challenges in dataset diversity, adversarial robustness, computational scalability, model interpretability, and privacy preservation, limiting their efficacy in real-time, multi-platform environments. This paper introduces PhishGuard, an innovative framework for real-time phishing detection, designed to overcome these limitations. PhishGuard integrates lightweight transformer models (e.g., distilled BERT), hybrid detection techniques combining natural language processing (NLP), propagation analysis, and user behavior analysis, and explainable AI (XAI) methods like SHAP and LIME for transparent decision-making. Privacy-preserving techniques, including federated learning and local differential privacy, ensure secure processing of sensitive user data. Evaluated on diverse datasets such as PhiKitA, Enron, and a custom social media corpus, PhishGuard achieves up to 97.5% accuracy, 94% F1-score, and inference times below 5 ms, demonstrating scalability for resource-constrained devices. The framework also incorporates zero-knowledge proofs for verifiable inference, addressing trust and integrity concerns. By tackling cross-domain generalization, adversarial robustness, and real-time performance, PhishGuard offers a scalable, user centric solution for secure digital communications, with applications in finance, healthcare, and social media platforms. Future enhancements include multilingual support and image based phishing detection, paving the way for a comprehensive defense against evolving cyber threats.

Keywords:

phishing detection

;

artificial intelligence

;

real-time communications

;

natural language processing

;

explainable AI

;

federated learning

;

adversarial robustness

;

dataset diversity

Subject:

Computer Science and Mathematics - Artificial Intelligence and Machine Learning

I. Introduction

Digital communication platforms, including email, chat applications, and social media like X, have transformed global connectivity, enabling seamless information exchange. However, this interconnectedness has fueled a surge in phishing attacks, which deceive users into revealing sensitive data or engaging with malicious content. These attacks exploit trust through carefully crafted messages, leveraging generative AI to mimic legitimate communications across diverse platforms.

The severity of phishing is evident in its economic and societal impact, with global losses estimated at $6 billion annually [3]. A 667% spike in spear-phishing during the 2020 COVID-19 crisis underscores the adaptability of attackers, who exploit urgent narratives to manipulate users [1]. Traditional detection methods, such as URL blacklists and rule-based filters, struggle to counter these dynamic, context-driven threats, necessitating advanced AI-driven solutions [2].

Despite progress in AI-based phishing detection, critical gaps remain. Current datasets, like PhiKitA, lack diversity, often excluding mobile app or multi-stage attacks prevalent in chat and social media [2]. Deep learning models face adversarial vulnerabilities, with a 22.3% adversarial success rate against CNNs [2]. Additionally, complex models lack interpretability, computational costs hinder real-time deployment (e.g., 150 seconds for GAN training [2]), and privacy-preserving techniques like federated learning are underexplored [5].

This paper proposes PhishGuard, a framework for real-time phishing detection across email, chat, and social media. Integrating lightweight transformer models, hybrid detection (NLP, propagation analysis, user behavior), explainable AI, and privacy-preserving methods (federated learning, differential privacy), PhishGuard addresses these gaps. Evaluated on PhiKitA, Enron, and a custom social media corpus, it offers scalable, transparent, and secure phishing defense.

II. Literature Review

Phishing detection in digital communications has evolved significantly with the adoption of AI, particularly for email, chat, and social media platforms. Content-based approaches analyze linguistic features to identify phishing attempts. Gupta et al. [3] employed term frequency-inverse document frequency (TF-IDF) with Support Vector Machines (SVM) for email phishing detection, achieving 95% accuracy but struggling with context-heavy messages. Similarly, Sahingoz et al. [6] used Random Forests to classify phishing URLs, reporting 94% precision but limited generalization to social media or chat-based attacks. These methods rely on static features, often failing to capture dynamic phishing tactics leveraging generative AI [2].

Propagation-based approaches focus on how phishing content spreads across social networks. Ma et al. [5] developed tree-structured recursive neural networks to model X post propagation, observing that phishing messages exhibit deeper, more chaotic retweet structures compared to legitimate content, improving detection by 10% over baseline classifiers. Bian et al. [7] utilized bi-directional graph convolutional networks to analyze user interactions, achieving a 12% accuracy boost by incorporating sharing patterns. However, these methods are computationally intensive and lack datasets covering mobile app or multi-stage phishing attacks, limiting cross-platform applicability [2].

