Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model  

作　　者：R.Sheeba R.Sharmila Ahmed Alkhayyat Rami Q.Malik 

机构地区：[1]Department of Computer Science and Engineering,K.Ramakrishnan College of Engineering,Tiruchirappalli,621112,India [2]Department of Computer Applications,Dhanalakshmi Srinivasan Engineering College,Perambalur,621212,India [3]College of Technical Engineering,The Islamic University,Najaf,Iraq [4]Medical Instrumentation Techniques Engineering Department,Al-Mustaqbal University College,Babylon,Iraq

出　　处：《Computer Systems Science & Engineering》2023年第8期1415-1429,共15页计算机系统科学与工程（英文）

摘　　要：Lately,the Internet of Things(IoT)application requires millions of structured and unstructured data since it has numerous problems,such as data organization,production,and capturing.To address these shortcomings,big data analytics is the most superior technology that has to be adapted.Even though big data and IoT could make human life more convenient,those benefits come at the expense of security.To manage these kinds of threats,the intrusion detection system has been extensively applied to identify malicious network traffic,particularly once the preventive technique fails at the level of endpoint IoT devices.As cyberattacks targeting IoT have gradually become stealthy and more sophisticated,intrusion detection systems(IDS)must continually emerge to manage evolving security threats.This study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning(IDMBOA-DL)algorithm.In the presented IDMBOA-DL model,the Hadoop MapReduce tool is exploited for managing big data.The MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature subsets.Finally,the sine cosine algorithm(SCA)with convolutional autoencoder(CAE)mechanism is utilized to recognize and classify the intrusions in the IoT network.A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL algorithm.The comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches.

关 键 词：Big data analytics internet of things SECURITY intrusion detection deep learning 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]