International Journal of Advanced Networking and Applications

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Enhancing Cybercrime Deterrence with Artificial Intelligence


Affiliations
1 Computer Science Department, The University of Jordan, Jordan
2 Artificial Intelligence Department, The University of Jordan, Jordan
 

Web phishing poses a significant security challenge for web users owing to three primary factors. First, it is easy to implement and does not require profound technical expertise in programming or networking. Second, it can be executed across various platforms, including the web, SMS, and social media platforms. Finally, this type of attack relies on social engineering, meaning that users' responses are influenced by the content presented to them. Over the past few decades, there has been a proliferation of methods and services designed for phishing detection. In this study, we introduced a novel approach to web phishing detection based on a hybrid weighted machine learning framework. Our method harnesses the capabilities of four distinct machine learning algorithms, including an unsupervised approach (K-means) and three supervised techniques. The outputs of these algorithms were strategically weighted to produce a final decision. To train and evaluate our proposed algorithm, we employed a vast dataset encompassing no content web features, totaling 111 distinct attributes. The correlations between these features and the classification outcomes were leveraged to streamline the feature set, and various correlation values were explored. Our findings from the training and validation phases underscore the significance of the correlation between the chosen features in determining the accuracy of the algorithm. In summary, our research introduces an innovative approach to combat web phishing, showcasing the potential of hybrid machine learning techniques and the critical role of feature selection through correlation analysis to enhance detection accuracy. The accuracy outcomes of the various algorithms exhibited a range of values, ranging from 0.6561 to 0.8833, across different correlation thresholds when considering all features.

Keywords

Web Phishing, Cybersecurity, Machine Learning, Detection Methods, Cyber Threats, Security Challenges.
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  • Adam M. Bossler & Tamar Berenblum. (2019) “Introduction: new directions in cybercrime research” Journal of Crime and Justice, 495-499, Doi: 10.1080/0735648X.2019.1692426.

  • Arun Kulkarni, Leonard L. Brown, (2019), Phishing websites detection using machine learning, International Journal of Advanced Computer Science and Applications, 10(7).

  • Ashit Kumar Dutta. (2021) “Detecting phishing websites using machine learning technique” PLoS ONE, doi: 10.1371/ journal. pone.0258361.

  • Aslam Khan, Rahul Sharma. (2018)” A Survey Paper on Detection of Phishing Website by URL Technique” International Journal of Computer Science and Mobile Applications, 6 (4), 33-37.

  • Ishant Tyagi, Jatin Shad; Shubham Sharma; Siddharth Gaur; Gagandeep Kaur. (2018) “A novel Machine Learning Approach to Detect Phishing Websites" 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), 425-430.

  • Justin Ma, Lawrence K. Saul, Stefan Savage, Geoffrey M. Voelker. (2011) “Learning to detect malicious URLs” ACM Transactions on Intelligent Systems and Technology, 2 (3), Article No.: 30, 1–24.

  • Khana Faisal Md, Rana B. L. (2021) “Detection of Phishing Websites Using Deep Learning Techniques” Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(10), 3880-3892.

  • Maddie Rosenthal. (2022) “Must-Know Phishing Statistics” [Online]: available at: https://www.tessian.com/blog/phishing-statistics- 2020/

  • Peng yang,Guangzhen ahaus, and pungent .(2019) “Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning” IEEE Access, 7, 15196-15209.

  • Ping Yi, Yuxiang Guan, Futai Zou, Yao Yao, Wei Wang, and Ting Zhu. (2018) “Web phishing detection using a deep learning framework” Wireless Communications and Mobile Computing, Article ID 4678746, doi:10.1155/2018/4678746.

  • Rishikesh Mahajan, Irfan Siddavatam. (2018) ‘Phishing Website Detection using Machine Learning Algorithms” International Journal of Computer Applications (0975 – 8887), 181(23).

  • Routhu Srinivasa Rao & Alwyn Roshan Pais. (2019) “Detection of phishing websites using an efficient feature-based machine learning framework” Neural Comput & Applic 31, 3851–3873.

  • Rundong Yang , Kangfeng Zheng , Bin Wu , Chunhua Wu , Xiujuan Wang. (2021) “Phishing Website Detection Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning” Sensors 2021.