Deep learning and natural language processing (NLP) have advanced phishing detection by capturing contextual nuances. Kaliyar et al. [8] adapted BERT for social media phishing, achieving 97% accuracy by analyzing linguistic cues like urgency or impersonation. Ruchansky et al. [9] proposed a hybrid model combining text, user behavior, and source credibility, enabling early-stage detection with 96% F1-score. Despite high accuracy, these models face adversarial vulnerabilities, with a 22.3% adversarial success rate against CNNs, and lack interpretability, hindering trust in sensitive domains like finance [2].

Real-time deployment and privacy remain critical challenges. Deep learning models, such as GANs, require significant computational resources (e.g., 150 seconds training time, 350 MB memory), limiting scalability on resource-constrained devices [2]. Privacy-preserving techniques like federated learning and differential privacy are underexplored, despite their importance for protecting sensitive user data in chat and email systems [5]. Additionally, high false positive rates and insufficient dataset diversity (e.g., PhiKitA’s focus on email URLs) restrict cross-domain generalization [2]. These gaps underscore the need for a scalable, robust, and transparent phishing detection framework.

III. Problem Statement

A. Objectives

Real-Time Detection: Develop an AI-powered system, PhishGuard, to accurately and rapidly classify content in email, chat, and social media as “Phishing” or “Legitimate,” leveraging natural language processing (NLP) and machine learning techniques for real-time analysis in dynamic communication environments.

Contextual Understanding: Equip the model to identify subtle linguistic cues, such as urgency or impersonation, and behavioral patterns, ensuring adaptability to diverse phishing tactics across platforms, including multi-stage attacks and AI-generated deceptive content.

Scalability and Accessibility: Build a scalable, user-friendly web application that handles high traffic volumes, delivers rapid responses, and is accessible across devices, enabling seamless deployment in high-stakes settings like finance and healthcare.

Accuracy and Robustness: Enhance detection accuracy using advanced models like distilled BERT, fortified against adversarial attacks, and continuously updated to counter evolving phishing strategies, ensuring robust performance across diverse datasets.

Real-Time Feedback: Provide instant feedback with confidence scores and transparent explanations, allowing users to assess message credibility within seconds, fostering trust and usability in real-time communication platforms.

B. Idea

The PhishGuard framework enables real-time phishing detection across email, chat, and social media by integrating advanced AI techniques. Textual analysis leverages lightweight transformer models, such as distilled BERT, to classify messages as “Phishing” or “Legitimate” based on linguistic cues like urgency or impersonation. The classification probability is modeled using a softmax function:

P (y = Phish∣ x) = \frac{e^{z_{Phish}}}{e^{z_{Phish}} + e^{z_{Legitimate}}}

Where

z_{Phish}

and

z_{Legitimate}

are logits for input text $ x $, derived from a distilled BERT model optimized via quantization for low-latency inference [4,8]. This approach addresses dataset diversity by training on PhiKitA, Enron, and a custom social media corpus, capturing multi-platform phishing patterns [2].

Propagation analysis enhances detection by modeling message spread on social media platforms like X. Using graph-based techniques inspired by Bian et al. [7], PhishGuard analyzes retweet and reply structures to identify chaotic propagation patterns indicative of phishing. The propagation probability is calculated as:

[P (spread) = \frac{\sum_{u \in U} I (u)}{|U|}]

Where

I (u)

indicates if user

u

shares the content, and

|U|

is the total user base. This method improves cross-domain generalization, addressing the gap in mobile app and multi-stage attack detection [2].

User interaction and feedback are integral to PhishGuard’s real-time web application. Users submit message text for instant classification, receiving confidence scores and explanations via explainable AI (XAI) techniques like SHAP. Model uncertainty is quantified to enhance trust:

U n c e r t a i n t y = 1 - m a x (P (y = P h i s h| x), P (y = L e g i t i m a t e| x))

This minimizes false positives, a key challenge in real-time systems, and provides transparent feedback, fostering user confidence across email, chat, and social media platforms [2,9].

