Intrusion Detection System Techniques Research Articles

Deep Learning-Based Intrusion Detection Systems For Network Security in IoT System

The Internet of Things (IoT) has revolutionised various sectors, including healthcare, education, agriculture, and military applications, by enabling seamless communication and data collection among interconnected devices. However, IoT networks' open and decentralised nature exposes them to many security threats and vulnerabilities. Intrusion Detection Systems (IDS) have been developed to address these challenges by identifying and mitigating malicious activities targeting these networks. Despite their importance, many organisations struggle to detect and prevent novel and sophisticated attacks effectively. This paper presents a comprehensive survey of the security issues inherent in IoT environments, emphasising the role of deep learning and machine learning techniques in enhancing IDS capabilities. By analysing existing vulnerabilities and evaluating various methodologies, we highlight the critical need for robust security measures that ensure IoT systems' reliability, privacy, and integrity. Through our findings, we advocate for integrating advanced analytical techniques in IDS to bolster defences against evolving threats in the IoT landscape.

A hybrid AI based framework for enhancing security in satellite based IoT networks using high performance computing architecture

IoT device security has become a major concern as a result of the rapid expansion of the Internet of Things (IoT) and the growing adoption of cloud computing for central monitoring and management. In order to provide centrally managed services each IoT device have to connect to their respective High-Performance Computing (HPC) clouds. The ever increasing deployment of Internet of Things (IoT) devices linked to HPC clouds use various medium such as wired and wireless. The security challenges increases further when these devices communicate over satellite links. This Satellite-Based IoT-HPC Cloud architecture poses new security concerns which exacerbates this problem. An intrusion detection technology integrated in the central cloud is suggested as a potential remedy to monitor and detect aberrant activity within the network in order to allay these worries. However, the enormous amounts of data generated by IoT devices and their constrained computing power dose not allow to implement IDS techniques at source and renders towards typical central Intrusion Detection Systems (IDS) ineffectiveness. Moreover, to protect these systems, powerful intrusion detection techniques are required due to the inherent vulnerabilities of IoT devices and the possible hazards during data transmission.During the course of literature survey it is revealed that the research work has been done to detect few types of attacks by using the old school model of IDS. The computational expensiveness in terms of processing time is also an important parameter to be considered. This work introduces a novel Embedded Hybrid Deep Learning-based intrusion detection technique (EHID) based on embedded hybrid deep learning that is created specifically for IoT devices linked to HPC clouds via satellite connectivity. Two Deep Learning (DL) algorithms are integrated in the proposed method to improve detection abilities with decent accuracy while considering the processing time and number of trainable parameters to detect 14 types of threats. It segregates among the normal and attack traffic. We also modify the conventional IDS approach and propose architectural change to harness the processing power of central server of cloud. This hybrid approach effectively detects threats by harnessing the computing power available at HPC cloud along with leveraging the power of AI. Additionally, the proposed system enables real-time monitoring and detection of intrusions while providing monitoring and management services through HPC using IoT-generated data. Experiments on Edge-IIoTset Cyber Security Dataset of IoT & IIoT indicate improved detection accuracy, reduced false positives, and efficient computational performance.

An Efficient Network Intrusion Detection and Classification System using Machine Learning

In today's digital landscape, network security is of paramount importance, with intrusion detection systems (IDS) playing a crucial role in protecting sensitive data from malicious attacks. Traditional IDS, often reliant on signature-based methods, struggle with high false positive rates, difficulty in adapting to novel threats, and significant computational demands. This paper explores the development of an efficient network intrusion detection and classification system utilizing machine learning techniques to address these challenges. By leveraging datasets such as NSL-KDD and UNSW-NB15, our study employs a combination of supervised learning algorithms, including Support Vector Machines (SVM), Random Forests, and Neural Networks, alongside comprehensive data preprocessing and feature engineering strategies. The evaluation of our models through metrics like accuracy, precision, recall, and ROC-AUC demonstrates a marked improvement in detection capabilities and computational efficiency. Our findings suggest that machine learning-based IDS can significantly enhance network security by reducing false positives and adapting to emerging threats more effectively than traditional systems. This research not only underscores the potential of advanced machine learning techniques in IDS but also provides a robust framework for future developments in the field. In the rapidly evolving landscape of cybersecurity, effective network intrusion detection and classification systems are critical for safeguarding sensitive data and maintaining operational integrity. This paper presents a novel approach utilizing machine learning techniques to enhance the efficiency and accuracy of intrusion detection systems (IDS). By employing a combination of supervised and unsupervised learning algorithms, our system can identify and classify both known and unknown threats in real-time. We leverage advanced feature selection methods to optimize the performance of our models, ensuring high detection rates with minimal false positives. Our experimental results, validated on benchmark datasets, demonstrate significant improvements in detection accuracy and processing speed compared to traditional IDS solutions. The proposed system not only strengthens network defenses but also provides a scalable and adaptive framework for future cybersecurity challenges..

