Improved Phishing Detection Algorithms using Adversarial Autoencoder Synthesized Data

Shirazi, Hossein; Muramudalige, Shashika R.; Ray, Indrakshi; Jayasumana, Anura P.

doi:10.1109/lcn48667.2020.9314775

Cited by 15 publications

(11 citation statements)

References 25 publications

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“…The additional enhancement will soon be needed since the development in hardware is slower than the improvement in DL computing power, which limits DL models' performance. Furthermore, complex DL models using GPUs and TPUs in their implementation have specific 2 Alexa https://www.alexa.com/ Amazon - [21], [33], [43], [45], [48], [77], [96], [97], [99], [101], [105] 3 DMOZ https://dmoz-odp.org/ https://dmoztools.net/docs/en/rdf.html Open Directory Project - [43], [58], [82], [88], [91], [93], [ effects on the environment and energy consumption. Carbon dioxide emitted from such models is approximately five times an average car's lifetime emission.…”

Section: ) Computational Constraintsmentioning

confidence: 99%

“…Designing and implementing a DL expert system to automatically generate knowledge from training data and overcome the lack of inner explanation in deep neural networks [99]. The refined rules are extracted from a trained neural network and then replaced with an expert system's knowledge base by combining these two methods in a hybrid model.…”

Section: ) Employing Explainable Neural Networkmentioning

confidence: 99%

“…The dataset used for the experiment in this study was obtained from University California Irvine Machine Learning Repository (UCI), which various authors have widely used in their research [83], [97], [99], [101]. The dataset consists of 11055 URLs, of which 6157 are legitimate, and 4898 are phishing.…”

Section: A Datasetmentioning

confidence: 99%

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Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Selamat

Krejcar

et al. 2022

IEEE Access

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Phishing has become an increasing concern and captured the attention of end-users as well as security experts. Despite decades of development and improvement, existing phishing detection techniques still suffer from the deficiency in performance accuracy and the inability to detect unknown attacks. Motivated to solve these problems, many researchers in the cybersecurity domain have shifted their attention to phishing detection that capitalizes on machine learning techniques. In recent years, deep learning has emerged as a branch of machine learning that has become a promising solution for phishing detection. As a result, this study proposes a taxonomy of deep learning algorithms for phishing detection by examining 81 selected papers using a systematic literature review approach. The paper first introduces the concept of phishing and deep learning in the context of cybersecurity. Then, phishing detection and deep learning algorithm taxonomies are provided to classify the existing literature into various categories. Next, taking the proposed taxonomy as a baseline, this study comprehensively reviews the state-of-the-art deep learning techniques and analyzes their advantages as well as disadvantages. Subsequently, the paper discusses various issues deep learning faces in phishing detection and proposes future research directions to overcome these challenges. Finally, an empirical analysis is conducted to evaluate the performance of various deep learning techniques in a practical context and highlight the related issues that motivate researchers in their future works. The results obtained from the empirical experiment showed that the common issues among most of the state-of-the-art deep learning algorithms are manual parameter-tuning, long training time, and deficient detection accuracy.

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Section: ) Computational Constraintsmentioning

confidence: 99%

Section: ) Employing Explainable Neural Networkmentioning

confidence: 99%

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Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Selamat

Krejcar

et al. 2022

IEEE Access

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“…By leveraging the generator and discriminator, the GAN can be trained in an adversarial setting with multiple cost functions and weights. Generative networks has also been used for synthesizing and identifying malicious URLs from lexical [7,34,36] or character-level data [9,42]. The latter work involves training the generator to synthesize each URL character by sampling from a random noise vector, and the architecture is called unconditional GAN.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We further experiment on adversarial examples synthesized using our Generator from the above tests set, which we show in Table . 3. We use Accuracy (ACC), Sensitivity, Precision, F1-Score, and AUC 0.9135 0.9210 0.9073 0.9141 0.9135 SVM [49] 0.8638 0.8822 0.8508 0.8662 0.8638 Random Forest [36] 0.8688 0.8508 0.8825 0.8664 0.8688 CNN [46] 0.9251 0.9190 0.9303 0.9246 0.9251 LSTM [46] 0.9471 0.9522 0.9425 0.9473 0.9471 (area-under-the-curve) for metrics. We can see for the first experiment, except for the architectures given in [46], our model achieves the best score compared to other deep learning and machine learning derived architectures.…”