Privacy and ethical considerations are addressed through federated learning and local differential privacy. Federated learning aggregates model updates without sharing raw data, using secure aggregation protocols [10]. Local differential privacy adds noise to inputs:

\tilde{x} = x + N (0, σ^{2}), σ = \sqrt{\frac{2 \ln (\frac{1.25}{δ})}{ϵ}}

where

ϵ

and

σ

control privacy guarantees [11]. Transparent classification explanations align with ethical AI principles, ensuring user data security and addressing privacy gaps in phishing detection [2,5].

C. Problems Faced

Contextual Understanding and Ambiguity: Phishing messages often employ subtle linguistic cues, such as impersonation or urgency, and may include ambiguous or context-dependent content that mimics legitimate communications. Detecting these nuances, particularly in chat and social media where informal language and emojis are prevalent, poses a significant challenge. For instance, BERT-based models struggle with sarcasm or culturally specific phrases, reducing accuracy in multi-platform settings [2,8]. PhishGuard must adapt to diverse linguistic patterns and multi-stage attacks to maintain effectiveness across communication channels.

Data Privacy and Security: As a web-based application processing sensitive user data (e.g., email content, chat messages), ensuring privacy and security is critical. Storing or analyzing user-submitted messages risks potential breaches, and users may distrust systems that retain data for model improvement. Implementing federated learning and differential privacy, as proposed in PhishGuard, is complex, requiring robust encryption and compliance with regulations like GDPR [5,11]. Building user trust through transparent data handling remains a key hurdle.

Scalability and Latency: Real-time phishing detection demands low-latency responses, especially during high-traffic events like cyberattack surges. Deep learning models, such as distilled BERT, require significant computational resources, with inference times of 3.8–5.0 ms for CNNs [2]. Scaling PhishGuard to handle large-scale interactions across email, chat, and social media, particularly in regions with variable network speeds, is technically challenging. Optimization techniques like model pruning and distributed computing are essential but increase development complexity.

Bias and Accuracy: AI models risk introducing bias if trained on imbalanced or incomplete datasets, leading to false positives or missed phishing attempts. For example, PhiKitA focuses on email URLs, lacking coverage for social media or mobile app-based attacks [2]. This limits PhishGuard’s cross-domain generalization, potentially causing inaccurate classifications in diverse contexts. Techniques like SMOTE or GANs for data augmentation are needed to address imbalance, but their integration into real-time systems remains a challenge [2].

IV. Proposal System

A. Architecture Diagram

The architecture diagram (Figure 1) depicts PhishGuard’s internal structure for real-time phishing detection across email, chat, and social media platforms. The four layers and their theoretical roles in addressing research gaps are:

Data Ingestion Layer:

This layer collects messages from email servers, chat apps like WhatsApp, and social media platforms such as X, handling diverse formats. By normalizing these inputs, it ensures dataset diversity, supporting robust cross-platform detection, a key focus in recent phishing studies.

Hybrid Analysis Layer:

This layer analyzes messages using distilled BERT for text, GCNs for propagation patterns, and user behavior modeling. Integrating multi-modal features enhances adversarial robustness against evasion techniques like polymorphic URLs, as Kaliyar et al. note in their research.

Privacy Layer:

The privacy layer uses federated learning and differential privacy to protect user data during analysis. These methods address privacy concerns in sensitive contexts like finance, a priority emphasized by Bonawitz et al. and Dwork et al.

Output Layer:

This layer classifies messages as phishing or legitimate, using SHAP explanations to highlight features like “urgent.” This improves interpretability, fostering user trust, a factor Ruchansky et al. identify as essential for adoption.

This architecture ensures a privacy-preserving, interpretable approach to phishing detection across platforms.

B. System Flow

Attack Cycle (Left Side):

Malicious Source: Represents adversaries launching phishing campaigns by sending deceptive messages, such as fraudulent emails mimicking bank alerts, WhatsApp messages promising fake lottery wins, or X posts containing malicious URLs. These attacks exploit user vulnerabilities, such as trust in familiar brands or urgency cues, as noted by Hong in his study on phishing attack trends.

Figure 2. System cycle.

Propagation Phase: Captures the spread of phishing content as users forward emails, share messages in group chats, or retweet posts, amplifying the attack’s reach. Bian et al. highlight how such propagation underscores the challenge of cross-domain threats, where attacks spread rapidly across platforms, necessitating robust detection mechanisms.

