Cyber Security & Network Forensics

Real-Time Phishing Detection: Challenges and Solutions in Streaming Data

By: Mosiur Rahaman1 and Princy Pappachan2

1International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan.

2Center for the Development of Language Teaching and Research, Asia University, Taiwan.

Abstract

One of the most persistent and evolving threats in the cyber world today is phishing attacks. The sophistication of these malicious endeavors to deceive individuals into providing sensitive information has increased, resulting in a substantial risk to the security of personal and organizational assets. Conventional techniques for identifying phishing attacks are frequently insufficient when addressing the fast-paced nature of contemporary cyber threats. Consequently, real-time phishing detection in streaming data environments has become an essential area of research and development. This comprehensive article analyzes the complex difficulties in detecting phishing attacks in real-time and suggests various innovative solutions to address these challenges effectively.

Keywords: phishing, real-time phishing, streaming data

Introduction

Phishing attacks are conducted commonly through emails, websites, or messages that appear legitimate but are intended to deceive recipients into revealing sensitive information, such as usernames, passwords, or credit card details. Attackers frequently employ social engineering techniques to induce a sense of urgency or trust, prompting the victim to act swiftly without proper caution [1]. Also, as cybercriminals are always improving their methods, the complexity of phishing schemes increases, making them more challenging to detect and counter. Presently, cybercriminals are scaling up phishing campaigns through the use of artificial intelligence (AI) and automation by using AI to create persuasive phishing emails, assess potential targets, and automation to streamline the dissemination of phishing messages, and execute extensive campaigns with minimal exertion [2].

With the increasing reliance on digital communication platforms, there are more opportunities for attackers to exploit, leading to a significant increase in the frequency of phishing attacks. This increasing number of phishing incidents highlights the need for strong detection mechanisms that can work in real-time, promptly identifying, and neutralizing threats before they can cause substantial damage [3]. Real-time detection is particularly crucial in environments were data streams continuously.

Challenges in Real-time Phishing Detection

Developing efficient real-time phishing detection systems requires addressing numerous substantial obstacles inherent to the characteristics of streaming data and the attackers’ ever-changing strategies. The challenges can be classified into five primary domains:

High Volume and Velocity of Data- The sheer volume and speed with which data is produced in contemporary digital environments presents one of the main obstacles in real-time phishing detection. Mass amounts of data are constantly produced by streaming data environments, including email services, social media platforms, and online transactions [4]. Given their usually time-consuming data aggregation and analysis requirements, traditional batch processing techniques are not fit for these high-throughput situations. To spot phishing efforts quickly, real-time detection systems must be able to ingest, process, and analyze data streams on demand.

Data Variability and Concept Drift- Phishing techniques are dynamic and always changing as attackers develop new ways to get around security measures. So, real-time detection systems face a great challenge from a phenomenon called concept drift, which is the process by which previously trained models become less useful over time due to changes in the statistical characteristics of the data [5]. Thus, real-time phishing detection systems must be adaptive, learn from fresh data, and quickly react to new threats to maintain high detection accuracy.

Latency and Computational Constraints- To guarantee the timely identification and mitigation of threats, real-time phishing detection necessitates low-latency processing. However, the computational complexity of hybrid models makes it difficult to achieve low latency while maintaining high accuracy and efficiency [6]. Also, hybrid models, which combine machine learning and heuristic-based approaches, frequently use resource-intensive algorithms, which can slow processing times. It is thus imperative to strike a balance between the speed of detection and the accuracy of detection to achieve effective real-time phishing detection.

Imbalanced Data- Compared to genuine activities, phishing attempts are typically uncommon, leading to drastically unbalanced datasets. This imbalance can result in biased model performance, as the model may favor the majority class (legitimate data) over the minority class (phishing data) [7]. Consequently, the detection system may fail to identify numerous phishing attempts, resulting in false negatives. Accordingly, resolving data imbalance is crucial to maintaining the model’s performance with different data distributions.

Feature extraction and selection- The performance of phishing detection models is significantly influenced by the extraction and selection of features [8]. However, extracting pertinent features from streaming data can be challenging due to the dynamic and heterogeneous characteristics of the data. In addition, feature selection must be efficient to reduce computational overhead while retaining the most informative features for accurate detection. The success of real-time phishing detection systems is thus contingent upon developing robust feature extraction and selection methods, which is quite challenging.

Figure 1:Illustrated Challenges and Solutions in Real-time Phishing Detection

Solutions for Real-time Phishing Detection

The following are some of the strategies that organizations can implement to bolster real-time defense mechanisms against phishing attacks.

Stream Processing Frameworks– To deal with the high volume and velocity of streaming data, real-time phishing detection systems can use advanced stream processing frameworks like Apache Kafka, Apache Flink, and Apache Spark Streaming [9]. These systems provide the required infrastructure for real-time data intake, processing, and analysis since they are meant to effectively manage huge data flows.

Adaptive Learning Algorithms- Adaptive learning algorithms can be implemented in real-time phishing detection systems to mitigate the issue of concept drift and guarantee that the model remains pertinent over time. These algorithms allow models to dynamically adjust their parameters and learn continuously from new data, making these algorithms ideal for streaming data environments [10]. Additionally, incremental learning techniques update the model incrementally with each new data point, allowing it to adapt to shifting data patterns, unlike traditional batch learning. Incremental learning can also be combined with ensemble techniques to improve detection accuracy and robustness.

Efficient Feature Engineering- In real-time phishing detection systems, reducing computational overhead and maintaining low-latency processing through efficient feature extraction and selection is essential. This objective may be accomplished by implementing numerous methodologies like the Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), two-dimensionality reduction techniques that minimize the number of features while preserving the most informative ones [11]. Another efficient method for feature extraction is feature hashing. It entails converting categorical features into numerical values through hash functions, allowing faster processing without a substantial loss of information. Other additional techniques to identify the most pertinent features for phishing detection include Recursive Feature Elimination (RFE) and Genetic Algorithms, which can iteratively assess and choose features based on their impact on the model’s performance [12].

Handling Imbalanced Data- The dataset can be balanced by either increasing the number of phishing samples or reducing the number of legitimate samples using oversampling and under sampling techniques. The Synthetic Minority Over-Sampling Technique (SMOTE) is a widely used oversampling technique that generates synthetic samples for the minority class to balance the dataset [13]. Utilizing anomaly detection techniques can also effectively identify uncommon instances of phishing attempts. Anomaly detection algorithms, such as Isolation Forest and One-Class SVM, are specifically designed to identify outliers in data, rendering them appropriate for detecting phishing attempts in imbalanced datasets [14-17]. Additionally, the model can be trained to prioritize the detection of phishing attempts over legitimate activities by setting higher costs for false negatives (missed phishing attempts).

Conclusion

By implementing these innovative solutions, organizations can significantly improve their real-time phishing detection capabilities, protecting their digital assets and maintaining stakeholder trust. In addition to the above solutions, combining several models in an ensemble approach can also help improve the accuracy and dependability of real-time phishing detection systems. Machine learning and heuristic-based hybrid models can help to strike a compromise between accuracy and detection speed.

References

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Cite As

Rahaman M, Pappachan P (2024) Real-Time Phishing Detection: Challenges and Solutions in Streaming Data, Insights2Techinfo, pp.1

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