Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel machine learning techniques for intrusion detection and mitigation based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods is evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior detection and localization performance of the proposed methods by 78% in the best case and 27% in the worst case, while achieving the same level of false alarm probability.
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Artificial Intelligence Techniques for SQL Injection Attack Detection
In recent years, web-based platforms and business applications
have been rising in popularity deeming themselves indispensable
as they constitute the main backbone of business processes and in-
formation sharing. However, the unprecedented increased number
of cyber-attacks have been threatening their day-to-day opera-
tions. In particular, the Standard Query Language Injection Attack
(SQLIA) remains one of the most prevalent cyber attacks targeting
web-based applications. As a consequence, the SQLIA detection
techniques need to be constantly revamped and stay up-to-date in
order to achieve the full potential of mitigating such threats. In this
paper, we propose an artificial intelligence model based on super-
vised machine learning techniques to detect SQLIA. As part of the
proposed model, we introduce an input string validation technique
as a primary anomaly identifier using pattern matching for SQL
Query data with anomalies-injections. To evaluate our approach we
injected one type of SQLIA that is tautology attacks and measured
the performance of our model. We used three main classifiers in
our model and our findings indicate a model prediction accuracy of
98.3605% for Support Vector Machine (SVM), 96.296% for K-Nearest
Neighbors (KNN), and 97.530% for Random Forest. The approach
proposed in this paper has the potential of being used to integrate
an automated SQL Injection detection mechanism with Intrusion
Detection Systems (IDS) and Intrusion Protection Systems (IPS).
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- Award ID(s):
- 2011689
- PAR ID:
- 10433659
- Date Published:
- Journal Name:
- Proceedings of the 2023 8th International Conference on Intelligent Information Technology
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3591569.3591576