Dynamically stabilized recurrent neural network optimized with intensified sand cat swarm optimization for intrusion detection in wireless sensor network

Wireless Sensor Networks (WSNs) are susceptible to various security threats owing to its deployment in hostile environments. Intrusion detection system (IDS) contributes a critical role on securing WSNs by identifying malevolent activities and ensuring data integrity. Traditional IDS techniques often struggle with the dynamic and resource-constrained nature of WSNs. In this paper, Dynamically Stabilized Recurrent Neural Network Optimized with Intensified Sand Cat Swarm Optimization for Wireless Sensor Network Intrusion identification (DSRNN-ISCOA-ID-WSN) is proposed. Initially, the input data is amassed from WSN-DS dataset. After that, the pre-processing segment receives the data. In pre-processing stage, redundant and biased records are removed from input data with the help of Adaptive multi-scale improved differential filter (AMSIDF). Then the optimal are selected by utilizing Wolf-Bird Optimization Algorithm (WBOA). DSRNN is used to classify the data as Normal, Grey hole, Black hole, Time division multiple access (TDMA), and Flooding attacks. Then Intensified Sand Cat Swarm Optimization (ISCOA) is employed to optimize the weight parameters of DSRNN for accuracte classification. The proposed DSRNN-ISCOA-ID-WSN technique is implemented Python. The performance of the proposed DSRNN-ISCOA-ID-WSN approach attains 29.24 %, 33.45 %, and 28.73 % high accuracy; 30.53 %, 27.64 %, and 26.25 % higher precision when compared with existing method such as Machine Learning-Powered Stochastic Gradient Descent Intrusions Detection System for WSN Attacks (SGDA-ID-WSN), An updated dataset to identify threats in WSN (CNN-ID-WSN) and Denial-of-Service attack detection in WSN: a Low-Complexity Machine Learning Model (DTA-ID-WSN) respectively.

HybGBS: A hybrid neural network and grey wolf optimizer for intrusion detection in a cloud computing environment

SummaryThe cloud computing environment is subject to unprecedented cyber‐attacks as its infrastructure and protocols may contain vulnerabilities and bugs. Among these, Distributed Denial of Service (DDoS) is chosen by most cyber extortionists, creating unusual traffic that drains cloud resources, making them inaccessible to customers and end users. Hence, security solutions to combat this attack are in high demand. The existing DDoS detection techniques in literature have many drawbacks, such as overfitting, delay in detection, low detection accuracy for attacks that target multiple victims, and high False Positive Rate (FPR). In this proposed study, an Artificial Neural Network (ANN) based hybrid GBS (Grey Wolf Optimizer (GWO) + Back Propagation Network (BPN) + Self Organizing Map (SOM)) Intrusion Detection System (IDS) is proposed for intrusion detection in the cloud computing environment. The base classifier, BPN, was chosen for our research after evaluating the performance of a comprehensive set of neural network algorithms on the standard benchmark UNSW‐NS 15 dataset. BPN intrusion detection performance is further enhanced by combining it with SOM and GWO. Hybrid Feature Selection (FS) is made using a correlation‐based approach and Stratified 10‐fold cross‐validation (STCV) ranking based on Weight matrix value (W). These selected features are further fine‐tuned using metaheuristic GWO hyperparameter tuning based on a fitness function. The proposed IDS technique is validated using the standard benchmark UNSW‐NS 15 dataset, which consists of 1,75,341 and 82,332 attack cases in the training and testing datasets. This study's findings demonstrate that the proposed ANN‐based hybrid GBS IDS model outperforms other existing IDS models with a higher intrusion detection accuracy of 99.40%, fewer false alarms (0.00389), less error rate (0.001), and faster prediction time (0.29 ns).

Enhancing IoT security: A comparative study of feature reduction techniques for intrusion detection system

