This paper concentrate on an option for mitigating distributed denial of service (DDoS) attacks that can stern consequences in mobile ad hoc network (MANET). Discovering a solution to a DDoS attack has gained research focus but challenges exists in performing attack detection with high accuracy along with developing a mechanism in detecting diverse methods to classify DDoS attack activities and also to classify it as an effective measure. The existing methods have numerous difficulties involving detection system performance limits, system scalability and stability, and the capability to develop large volumes of information. This paper concentrates on ETC with randomized search algorithm to detect attacks categorized as flooding, scheduling, black holes and gray holes, using a machine learning (ML) technique as classifier for understanding the behavior of these attacks and trains the better classification method in the MANET data transmitting dataset. The ETC algorithm employs the traditional top-down construction method to construct an ensemble of unpruned decision or regression trees. It separates nodes by selecting cut points thresholds completely at random, which sets it apart from previous tree-based ensemble approaches. When the data transmitted in the AODV, the behavior of node is analyzed and reported in the dataset as target which is trained through ML method. This AODV with ML proposed model can justify the behavior of network in MANET and classify the attack type for the current application. Moreover, the ML method performance has been developed through hyperparameter tuning which can be evaluated through confusion matrix metrics. This AODV with extra tree classifier (ETC) generate improved accuracy as 98.89% using hyperparameter tuning process in determining the safe data transaction in MANET.

本文重点讨论缓解分布式拒绝服务 (DDoS) 攻击的选项，这种攻击可能对移动自组织网络 (MANET) 造成严重后果。发现 DDoS 攻击的解决方案已成为研究重点，但高精度执行攻击检测以及开发一种检测多种方法以对 DDoS 攻击活动进行分类并将其分类为有效措施的机制存在挑战。现有方法存在许多困难，涉及检测系统性能限制、系统可扩展性和稳定性以及开发大量信息的能力。本文重点关注 ETC，采用随机搜索算法来检测分类为泛洪、调度、黑洞和灰洞的攻击，使用机器学习 (ML) 技术作为分类器来理解这些攻击的行为，并在 MANET 中训练更好的分类方法数据传输数据集。ETC算法采用传统的自上而下的构建方法来构建未剪枝的决策树或回归树的集合。它通过完全随机选择切点阈值来分离节点，这使其与之前基于树的集成方法不同。当数据在 AODV 中传输时，节点的行为被分析并报告在数据集中作为通过 ML 方法训练的目标。这种采用 ML 的 AODV 模型可以证明 MANET 中网络的行为合理性，并对当前应用的攻击类型进行分类。此外，机器学习方法的性能是通过超参数调整来开发的，可以通过混淆矩阵指标进行评估。该 AODV 具有额外的树分类器 (ETC)，使用超参数调整过程确定 MANET 中的安全数据交易，准确率提高到 98.89%。