Detecting Network Intrusion in Cloud Environment Through Ensemble Learning and Feature Selection Approach

Dynamic Multiple object detection is significantly a difficult task in object detection. In this paper, we’ve done object detection in which there is a single object in the whole image. The CNN network has taken an image and has given the output of two things (i.e.) The class or category of the objects like a dog, person, etc. And the second is the bounding box coordinates and the detection of multiple objects in a single frame, the existing methods do not perform well. An R-CNN is a region-based convolutional network it has achieved success in region-based feature extraction, and the part filters in a DPM it is a deformable part-based model that is very suitable for detecting hidden objects. 

A Vibrant Multiple Object Detection Using Machine Learning Techniques

Current Intrusion Detection Systems (IDSs), which rely on signature-based detection using a set of rules formed by the past inspection of traffic flows and their signature, fail to detect new threats and malware. In this paper, we advocate for a hybrid algorithm combining signature and deep learning, dubbed signature, and deep analysis-based intrusion detection (SDAID) algorithm. In particular, the SDAID algorithm is constituted by an ensemble learning model of Deep Neural Network and Extreme Gradient Boosting. In addition, we also handle the im-balanced training dataset to improve the prediction performance of SDAID. To validate the feasibility of the proposed SDAID, well-known datasets, including CSE-CIC-IDS2018 and NSL-KDD datasets, are employed to conduct rigorous experiments. The results indicate that the proposed deep learning analysis achieves an excellent F1-score of 99.93% and 99.62% with the CSE-CIC-IDS2018 and NSL-KDD datasets. It also performs better than related models using the same datasets.

SDAID: Towards a Hybrid Signature and Deep Analysis-based Intrusion Detection Method

Researchers are looking into solutions to support the enormous demand for wireless communication, which has been exponentially increasing along with the growth of technology. The sixth generation (6G) Network emerged as the leading solution for satisfying the requirements placed on the telecommunications system. 6G technology mainly depends on various machine learning and artificial intelligence techniques. The performance of these machine learning algorithms is high. Still, their security has been neglected for some reason, which leaves the door open to various vulnerabilities that attackers can exploit to compromise systems. Therefore, it is essential to evaluate the security of machine learning algorithms to prevent them from being spoofed by malicious hackers. Prior research has shown that the decision tree is one of the most popular algorithms used by 80% of researchers for classification problems. In this work, we collect the dataset from a laboratory testbed of over 100 Internet of things (IoT) devices. The devices include smart cameras, smart light bulbs, Alexa, and others. We evaluate classifiers using the original dataset during the experiment and record a 98% accuracy. We then use the label-flipping attack approach to poison our dataset and record the output. As a result, flipping 10%, 20%, 30%, 40%, and 50% of the poison data generated accuracies of 86%, 74%, 64%, 54%, and 50%, respectively. 

Adversarial Machine Learning Attacks in Internet of Things Systems

A Review Paper on Network Intrusion Detection System

Intrusion Detection System (IDS) is one of the most important security tool for many security issues that are prevailing in today's cyber world. Intrusion Detection System is designed to scan the system applications and network traffic to detect suspicious activities and issue an alert if it is discovered. So many techniques are available in machine learning for intrusion detection. The main objective of this project is to apply machine learning algorithms to the data set and to compare and evaluate their performances. The proposed application has used the SVM (Support Vector Machine) and ANN (Artificial Neural Networks) Algorithms to detect the intrusion rates. Each algorithm is used to detect whether the requested data is authorized or contains any anomalies. While IDS scans the requested data if it finds any malicious information it drops that request. These algorithms have used Correlation-Based and Chi-Squared Based feature selection algorithms to reduce the dataset by eliminating the useless data. The preprocessed dataset is trained and tested with the models to obtain the prominent results, which leads to increasing the prediction accuracy. The NSL KDD dataset has been used for the experimentation. Finally, an accuracy of about 48% has been achieved by the SVM algorithm and 97% has been achieved by ANN algorithm. Henceforth, ANN model is working better than the SVM on this dataset.

Intrusion Detection System Framework Using Machine Learning

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Intrusion Detection System Framework Using Machine Learning