  • Vrbančič, G., Fister Jr, I., & Podgorelec, V. (2020) “Datasets for phishing websites detection” Data in Brief, 33, 106438.

  • Yussra M. AL-Shareef, Hesham Abusaimeh. (2020) “How to Detect Phishing Website Using Three Model Ensemble Classification” Computer Science Department, Faculty of Information Technology, Middle East University.

  • A.K. Patel, Jaypalsinh A. Gohil, D.T. Meva, (2010), Proposed Methodology for Securing Wireless LANs from Wormhole Attack, Int. J. of Advanced Networking and Applications 342 Volume: 01, Issue: 06, Pages:342-346.

  • Sg, Hymlin & R, Thilothi & Banu.N, Rubiya & A, Sandhiya & Sumithra,. (2021). Detection Of Jamming Attack Using IEWMA In Clustered Wireless Sensor Network. International Journal of Advanced Networking and Applications. 12. 4691-4696.

  • P.Mohana Priya, Abhijit Ranganathan, , (2022), Cyber Awareness Learning Imitation Environment (CALIE): A Card Game to provide Cyber Security Awareness for Various Group of Practitioners, Int. J. Advanced Networking and Applications Volume: 14 Issue: 02 Pages: 5318-5328.

  • Hung Le, Quang Pham, Doyen Sahoo, Steven C.H. Hoi. (2018) “URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection” arXiv, Computer Science, Cryptography and Security, arXiv:1802.03162.

  • Heba Harahsheh, Mohammad Shraideh, Saleh Sharaeh, (2021), PERFORMANCE OF Malware Detection Classifier Using Genetic Programming in Feature Selection, Informatica, 45(4).

  • Heba Harahsheh, Mohammad Alshraideh, Saleh Al- Sharaeh & Rizik Al-Sayyed (2023) Improving Classification Performance for Malware Detection Using Genetic Programming Feature Selection Techniques, Journal of Applied Security Research, 18:3, 627-647.

  • Methaq Kadhum, Enas Rawashdeh, Mohammad Alshraideh, (2021), An Efficient Bug Reports Assignment for IOT Application with Auto-Tuning Structure of ELM Using Dragonfly Optimizer, Journal of Hunan University Natural Sciences, 48(7).


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  • Enhancing Cybercrime Deterrence with Artificial Intelligence

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Authors

Muyad Mahmmoud ALkharabsheh
Computer Science Department, The University of Jordan, Jordan
Mohammad Alshraideh
Artificial Intelligence Department, The University of Jordan, Jordan
Imad Salah
Computer Science Department, The University of Jordan, Jordan

Abstract


Web phishing poses a significant security challenge for web users owing to three primary factors. First, it is easy to implement and does not require profound technical expertise in programming or networking. Second, it can be executed across various platforms, including the web, SMS, and social media platforms. Finally, this type of attack relies on social engineering, meaning that users' responses are influenced by the content presented to them. Over the past few decades, there has been a proliferation of methods and services designed for phishing detection. In this study, we introduced a novel approach to web phishing detection based on a hybrid weighted machine learning framework. Our method harnesses the capabilities of four distinct machine learning algorithms, including an unsupervised approach (K-means) and three supervised techniques. The outputs of these algorithms were strategically weighted to produce a final decision. To train and evaluate our proposed algorithm, we employed a vast dataset encompassing no content web features, totaling 111 distinct attributes. The correlations between these features and the classification outcomes were leveraged to streamline the feature set, and various correlation values were explored. Our findings from the training and validation phases underscore the significance of the correlation between the chosen features in determining the accuracy of the algorithm. In summary, our research introduces an innovative approach to combat web phishing, showcasing the potential of hybrid machine learning techniques and the critical role of feature selection through correlation analysis to enhance detection accuracy. The accuracy outcomes of the various algorithms exhibited a range of values, ranging from 0.6561 to 0.8833, across different correlation thresholds when considering all features.

Keywords


Web Phishing, Cybersecurity, Machine Learning, Detection Methods, Cyber Threats, Security Challenges.

References