MODEL DETEKSI SERANGAN SSH-BRUTE FORCE BERDASARKAN DEEP BELIEF NETWORK

Deep Belief Networks are deep learning models that utilize stacks of Restricted Boltzmann Machines (RBM) or sometimes Autoencoders. Autoencoder is a neural network model that has the same input and output. The autoencoder learns the input data and attempts to reconstruct the input data. The solution in this study can provide several tests on DBN such as detecting recall accuracy and better classification precision. By using this algorithm, it is hoped that we as users can overcome problems that occur quite often such as brute force attacks in our accounts and within the company. And the results obtained from this DBN experiment are with an accuracy value of 90.27%, recall 90.27%, precession 91.67%, F1-score 90.51%. The results of this study are the data values of accuracy, recall, precession, and f1-score data used to detect brute force attacks are quite efficient using the deep model of the deep belief network. &nbsp

An Efficient Anomaly Detection Through Optimized Navigation Using Dlvq-Cdma And H-Dso In Healthcare Iot Environment

An Anomaly detection (AD) framework intends to discover irregular data and also unusable activities in a system. The abnormality in the healthcare information is picked up by the AD in the healthcare system and then, the outcome is updated for the authority to evaluate the data. Numerous researchers have developed an AD method that has the disadvantage of data loss issues and complexity in computation. An enhanced AD framework utilizing Deep Learning Vector Quantization-Correlation Distance Mayfly Algorithm (DLVQ-CDMA) and Hyper-sphere Dolphin Swarm Optimization (H-DSO) methodology is presented in this work to overcome these disadvantages. By aid of the Internet of Things (IoT)-connected systems, proffered model gathers information about the patient and as well forwards the information to patient's health care application. Information from health care application is then sent via the optimal path by utilizing the H-DSO method. The data is uploaded to the cloud server later and then, it is recovered and provided to the AD system. The data is then pre-processed in an AD system. After extricating the features, the feature reduction is performed by employing the Entropy-Generalized Discriminant Analysis(E-GDA) scheme. Subsequently, the DLVQ-CDMA algorithm is utilized with the required features. Information is formerly categorized as usual data or irregularity data. data, which is attacked is stored in the log file and the normal data will undergo further evaluation for the identification of the presence of disease or disorder. After evaluation, the outcome is communicated to the patient. The experiential analysis specifies that the proffered DLVQ-CDMA methodology executes better than the prevailing methodologies

Distributed Denial of Service Attack Detection in IoT Networks using Deep Learning and Feature Fusion: A Review

The explosive growth of Internet of Things (IoT) devices has led to escalating threats from distributed denial of service (DDoS) attacks. Moreover, the scale and heterogeneity of IoT environments pose unique security challenges, and intelligent solutions tailored for the IoT are needed to defend critical infrastructure. The deep learning technique shows great promise because automatic feature learning capabilities are well suited for the complex and high-dimensional data of IoT systems. Additionally, feature fusion approaches have gained traction in enhancing the performance of deep learning models by combining complementary feature sets extracted from multiple data sources. This paper aims to provide a comprehensive literature review focused specifically on deep learning techniques and feature fusion for DDoS attack detection in IoT networks. Studies employing diverse deep learning models and feature fusion techniques are analysed, highlighting key trends and developments in this crucial domain. This review provides several significant contributions, including an overview of various types of DDoS attacks, a comparison of existing surveys, and a thorough examination of recent applications of deep learning and feature fusion for detecting DDoS attacks in IoT networks. Importantly, it highlights the current challenges and limitations of these deep learning techniques based on the literature surveyed. This review concludes by suggesting promising areas for further research to enhance deep learning security solutions, which are specifically tailored to safeguarding the fast-growing IoT infrastructure against DDoS attacks