SHAKE-ESDRL-based energy efficient intrusion detection and hashing system

IF 1.8 4区 计算机科学 Q3 TELECOMMUNICATIONS Annals of Telecommunications Pub Date : 2023-05-31 DOI:10.1007/s12243-023-00963-w
Geo Francis E, S. Sheeja
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Abstract

Outstanding progress in unsolicited intrusions along with security threats, which interrupt the normal operations of wireless sensor networks (WSNs), have been attracted by the proliferation of WSNs and their applications. In WSNs, this demands an intrusion detection system (IDS), which can detect such attacks with higher detection accuracy. Designing an effective model for IDS using the SDK-LSHB-based SHAKE-ESDRL algorithm to improve accuracy and lessen training time and response time is the goal of this work. At first, duplicate removal, missing data removal, and data transfer are the steps through which the dataset was processed. From the processed data, by providing the extracted attributes as input to the entropy-based generalized discriminant analysis (E-GDA) method, the number of attributes is reduced. After that, the LogSwish-based deep reinforcement learning algorithm (LS-DRLA) method wielded the reduced attributes for intrusion detection (ID). By utilizing the SHAKE 256 algorithm, the attributes that fall into the attacked class label are hashed and stored in the hash table during this process. Next, to test the real-time data with the trained IDS, the WSN nodes are initialized. For this, by utilizing the supremum distance (SD-K-Means) algorithm, the sensor nodes (SNs) are clustered centered on the cluster heads (CHs) selected by the linear scaling-based honey badger optimization algorithm (LS-HBOA) method. At last, utilizing real-world-based datasets, the proposed algorithms are evaluated and the results are compared using statistical metrics.

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基于 SHAKE-ESDRL 的高能效入侵检测和散列系统
随着无线传感器网络(WSN)及其应用的激增,主动入侵及其安全威胁取得了显著进展,这些威胁干扰了无线传感器网络(WSN)的正常运行。在 WSN 中,这就要求入侵检测系统(IDS)能以更高的检测精度检测出此类攻击。使用基于 SDK-LSHB 的 SHAKE-ESDRL 算法为 IDS 设计一个有效的模型,以提高准确率并减少训练时间和响应时间,是这项工作的目标。首先,对数据集进行重复删除、缺失数据删除和数据传输等处理。从处理过的数据中,通过将提取的属性作为基于熵的广义判别分析(E-GDA)方法的输入,减少了属性的数量。之后,基于 LogSwish 的深度强化学习算法(LS-DRLA)方法利用减少的属性进行入侵检测(ID)。在此过程中,通过使用 SHAKE 256 算法,对属于被攻击类别标签的属性进行哈希处理并存储在哈希表中。接下来,为了用训练有素的 IDS 测试实时数据,需要对 WSN 节点进行初始化。为此,利用超和距离(SD-K-Means)算法,以基于线性缩放的蜜獾优化算法(LS-HBOA)方法选出的簇头(CH)为中心,对传感器节点(SN)进行聚类。最后,利用基于真实世界的数据集对所提出的算法进行评估,并使用统计指标对结果进行比较。
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来源期刊
Annals of Telecommunications
Annals of Telecommunications 工程技术-电信学
CiteScore
5.20
自引率
5.30%
发文量
37
审稿时长
4.5 months
期刊介绍: Annals of Telecommunications is an international journal publishing original peer-reviewed papers in the field of telecommunications. It covers all the essential branches of modern telecommunications, ranging from digital communications to communication networks and the internet, to software, protocols and services, uses and economics. This large spectrum of topics accounts for the rapid convergence through telecommunications of the underlying technologies in computers, communications, content management towards the emergence of the information and knowledge society. As a consequence, the Journal provides a medium for exchanging research results and technological achievements accomplished by the European and international scientific community from academia and industry.
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