Intrusion Detection System for IoE-Based Medical Networks

The capacity to identify breaches and malicious activity inside the Internet of Things (IoT) networks is important for network infrastructure resilience as the dependence on IoT devices and services grows. Intrusion detection systems (IDS) are basic components of network security. IDSs monitor and analyze the activity of a system in a network to identify intrusions. Existing intrusion detection systems (IDS) gather and utilize large amounts of data with irrelevant, unnecessary, and unsuitable characteristics, resulting in long detection times and low accuracy. In this paper, we present an IDS model based on a Random Forest (RF) classifier. NSL-KDD dataset is used to test the performance of the model and the satisfying performance is obtained in terms of accuracy, detection rate, and false alarm rate. The proposed model has attained an average accuracy of 99.3% and 98% for binary classification and multiclass classification, respectively. To demonstrate the efficacy of the suggested model, its accuracy was compared with some existing approaches that utilize other models such as AIDS, ELM and PCA, MapReduce-based hybrid architecture, and DRNN.

With the development of internet of things (IoT), capabilities of computing, networking infrastructure, storage of data and management have come very close to the edge of networks. This has accelerated the necessity of Fog computing paradigm. Due to availability of Internet, most of our business operations are integrated with IoT platform. Fog computing has enhanced the strategy of collecting and processing, huge amount of data. On the other hand, attacks and malicious activities has adverse consequences on the development of IoT, Fog, and cloud computing. This has led to development of many security models using fog computing to protect IoT network. Therefore, for dynamic and highly scalable IoT environment, a distributed architecture based intrusion detection system (IDS) is required that can distribute the existing centralized computing to local fog nodes and can efficiently detect modern IoT attacks. This paper proposes a novel distributed ensemble design based IDS using Fog computing, which combines k-nearest neighbors, XGBoost, and Gaussian naive Bayes as first-level individual learners. At second-level, the prediction results obtained from first level is used by Random Forest for final classification. Most of the existing proposals are tested using KDD99 or NSL-KDD dataset. However, these datasets are obsolete and lack modern IoT-based attacks. In this paper, UNSW-NB15 and actual IoT-based dataset namely, DS2OS are used for verifying the effectiveness of the proposed system. The experimental result revealed that the proposed distributed IDS with UNSW-NB15 can achieve higher detection rate upto 71.18% for Backdoor, 68.98% for Analysis, 92.25% for Reconnaissance and 85.42% for DoS attacks. Similarly, with DS2OS dataset, detection rate is upto 99.99% for most of the attack vectors.

WOGRU-IDS — An intelligent intrusion detection system for IoT assisted Wireless Sensor Networks

Wide adoption of Internet of Things (IoT) devices and applications encounters security vulnerabilities as roadblocks. The heterogeneous nature of IoT systems prevents common benchmarks, such as the NSL-KDD dataset, from being used to test and verify the performance of different Network Intrusion Detection Systems (NIDS). In order to bridge this gap, in this paper, we examine specific attacks in the NSL-KDD dataset that can impact sensor nodes and networks in IoT settings. Furthermore, in order to detect the introduced attacks, we study eleven machine learning algorithms and report the results. Through numerical analysis, we show that tree-based methods and ensemble methods outperform the rest of the studied machine learning methods. Among the supervised algorithms, XGBoost ranks the first with 97% accuracy, 90.5% Matthews correlation coefficient (MCC), and 99.6% Area Under the Curve (AUC) performance. Moreover, a notable research finding of this study is that the Expectation-Maximization (EM) algorithm, which is an unsupervised method, also performs reasonably well in the detection of the attacks in the NSL-KDD dataset and outperforms the accuracy of the Naive Bayes classifier by 22.0%.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios.

Recently, the internet of things (IoT) has evolved into a breakthrough for creating intelligent settings. Any technology's reliance on the IoT model is seen as having major security and privacy issues. The various conceivable attacks carried out by intruders give rise to privacy and security considerations. Therefore, creating an intrusion detection system is crucial for identifying attacks and anomalies in the IoT system. In this work, a deep belief network (DBN) algorithm model for the intrusion detection system has been proposed. The CICIDS 2017 dataset is used for the performance analysis of the current IDS model in terms of assaults and anomaly detection. Accuracy, recall, precision, F1-score, detection rate, and other characteristics were all improved by the proposed method. IoT technology has revolutionized how healthcare is provided to patients. Medical institutions are very concerned about IoT security because of the network enabled IoT devices' integration with healthcare network infrastructure.