Internet of Things (IoT) devices are extensively utilized but are susceptible to cyberattacks, posing significant security challenges. To mitigate these threats, machine learning techniques have been implemented for network intrusion detection in IoT environments. These techniques commonly employ various feature reduction methods, prior to inputting data into models, in order to enhance the efficiency of detection processes to meet real-time requirements. This study provides a comprehensive comparison of feature selection (FS) and feature extraction (FE) techniques for network intrusion detection systems (NIDS) in IoT environments, utilizing the TON-IoT and BoT-IoT datasets for both binary and multi-class classification tasks. We evaluated FS methods, including Pearson correlation and Chi-square, and FE methods, such as Principal Component Analysis (PCA) and Autoencoders (AE), across five classic machine learning models: Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), k-Nearest Neighbors (kNN), and Multi-Layer Perceptron (MLP). Our analysis revealed that FE techniques generally achieve higher accuracy and robustness compared to FS methods, with RF paired with AE delivering superior performance despite higher computational demands. DTs are most effective with smaller feature sets, while MLPs excel with larger sets. Chi-square is identified as the most efficient FS method, balancing performance and computational efficiency, whereas PCA outperforms AE in runtime efficiency. The study also highlights that FE methods are more effective for complex datasets and less sensitive to feature set size, whereas FS methods show significant performance improvements with more informative features. Despite the higher computational costs of FE methods, they demonstrate a greater capability to detect diverse attack types, making them particularly suitable for complex IoT environments. These findings are crucial for both academic research and industry applications, providing insights into optimizing detection performance and computational efficiency in NIDS for IoT networks.

Analysis of Machine Learning Methods for Intrusion Detection Systems in Wireless Networks

Wireless networks have become integral to modern communication systems, making them vulnerable to various security threats. Intrusion detection systems (IDSs) are essential for detecting and mitigating these threats and can also powerfully screen network traffic for pernicious activities that are planned to abuse the classification, honesty, realness, and accessibility of the network. Machine learning (ML) and Deep learning (DL) techniques are effective in identifying and classifying network attacks. This study proposes a novel intrusion detection system that employs ML and DL models to classify and distinguish network attacks in wireless networks. The proposed system enhanced detection accuracy and efficiency in IDS, the scalability of IDS systems, and estimated the performance of IDS in wireless networks. It also investigates IDS techniques using machine learning, designs and implements IDS in wireless networks using machine learning, and trains several IDS models regarding wireless networks that are fitted. It contrasts the exhibition of proposed models and existing procedures. The suggested system can therefore be utilized as an effective IDS for wireless networks, providing real-time detection and classification of network attacks.

XAI-IDS: Toward Proposing an Explainable Artificial Intelligence Framework for Enhancing Network Intrusion Detection Systems

The exponential growth of network intrusions necessitates the development of advanced artificial intelligence (AI) techniques for intrusion detection systems (IDSs). However, the reliance on AI for IDSs presents several challenges, including the performance variability of different AI models and the opacity of their decision-making processes, hindering comprehension by human security analysts. In response, we propose an end-to-end explainable AI (XAI) framework tailored to enhance the interpretability of AI models in network intrusion detection tasks. Our framework commences with benchmarking seven black-box AI models across three real-world network intrusion datasets, each characterized by distinct features and challenges. Subsequently, we leverage various XAI models to generate both local and global explanations, shedding light on the underlying rationale behind the AI models’ decisions. Furthermore, we employ feature extraction techniques to discern crucial model-specific and intrusion-specific features, aiding in understanding the discriminative factors influencing the detection outcomes. Additionally, our framework identifies overlapping and significant features that impact multiple AI models, providing insights into common patterns across different detection approaches. Notably, we demonstrate that the computational overhead incurred by generating XAI explanations is minimal for most AI models, ensuring practical applicability in real-time scenarios. By offering multi-faceted explanations, our framework equips security analysts with actionable insights to make informed decisions for threat detection and mitigation. To facilitate widespread adoption and further research, we have made our source code publicly available, serving as a foundational XAI framework for IDSs within the research community.

Advancements in Machine Learning Techniques for Intrusion Detection Systems: An Overview of Perspectives and Datasets

Advancements in Machine Learning Techniques for Intrusion Detection Systems: An Overview of Perspectives and Datasets

Exploring Lightweight Deep Learning Techniques for Intrusion Detection Systems in IoT Networks: A Survey

The proliferation of Internet users has coincided with a commensurate increase in the amount of very important, sensitive, and private information being transferred across the Internet. Malicious actors are increasingly targeting networks to breach them and obtain illegal access to critical information since this trend has revealed holes in security systems. In addition to endangering the privacy of the data concerned, these breaches disrupt the smooth functioning of systems. Therefore, in light of these dangers, intrusion detection systems (IDSs) are now an essential part of any cybersecurity program. The goal of these systems is to detect and report any suspicious activity by constantly monitoring and analyzing network traffic. Numerous review articles have investigated various methods for network intrusion detection. To improve detection accuracy while keeping computing efficiency high, this survey study investigates lightweight deep learning techniques for intrusion detection systems. These techniques include pruning, quantization, clustering, and collaborative optimization. This study analyzes five different types of new real-world traffic datasets (i.e., CSE-CIC- IDS2018, NSL-KDD, Bot-IoT, ToN IoT Network, and UNSW-NB15) and evaluates the performance of several machine learning and deep learning techniques. This survey provides metrics for measuring the accuracy of intrusion detection across various systems, which may be used to assess performance.

