Random Forest Stratified K-Fold Cross Validation on SYN DoS Attack SD-IoV
- URL: http://arxiv.org/abs/2509.07016v1
- Date: Sun, 07 Sep 2025 00:18:30 GMT
- Title: Random Forest Stratified K-Fold Cross Validation on SYN DoS Attack SD-IoV
- Authors: Muhammad Arif Hakimi Zamrai, Kamaludin Mohd Yusof,
- Abstract summary: TCP SYN flood attacks are prevalent within the context of Software-Defined Internet of Vehicles (SD-IoV)<n>This research focuses on optimizing a Random Forest model to achieve maximum accuracy and minimal detection time.<n>It provides a robust solution against TCP SYN flood attacks while maintaining network efficiency and reliability.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In response to the prevalent concern of TCP SYN flood attacks within the context of Software-Defined Internet of Vehicles (SD-IoV), this study addresses the significant challenge of network security in rapidly evolving vehicular communication systems. This research focuses on optimizing a Random Forest Classifier model to achieve maximum accuracy and minimal detection time, thereby enhancing vehicular network security. The methodology involves preprocessing a dataset containing SYN attack instances, employing feature scaling and label encoding techniques, and applying Stratified K-Fold cross-validation to target key metrics such as accuracy, precision, recall, and F1-score. This research achieved an average value of 0.999998 for all metrics with a SYN DoS attack detection time of 0.24 seconds. Results show that the fine-tuned Random Forest model, configured with 20 estimators and a depth of 10, effectively differentiates between normal and malicious traffic with high accuracy and minimal detection time, which is crucial for SD-IoV networks. This approach marks a significant advancement and introduces a state-of-the-art algorithm in detecting SYN flood attacks, combining high accuracy with minimal detection time. It contributes to vehicular network security by providing a robust solution against TCP SYN flood attacks while maintaining network efficiency and reliability.
Related papers
- Scalable Hierarchical AI-Blockchain Framework for Real-Time Anomaly Detection in Large-Scale Autonomous Vehicle Networks [0.5505634045241287]
Existing security schemes are unable to provide sub-10 ms anomaly detection and distributed coordination of large-scale networks of vehicles.<n>This paper introduces a three-tier hybrid security architecture HAVEN, which decouples real-time local threat detection and distributed coordination operations.<n>It incorporates a light ensemble anomaly detection model on the edge, Byzantine-fault-tolerant federated learning to aggregate threat intelligence at a regional scale, and selected blockchain mechanisms to ensure critical security coordination.
arXiv Detail & Related papers (2025-11-16T15:30:46Z) - Temporal-Spatial Attention Network (TSAN) for DoS Attack Detection in Network Traffic [0.0]
We propose a novel Temporal-Spatial Attention Network (TSAN) architecture for detecting Denial of Service (DoS) attacks in network traffic.<n>By leveraging both temporal and spatial features of network traffic, our approach captures complex traffic patterns and anomalies that traditional methods might miss.<n> Experimental results on the NSL-KDD dataset demonstrate that TSAN outperforms state-of-the-art models.
arXiv Detail & Related papers (2025-03-20T11:31:45Z) - Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection [14.728689487990836]
generative active adaptation framework minimizes labeling effort while enhancing model robustness.<n>We evaluate our end-to-end framework NetGuard on both simulated IDS data and a real-world ISP dataset.
arXiv Detail & Related papers (2025-03-04T21:49:42Z) - Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models [7.136205674624813]
The rapid expansion of Internet of Things (IoT) devices has increased the risk of cyber-attacks.<n>This work introduces a novel approach combining Self-Organizing Maps (SOMs), Deep Belief Networks (DBNs), and Autoencoders to detect known and previously unseen attack patterns.
arXiv Detail & Related papers (2025-02-17T06:01:06Z) - Correlating sparse sensing for large-scale traffic speed estimation: A
Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging.
We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
arXiv Detail & Related papers (2022-10-21T07:25:57Z) - Time-to-Green predictions for fully-actuated signal control systems with
supervised learning [56.66331540599836]
This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data.
We utilize state-of-the-art machine learning models to predict future signal phases' duration.
Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
arXiv Detail & Related papers (2022-08-24T07:50:43Z) - ARLIF-IDS -- Attention augmented Real-Time Isolation Forest Intrusion
Detection System [0.0]
Internet of Things and Software Defined Networking leverage lightweight strategies for the early detection of DDoS attacks.
It is essential to have a fast and effective security identification model based on low number of features.
In this work, a novel Attention-based Isolation Forest Intrusion Detection System is proposed.
arXiv Detail & Related papers (2022-04-20T18:40:23Z) - Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge
Computing: A Contextual-Bandit Approach [81.5261621619557]
We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC)
We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer.
Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
arXiv Detail & Related papers (2021-08-09T08:45:47Z) - Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy.
These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem.
We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
arXiv Detail & Related papers (2020-12-10T23:28:36Z) - Enabling certification of verification-agnostic networks via
memory-efficient semidefinite programming [97.40955121478716]
We propose a first-order dual SDP algorithm that requires memory only linear in the total number of network activations.
We significantly improve L-inf verified robust accuracy from 1% to 88% and 6% to 40% respectively.
We also demonstrate tight verification of a quadratic stability specification for the decoder of a variational autoencoder.
arXiv Detail & Related papers (2020-10-22T12:32:29Z) - Bayesian Optimization with Machine Learning Algorithms Towards Anomaly
Detection [66.05992706105224]
In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique.
The performance of the considered algorithms is evaluated using the ISCX 2012 dataset.
Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
arXiv Detail & Related papers (2020-08-05T19:29:35Z) - Resolution Adaptive Networks for Efficient Inference [53.04907454606711]
We propose a novel Resolution Adaptive Network (RANet), which is inspired by the intuition that low-resolution representations are sufficient for classifying "easy" inputs.
In RANet, the input images are first routed to a lightweight sub-network that efficiently extracts low-resolution representations.
High-resolution paths in the network maintain the capability to recognize the "hard" samples.
arXiv Detail & Related papers (2020-03-16T16:54:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.