One of the most basic characteristic features of every smart device in a network based on the Internet of Things (IoT) is to gather a larger set of data that has been created and then transfer the gathered data to the destination/receiver server through the internet. Thus, IoT-based networks are most vulnerable to simple or complex attacks that need to be identified in the early stage of data transmission for saving the network from these malicious attacks. The chief goal of the proposed work is to design and form the intelligent intrusion detection system (I-IDS) using the machine learning models such that the attacks can be identified in the IoT network. The model is built considering the normal and malicious attacks on the data that are generated in IoT smart environment. To simulate such a model, a testbed is built where a wireless router, a DHT11 sensor, and a node MCU are being used during the design phase. An attacker or adversarial system is built to perform poisoning and sniffing attacks using a laptop system. The node captures the sensor values and transmits the data to the ThinkSpeak platform, during the normal phase via the wireless gateway, and in the attack phase, the malicious attacker interprets the data, modifies it while transmitting from node to the ThinkSpeak server. Thus, the attack called Man-In-The-Middle (MITM) is performed and classified as abnormal data. Various machine learning algorithms are performed on the data, and finally, the results obtained using a probabilistic model called as Markov model have a high performance evaluated based on the I-IDS IoT network. The results obtained during the experimental analysis show that the Markov model has obtained a 100% detection rate and 19% of false alarm rate (FAR) with high precision and low error rate. The algorithms such as naïve Bayes classifier, support vector machine (SVM), decision tree, and Adaboost are considered in comparison with the Markov model. The optimal solution is obtained concerning other evaluation metrics like sensitivity, F1, and true-positive rate (TPR). Therefore, the integrated network of IoT-WSN with its performance metrics is tabulated to show the potentials of securing a network system. Additionally, the proposed work gives a high level of security for IoT smart environment as compared with the other machine learning algorithms using the novel technique of intelligent IDS.

Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and health systems and services. IoMT has successfully addressed many old healthcare problems. But it comes with its drawbacks essentially with patient’s information privacy and security related issues that comes from IoMT architecture. Using obsolete systems can bring security vulnerabilities and draw attacker’s attention emphasizing the need for effective solution to secure and protect the data traffic in IoMT network. Recently, intrusion detection system (IDS) is regarded as an essential security solution for protecting IoMT network. In the past decades, machines learning (ML) algorithms have demonstrated breakthrough results in the field of intrusion detection. Notwithstanding, to our knowledge, there is no work that investigates the power of machines learning algorithms for intrusion detection in IoMT network. This paper aims to fill this gap of knowledge investigating the application of different ML algorithms for intrusion detection in IoMT network. The investigation analysis includes ML algorithms such as K-nearest neighbor, Naïve Bayes, support vector machine, artificial neural network and decision tree. The benchmark dataset, Bot-IoT which is publicly available with comprehensive set of attacks was used to train and test the effectiveness of all ML models considered for investigation. Also, we used comprehensive set of evaluation metrics to compare the power of ML algorithms with regard to their detection accuracy for intrusion in IoMT networks. The outcome of the analysis provides a promising path to identify the best the machine learning approach can be used for building effective IDS that can safeguard IoMT network against malicious activities.

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

In recent years, developed Intrusion Detection Systems (IDSs) perform a vital function in improving security and anomaly detection. The effectiveness of deep learning-based methods has been proven in extracting better features and more accurate classification than other methods. In this paper, a feature extraction with convolutional neural network on Internet of Things (IoT) called FECNNIoT is designed and implemented to better detect anomalies on the IoT. Also, a binary multi-objective enhance of the Gorilla troops optimizer called BMEGTO is developed for effective feature selection. Finally, the combination of FECNNIoT and BMEGTO and KNN algorithm-based classification technique has led to the presentation of a hybrid method called CNN-BMEGTO-KNN. In the next step, the proposed model is implemented on two benchmark data sets, NSL-KDD and TON-IoT and tested regarding the accuracy, precision, recall, and F1-score criteria. The proposed CNN-BMEGTO-KNN model has reached 99.99% and 99.86% accuracy on TON-IoT and NSL-KDD datasets, respectively. In addition, the proposed BMEGTO method can identify about 27% and 25% of the effective features of the NSL-KDD and TON-IoT datasets, respectively.

