Bus Ticketing Online System
Abstract
The proliferation of web-based applications and
services has led to a significant rise in the
frequency and sophistication of cyberattacks. From
SQL injections to cross-site scripting (XSS) and
denial-of-service (DoS) attacks, these threats can
cause serious disruptions and data breaches. The
need for intelligent, scalable, and accurate detection
systems is more urgent than ever. This project aims
to conduct a comprehensive performance analysis
of various Machine Learning (ML) and Deep
Learning (DL) algorithms to detect web-based
attacks effectively. By leveraging well-established
datasets and evaluation metrics, we assess each
algorithm’s capacity to identify different categories
of web threats. The models explored include both
classical ML algorithms—such as Support Vector
Machines (SVM), Decision Trees, and Random
Forests—and advanced DL models like
Convolutional Neural Networks (CNNs) and Long
Short-Term Memory (LSTM) networks. Each of
these is evaluated under consistent data and
performance metrics.
Through comparative analysis, this research seeks
to recommend the most efficient models for realtime
implementation, balancing performance,
scalability, and interpretability. The insights gained
will contribute to the design of more robust
intrusion detection systems for modern web
infrastructure.
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References
Yin, C., Zhu, Y., Fei, J., & He, X.
(2017). *A Deep Learning Approach for
Intrusion Detection Using Recurrent
Neural Networks*. IEEE Access, 5,
21954–21961.
5. Javaid, A., Niyaz, Q., Sun, W., & Alam,
M. (2016). *A Deep Learning Approach
for Network Intrusion Detection System*.
EAI International Conference on Bioinspired
Information and Communications
Technologies.
6. Ahmad, I., Basheri, M., Iqbal, M. J., &
Rahim, A. (2018). *Performance
Comparison of Support Vector Machine,
Random Forest, and Extreme Learning
Machine for Intrusion Detection*. IEEE
Access, 6, 33789–33795.
7. Vinayakumar, R., Soman, K. P., &
Poornachandran, P. (2017). *Applying
Convolutional Neural Network for
Network Intrusion Detection*.
International Conference on Advances in
Computing, Communications and
Informatics (ICACCI).
8. Sharafaldin, I., Lashkari, A. H., &
Ghorbani, A. A. (2018). *Toward
Generating a New Intrusion Detection
Dataset and Intrusion Traffic
Characterization*. Proceedings of ICISSP.
9. Berman, D. S., Buczak, A. L., Chavis, J.
S., & Corbett, C. L. (2019). *A Survey of
Deep Learning Methods for Cyber
Security*. Information, 10(4), 122.
10. Kwon, D., Kim, J., & Kim, J. (2019).
*Deep Learning-Based Anomaly
Detection System for Discovering Web
Attacks*. IEEE Access, 7, 183527–
183536.
11. Goodfellow, I., Bengio, Y., &
Courville, A. (2016). *Deep Learning*.
MIT Press.
12. Lippmann, R. P., Haines, J. W., Fried,
D. J., Korba, J., & Das, K. (2000). *The
1999 DARPA Off-Line Intrusion
Detection Evaluation*. Computer
Networks, 34(4), 579–595.
13. Kim, Y., Kim, W., & Kim, H. K.
(2020). *A Novel Hybrid Intrusion
Detection Method Integrating Anomaly
Detection with Misuse Detection*. Expert
Systems with Applications, 186, 115002.
14. Sommer, R., & Paxson, V. (2010).
*Outside the Closed World: On Using
Machine Learning for Network Intrusion
Detection*. IEEE Symposium on Security
and Privacy.
15. Khan, L., Awad, M., &
Thuraisingham, B. (2007). *A New
Intrusion Detection System Using Support
Vector Machines and Hierarchical
Clustering*. The VLDB Journal, 16(4),
507–521.
