By: Gonipalli Bharath, Vel Tech University, Chennai, India, International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan; gonipallibharath@gmail.com
Abstract:
IoT changed the world of devices regarding their communication and interaction. However, this interconnected ecosystem opens up the possibilities for DDoS attacks, disrupting services and compromising security. Artificial Intelligence has promising solutions for the detection and mitigation of such attacks. This article explores various AI-based techniques for DDoS attack detection in IoT networks, including an overview of existing literature, methodologies employed, and a summary of findings in tabular format.
Introduction:
IoT devices find their applications in many areas, including smart homes, healthcare, and industrial automation. Still, due to the very limited computational capabilities of IoT devices, along with usually weak security measures, they have turned into the first target for a DDoS attack. In a DDoS attack, the target system is flooded with malicious traffic to make the services unavailable to genuine users [[1]]. These threats can be detected and mitigated using AI techniques like machine learning and deep learning in real time.
Literature Review:
Some research works have proposed AI-based DDoS attack detection in IoT systems on comprehensive review
Machine Learning Approaches:
Methods such as (SVM)Support Vector Machines, K- Nearest Neighbour (KNN) and Random Forests (RF) have been applied for classification of the network traffic to identify anomalies. For the detection of denial-of-service (DDoS) attacks, they have employed support vector machines (SVM), random forest algorithms (RF), and K-Nearest Neighbors (KNN). The SVM reveals a success rate with 99.5%, but the accuracy of KNN and RF is 97.5% and 98.74%, respectively. According to the comparison, the SVM performs more robustly than the RF, KNN, and cutting-edge machine learning (ML) and deep learning (DL) methods.[[2]]
Hybrid models:
This Hybrid models combining machine learning and deep learning with classical methods have ensured better efficiency in detection and reduced false positives. XAI-based technique to detect distributed denial of service attacks based on feature significance and primarily on unsupervised learning explanation because the supervised model lacks realistic attack data. [[3]]
In hybrid learning, any two methods—supervised deep learning or unsupervised deep learning—are combined to use the learning models. It highlights the benefits of both teaching methods to enhance the characteristics of the model’s performance. Other names for hybrid learning include deep ensemble learning and deep transfer learning. Hybrid learning can take several forms, such as CNN+LSTM, GAN+CNN, etc. Improving the models’ resilience, flexibility, and efficiency is the primary objective of hybrid learning [[4]].

Fig[[5]]
Methodology:
This work analyzes a standard AI-based DDoS detection pipeline for IoT networks:
- Data Collection:
The IoT device’s network traffic data is first collected and pre-processed to eliminate noise and irrelevant features.
- Feature Extraction:
Some important features, such as packet size, volume of traffic exchanged, and source IP addresses are extracted from the network traffic data to identify patterns associated with DDoS attacks.
- Training Models:
ML models, such as SVM, or DL models, such as RNN, are then trained over labelled datasets.
- Real-time Detection:
Trained models are deployed to observe live traffic and flag potential DDoS attacks.
- Evaluation:
Scores such as accuracy, precision, recall, and F1-score have been considered for model performance.
Table Representation:
AI Techniques | Key features | Advantages | Challenges |
Machine Learning (ML) | SVM, Random Forests | High accuracy, fast training | Requires labelled datasets |
Deep Learning | Recurrent Neural Network (RNN), Convolution Neural Network (CNN) | Handles complex patterns | High computational cost |
Hybrid Models | Combination of ML and DL | Improved detection efficiency | Increased system complexity |
Adopting AI based techniques [[6]]
Conclusion:
AI approaches offer effective solutions in detecting DDoS attacks in IoT systems. It is possible to detect attack patterns with high accuracy and respond in real time by leveraging ML and DL techniques. Future work may focus on improving model scalability and adapting to evolving attack methods.
References:
- Bala, Bindu, and Sunny Behal. “AI Techniques for IoT-Based DDoS Attack Detection: Taxonomies, Comprehensive Review and Research Challenges.” Computer Science Review 52 (May 1, 2024): 100631. https://doi.org/10.1016/j.cosrev.2024.100631.
- Kalutharage, Chathuranga Sampath, Xiaodong Liu, Christos Chrysoulas, Nikolaos Pitropakis, and Pavlos Papadopoulos. “Explainable AI-Based DDOS Attack Identification Method for IoT Networks.” Computers 12, no. 2 (February 2023): 32. https://doi.org/10.3390/computers12020032.
- Dahiya, A., & Gupta, B. B. (2021). A reputation score policy and Bayesian game theory based incentivized mechanism for DDoS attacks mitigation and cyber defense. Future Generation Computer Systems, 117, 193-204.
- Zhang, Z., Sun, R., Zhao, C., Wang, J., Chang, C. K., & Gupta, B. B. (2017). CyVOD: a novel trinity multimedia social network scheme. Multimedia Tools and Applications, 76, 18513-18529.
Cite As
Bharath G. (2025) Artificial Intelligence Approaches for IoT DDoS Attack Detection, Insights2Techinfo, pp. 1