The substantial growth of Internet-connected devices within the Internet of Things (IoT) has given rise to significant security challenges. Among the various threats confronting these interconnected devices, Denial of Service (DoS)/Distributed Denial of Service (DDoS) attacks emerge as significant concerns. The attacks, which seek to disrupt IoT services by flooding networks with unnecessary traffic, there is a critical need for robust security measures. Intrusion Detection Systems (IDS) are vital in identifying suspicious activities, yet many existing systems lack real-time capabilities to address evolving attack strategies. This study investigates the vulnerabilities of IoT networks and the pressing need to detect and classify DoS/DDoS attacks in real time. Traditional IDS, while effective in recognizing known attack patterns, fall short in identifying new attack types due to their reliance on historical data. To bridge this gap, this research focuses on developing an enhanced hybrid network intrusion detection and classification system. This study seeks to make contribution to the advancement of resilient security measures within IoT environments. The framework uses the Kth Nearest Neighbor (KNN) algorithm and dense neural networks to efficiently detect and categorize DoS/DDoS attacks in real time. To achieve this, a simulation model implementing the proposed hybrid algorithm will be developed. The framework utilized the MQTT-IoT-IDS2020 dataset, and a comparison was presented with recent approaches found in the literature. The results demonstrated enhanced performance in terms of true positive rate, accuracy, speed of detection.

The integration of IDS and Internet of Things (IoT) with deep learning plays a significant role in safety. Security has a strong role to play. Application of the IoT network decreases the time complexity and resources. In the traditional intrusion detection systems (IDS), this research work implements the cutting-edge methodologies in the IoT environment. This research is based on analysis, conception, testing and execution. Detection of intrusions can be performed by using the advanced deep learning system and multiagent. The NSL-KDD dataset is used to test the IoT system. The IoT system is used to test the IoT system. In order to detect attacks from intruders of transport layer, efficiency result rely on advanced deep learning idea. In order to increase the system performance, multi -agent algorithms could be employed to train communications agencies and to optimize the feedback training process. Advanced deep learning techniques such as CNN will be researched to boost system performance. The testing part an IoT includes data simulator which will be used to generate in continuous of research work finding with deep learning algorithms of suitable IDS in IoT network environment of current scenario without time complexity.

The Internet of Things (IoT) ecosystem consists of interconnected devices that work together. It facilitates communication between devices, real-time data exchange and cloud computing. The occurrence risk of cyber attacks grows exponentially with these interconnected systems. Devices, networks and data could easily come under attack, wherefore they become vulnerable and could be compromised by hackers. To address this problem, we propose an enhanced Framework used on Software Defined Network (SDN) environment-based Intrusion Detection System (IDS) for securing IoT devices from malicious activity. We implement the machine learning (ML) method as part of the SDN controller's Network Intrusion Detection System (NIDS). Our enhanced ML-based SDN Controller Framework (Improved ML-SDN) classify the data traffic and makes a real-time prediction. It is based on K-Nearest Neighbor (kNN) supervised learning algorithm with others improving model Accuracy. It has produced an accuracy of 99.7%, 0.02304 s of building model time, 0.2997 s of detection time and a false alarm rate (FAR) of 0.34%.

GDLC: A new Graph Deep Learning framework based on centrality measures for intrusion detection in IoT networks

The Internet of Things (IoT) is an interconnected framework of computer devices, software, and sensors to work collectively to automate and monitor different tasks and procedures without human intervention. IoTs provide benefits and ease in performing operations for various industries and home and health stakeholders. Still, it faces security threats and challenges that make the IoT framework unreliable regarding security and privacy. Intrusion detection systems have been designed and deployed to overcome these security challenges. Over the past few years, for making the IDS intelligent artificial intelligence has been integrated with them. Besides this, different feature selection techniques are also being used to make the datasets appropriate for Machine Learning (ML) classifiers. This research uses a filter-based feature selection technique to select the important features from two datasets, UNSW-NB15 and NSL-KDD. After that, datasets with selected features are given to the classifiers as input. This work also evaluates different ML classifiers such as Multiple Layers Perceptron (MLP), CatBoost, Light Gradient Boosting (LGBM), Extra-Tree Classifier (ETC), Random Forest (RF), and K-nearest neighbor (KNN). Different metrics, Accuracy, Precision, Recall, F1, Detection Rate, and FAR, have been used for performance analysis. Evaluation results proved the LGBM classifier effective among other classifiers with 98.70% accuracy on the UNSW-NB15 dataset and 99.8% on the NSL-KDD dataset after feature selection with mean absolute difference (MAD) feature selection technique. Results are also compared with existing models to provide the efficiency of the proposed model.