PhishGuard Response Cycle (Right Side):

Captures and processes messages from diverse platforms, ensuring comprehensive coverage of email, chat, and social media communications. This approach tackles dataset diversity by enabling the system to handle varied input formats, such as structured email headers, unstructured chat texts, and social media metadata, improving generalizability across domains, a key focus in recent phishing detection methodologies.

Mitigates evolving attacks through multi-modal analysis, combining textual, structural, and behavioral cues to detect sophisticated phishing attempts, such as polymorphic URLs or social engineering tactics. Kaliyar et al. emphasize that this enhances adversarial robustness by countering evasion techniques that exploit single-modal detection weaknesses, ensuring PhishGuard remains effective against dynamic threats.

Protects user data with privacy-preserving techniques, ensuring that sensitive information, such as email content or chat histories, remains secure during analysis. Bonawitz et al. and Dwork et al. stress the importance of such measures, particularly in regulated sectors like finance and healthcare, where data breaches can have severe consequences, addressing privacy concerns effectively.

Provides classifications (e.g., labeling a message as Phishing or Legitimate) with actionable explanations, such as highlighting “click now” as a suspicious phrase. Ruchansky et al. note that this improves interpretability, enabling users to understand detection outcomes and make informed decisions, which is critical for fostering trust in automated systems in high-stakes domains.

V. Application Overview

The PhishGuard framework is an AI-powered solution designed to combat phishing attacks across email, chat, and social media platforms in real time. It leverages advanced machine learning and natural language processing to detect deceptive messages, ensuring cross-platform compatibility and user privacy through federated learning. With a user-friendly interface built on Flutter, PhishGuard delivers interpretable results, empowering users with actionable insights and educational resources.

A. Key features

Real-Time Phishing Detection:

PhishGuard excels in identifying phishing attempts across email, chat, and social media platforms in real time. By continuously monitoring incoming messages, the system ensures timely detection, protecting users from fraudulent schemes like fake login prompts or malicious links before they can cause harm.

Cross-Platform Compatibility:

The framework operates seamlessly across diverse communication channels, handling varied formats such as email headers, chat texts, and social media posts. This ensures comprehensive protection, addressing the challenge of phishing attacks that span multiple platforms with differing structures.

Privacy Preservation:

User privacy is a priority, with PhishGuard employing federated learning and differential privacy to secure sensitive data. Bonawitz et al. highlight the importance of such techniques, especially in sectors like finance, ensuring that user information remains protected during analysis.

Interpretable Results:

The system provides clear explanations for its classifications, such as identifying “click now” as a suspicious phrase. Ruchansky et al. emphasize that this interpretability fosters user trust, enabling informed decision-making in high-stakes scenarios.

Scalable Architecture:

Designed for enterprise-level deployment, PhishGuard’s architecture supports high-traffic environments. Its scalability ensures consistent performance, making it suitable for large-scale applications where millions of messages are processed daily.

Educational Resources:

Beyond detection, PhishGuard offers resources to educate users on phishing tactics, such as recognizing social engineering cues. These tools empower users to navigate digital communications confidently, reducing their vulnerability to future attacks.

B. Technology stack

Machine Learning (ML):

At its core, PhishGuard leverages machine learning models trained on diverse datasets like PhiKitA and Enron. Using PyTorch, the system implements distilled BERT for text analysis and graph convolutional networks for propagation analysis, ensuring accurate detection of phishing attempts across platforms.

Natural Language Processing (NLP):

NLP capabilities are powered by spaCy, which preprocesses text from emails, chats, and social media posts. This enables PhishGuard to understand context and identify phishing indicators, such as urgency cues or suspicious phrases, enhancing detection reliability.

Privacy Mechanisms:

To safeguard user data, PhishGuard integrates Flower for federated learning and Opacus for differential privacy. Dwork et al. advocate for such mechanisms, which ensure secure model updates without compromising sensitive information during processing.

Real-Time Data Processing:

Future development will introduce multimodal analysis to evaluate images and URLs alongside text. By leveraging computer vision, PhishGuard can detect phishing in visual content, such as fake login pages or misleading graphics, providing a more comprehensive defense against sophisticated attacks.

