使用带有额外树分类器(ETC)的按需分布式距离向量(AODV)检测城域网中的分布式拒绝服务(DDoS)

IF 1.5 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
N. Sivanesan, A. Rajesh, S. Anitha, K. S. Archana
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引用次数: 0

摘要

本文集中探讨了一种缓解分布式拒绝服务(DDoS)攻击的方案,这种攻击会对移动特设网络(MANET)造成严重后果。发现 DDoS 攻击的解决方案已成为研究重点,但在高精度地进行攻击检测以及开发检测机制方面存在挑战,这些机制采用多种方法对 DDoS 攻击活动进行分类,并将其归类为有效措施。现有方法在检测系统性能限制、系统可扩展性和稳定性以及开发大量信息的能力方面存在诸多困难。本文集中研究了采用随机搜索算法的 ETC,以检测泛洪、调度、黑洞和灰洞等攻击,并使用机器学习(ML)技术作为分类器,以了解这些攻击的行为,并在城域网数据传输数据集中训练更好的分类方法。ETC 算法采用传统的自顶向下构建方法,构建未经剪枝的决策树或回归树集合。它通过完全随机地选择切点阈值来分离节点,这使它有别于以往基于树的集合方法。当数据在 AODV 中传输时,节点的行为会被分析并作为目标报告到数据集中,而数据集是通过 ML 方法训练出来的。这种带有 ML 的 AODV 模型可以证明城域网中的网络行为,并对当前应用的攻击类型进行分类。此外,ML 方法的性能已通过超参数调整得到开发,并可通过混淆矩阵指标进行评估。在确定城域网中的安全数据交易时,采用额外树分类器(ETC)的 AODV 在超参数调整过程中提高了 98.89% 的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Detecting Distributed Denial of Service (DDoS) in MANET Using Ad Hoc On-Demand Distance Vector (AODV) with Extra Tree Classifier (ETC)

Detecting Distributed Denial of Service (DDoS) in MANET Using Ad Hoc On-Demand Distance Vector (AODV) with Extra Tree Classifier (ETC)

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.

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来源期刊
CiteScore
5.50
自引率
4.20%
发文量
93
审稿时长
>12 weeks
期刊介绍: Transactions of Electrical Engineering is to foster the growth of scientific research in all branches of electrical engineering and its related grounds and to provide a medium by means of which the fruits of these researches may be brought to the attentionof the world’s scientific communities. The journal has the focus on the frontier topics in the theoretical, mathematical, numerical, experimental and scientific developments in electrical engineering as well as applications of established techniques to new domains in various electical engineering disciplines such as: Bio electric, Bio mechanics, Bio instrument, Microwaves, Wave Propagation, Communication Theory, Channel Estimation, radar & sonar system, Signal Processing, image processing, Artificial Neural Networks, Data Mining and Machine Learning, Fuzzy Logic and Systems, Fuzzy Control, Optimal & Robust ControlNavigation & Estimation Theory, Power Electronics & Drives, Power Generation & Management The editors will welcome papers from all professors and researchers from universities, research centers, organizations, companies and industries from all over the world in the hope that this will advance the scientific standards of the journal and provide a channel of communication between Iranian Scholars and their colleague in other parts of the world.
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