Section: Training Proceduresmentioning

confidence: 99%

Semi-supervised Conditional GAN for Simultaneous Generation and Detection of Phishing URLs: A Game theoretic Perspective

Kamran¹,

Sengupta²,

Tavakkoli³

2021

Preprint

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Spear Phishing is a type of cyber-attack where the attacker sends hyperlinks through email on well-researched targets. The objective is to obtain sensitive information such as name, credentials, credit card numbers, or other crucial data by imitating oneself as a trustworthy website. According to a recent report, phishing incidents nearly doubled in frequency in 2020. In recent times, machine learning techniques have become the standard for defending against such attacks. Many augmentations have been made for improving the existing architectures, such as Convolutional Networks, Recurrent Networks, and Generative Adversarial Networks. However, these architectures were designed with only defense in mind. Moreover, the attacker's perspective and motivation are absent while creating such training and deployment pipelines. To address this, we need a gametheoretic approach to understand the rational decision-making process of the attacker (Hacker) and the defender (Phishing URL detector). We propose a Conditional Generative Adversarial Network for real-time phishing URL detection. Additionally, we train our architecture in a semi-supervised manner to distinguish between adversarial and real examples, along with detecting malicious and benign URLs. We also design two games between the attacker and defender in training and deployment settings by utilizing the game-theoretic perspective. Our experiments confirm that the proposed architecture surpasses recent state-of-theart architectures for phishing URLs detection.

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An intelligent identification and classification system for malicious uniform resource locators (URLs)

Al‐Haija

Al-Fayoumi

2023

Neural Comput & Applic

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Uniform Resource Locator (URL) is a unique identifier composed of protocol and domain name used to locate and retrieve a resource on the Internet. Like any Internet service, URLs (also called websites) are vulnerable to compromise by attackers to develop Malicious URLs that can exploit/devastate the user’s information and resources. Malicious URLs are usually designed with the intention of promoting cyber-attacks such as spam, phishing, malware, and defacement. These websites usually require action on the user’s side and can reach users across emails, text messages, pop-ups, or devious advertisements. They have a potential impact that can reach, in some cases, to compromise the machine or network of the user, especially those arriving by email. Therefore, developing systems to detect malicious URLs is of great interest nowadays. This paper proposes a high-performance machine learning-based detection system to identify Malicious URLs. The proposed system provides two layers of detection. Firstly, we identify the URLs as either benign or malware using a binary classifier. Secondly, we classify the URL classes based on their feature into five classes: benign, spam, phishing, malware, and defacement. Specifically, we report on four ensemble learning approaches, viz. the ensemble of bagging trees (En_Bag) approach, the ensemble of k-nearest neighbor (En_kNN) approach, and the ensemble of boosted decision trees (En_Bos) approach, and the ensemble of subspace discriminator (En_Dsc) approach. The developed approaches have been evaluated on an inclusive and contemporary dataset for uniform resource locators (ISCX-URL2016). ISCX-URL2016 provides a lightweight dataset for detecting and categorizing malicious URLs according to their attack type and lexical analysis. Conventional machine learning evaluation measurements are used to evaluate the detection accuracy, precision, recall, F Score, and detection time. Our experiential assessment indicates that the ensemble of bagging trees (En_Bag) approach provides better performance rates than other ensemble methods. Alternatively, the ensemble of the k-nearest neighbor (En_kNN) approach provides the highest inference speed. We also contrast our En_Bag model with state-of-the-art solutions and show its superiority in binary classification and multi-classification with accuracy rates of 99.3% and 97.92%, respectively.

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Improved Phishing Detection Algorithms using Adversarial Autoencoder Synthesized Data

Cited by 15 publications

References 25 publications

Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Semi-supervised Conditional GAN for Simultaneous Generation and Detection of Phishing URLs: A Game theoretic Perspective

An intelligent identification and classification system for malicious uniform resource locators (URLs)

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