Real-Time Threat Intelligence:

Integrating real-time threat intelligence feeds will enable PhishGuard to adapt to emerging phishing trends. This enhancement will improve adversarial robustness, ensuring the system remains effective against novel attack vectors in an ever-evolving threat landscape.

Multilingual Support:

To broaden its global reach, support for multiple languages, such as Hindi and Arabic, will be added. This will allow users worldwide to analyze messages in their native languages, addressing dataset diversity and enhancing PhishGuard’s applicability across linguistic boundaries.

Latency Optimization:

Improving latency in detection and explanation generation, especially on resource-constrained devices, will enhance user experience. Optimizing algorithms for mobile environments will ensure PhishGuard delivers fast, reliable results even on low-power devices.

Blockchain-Based Transparency:

Exploring blockchain-based audit trails for detection decisions could further bolster transparency. By logging classifications on a decentralized ledger, PhishGuard can ensure compliance with privacy regulations and provide verifiable trust in high-stakes domains like finance.

VI. Result and Discussion

The evaluation of PhishGuard demonstrates its efficacy in combating phishing attacks across email, chat, and social media platforms, with testing conducted on diverse datasets like PhiKitA and Enron. The system achieved a detection accuracy of 97.2%, outperforming traditional approaches like CANTINA+ by 9%, a benchmark established by Xiang et al. in their analysis of phishing detection frameworks. Precision reached 96.1%, while recall stood at 95.8%, reflecting PhishGuard’s ability to accurately identify both phishing and legitimate messages with minimal false positives. Real-time processing, powered by FastAPI and AWS infrastructure, resulted in an average latency of 0.28 seconds per message, making it highly suitable for high-traffic enterprise environments. Privacy-preserving mechanisms, including federated learning and differential privacy, ensured data security with only a 1% accuracy trade-off, a balance Dwork et al. highlight as critical for regulated industries like healthcare. User studies revealed that SHAP-based explanations increased trust by 32%, as users appreciated insights into phrases like “urgent action required,” aligning with Ruchansky et al.’s findings on the importance of transparency in AI systems.

Table 1 provides a detailed comparison of PhishGuard’s performance against traditional methods, focusing on accuracy across email, SMS, and social media spam. PhishGuard consistently outperforms traditional approaches, particularly in SMS detection, addressing a critical gap where short-text formats challenge conventional methods. The results underscore PhishGuard’s ability to leverage distilled BERT and GCNs, enhancing dataset diversity and adversarial robustness.

However, PhishGuard faced challenges with zero-day phishing attacks involving obfuscated URLs, indicating a need for enhanced adversarial robustness through techniques like multimodal analysis. Additionally, while cross-platform compatibility addressed dataset diversity, performance slightly varied across platforms, with email detection outperforming social media by 3% due to richer metadata. These findings position PhishGuard as a scalable, interpretable solution, though future improvements in handling novel attack vectors and platform-specific optimizations are essential to maintain its effectiveness in dynamic threat landscapes.

VII. Conclusion

This study presented PhishGuard, an AI-driven framework designed to detect phishing attacks in real time across email, chat, and social media, effectively addressing key research gaps in dataset diversity, privacy, and interpretability. Its high accuracy, privacy-preserving approach, and scalable architecture make it a promising tool for enterprise-level deployment, while educational resources empower users to better recognize phishing threats.

Future enhancements will focus on improving adversarial robustness against zero-day attacks and incorporating multilingual support to broaden its global impact. Ultimately, PhishGuard offers a resilient defense against the evolving landscape of phishing, contributing significantly to safer digital communications.

Acknowledgments

I would like to express my heartfelt gratitude to Ms. Niki Modi for her invaluable guidance and mentorship throughout the research paper “Hook, Line, and Sinker: AI-Powered Phishing Defense for Digital Communications.” Her expertise, support, and encouragement have been instrumental in shaping the direction of this work. I am deeply thankful for her constructive feedback, knowledge sharing, and significant contributions, which have greatly enriched the quality and success of this project.

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Figure 1. System Architecture.

Table 1. Accuracy Comparison of Phishing Detection Methods Across Categories.

Method	Email(%)	SMS(%)	Social Media Spam (%)
CANTINA+	88.5	82.3	85.0
Feature-Based Approach	86.2	80.1	83.7
PhishGuard(proposed)	97.5	94.8	96.2

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