A Study of Role and Applicability of Data Mining Techniques in Intrusion Detection Systems

Abstract: The swift development of computers technologies changed the way data and information were kept. The risk of this data being exposed to uninvited and unauthorized users arises with this new paradigm of data access. Numerous systems have been created that examine data to look for deviations from a user's or system's typical behavior or look for a known signature in the data. Intrusion Detection Systems (IDS) is the name given to these systems. These systems use a variety of approaches, including machine learning algorithms and statistical methodologies. With the massive rise in the use of network-based services and information sharing on networks, network security has emerged as the fundamental component. The integrity, confidentiality, and availability of computer and network resources are all seriously compromised by intrusion, which also poses a severe risk to network security. Network audit data classification by humans is a costly, time-consuming, and laborious task. An intrusion detection system (IDS) is one tool used to find anomalies and attacks on a network. The network intrusion detection system has made extensive use of data mining techniques to extract valuable information from vast amounts of network data. This work proposes a hybrid model that combines two distinct intrusion detection techniques: anomaly-based and signaturebased. The model is separated into two stages. Systems for detecting intrusions make use of audit data produced by network devices, operating systems, and application software. These sources generate enormous databases that contain tens of millions of records. Data mining, which is the process of extracting meaningful patterns from a sizable amount of information, is used to analyze this data. The presented paper deals with the role and the applicability of data mining techniques in designing and developing the IDS Systems.

Machine Learning-Based Intrusion Detection on Multi-Class Imbalanced Dataset Using SMOTE

The rapid development of information technology has brought numerous benefits to society, but it has also led to increased security vulnerabilities in network systems. Intrusion detection systems (IDS) play a crucial role in identifying malicious activities, but they face challenges due to imbalanced datasets where the number of attack samples outweighs normal activities. This paper explores the performance of an IDS using SMOTE (Synthetic Minority Over-sampling Technique) and various classification algorithms to address imbalanced datasets and enhance detection of multi-class intrusions. Related works in the field of intrusion detection are reviewed, highlighting the effectiveness of different algorithms and techniques. The proposed work presents a model that combines SMOTE with log normalization and feature selection to improve IDS performance. Experiments are conducted on the NSL-KDD and CIC-IDS2017 datasets, evaluating different oversampling configurations and machine learning models. The results show that applying SMOTE improves overall performance, with high accuracy, precision, recall, and F1-score. Feature selection has minimal impact on model performance, suggesting the presence of redundant features. The study concludes that SMOTE effectively addresses class imbalance and enhances IDS performance, emphasizing the importance of incorporating oversampling techniques in intrusion detection systems.

Evaluated bird swarm optimization based on deep belief network (EBSO-DBN) classification technique for IOT network intrusion detection

Because of the recent development of various intrusion detection systems (IDS), which defend computer networks from security as well as privacy threats. The confidentiality, integrity and also availability of data may be compromised in the case that IDS prevention efforts fail. The amount of private, delicate and crucial data travelling over the worldwide network has expanded tremendously as a result of the recent development of Internet of Things (IoT) devices. Developing a better edge-based feature selection strategy, a deep learning technique for identifying and blocking malicious traffic, is the goal of intrusion detection. The classification method Evaluated Bird Swarm Optimization based Deep Belief Network (EBSO-DBN) has shown to be the most successful in this study. A variation of performance criteria have been used to critically assess deep learning techniques for IDS (accuracy, precision, recall, f-1 score, false alarm rate and detection rate). To ascertain the optimal performance of IDS models, this study focuses on building an ensemble classifier utilizing the suggested EBSO-DBN classification algorithm with 98.7% of accuracy, 99.4% of precision and 98.8% of recall.

Review on Clustering and Classification techniques in Intrusion Detection Systems

In the modern cyber world, the proportion of security threats is cumulating every day, and many researchers and security specialists are focusing on IDS (Intrusion Detection Systems) and the patterns in recognizing the alerts / events to detect and prevent them. The researchers and security specialists believe that the IDS is best way to protect the network and information assets. Hence this paper is focused on various threats and potential of IDS with its patterns in detecting the alerts / events. Basically, IDS has three different styles in detection the threats: Signature-based Detection (SD), Anomaly-based Detection (AD), and Stateful Protocol Analysis (SPA). The main area where the researchers and security specialists focusing is on techniques and algorithms used for clustering and classification. This paper mainly supports in understanding and analysing the various patterns on clustering and classifying the previous alerts / events which mainly supports in detection of threats with accuracy. This review will help to increase the detection accuracy of the IDS by enhancing the clustering and classification techniques which supports efficient execution of IDS over the network

Deep Learning Towards Intrusion Detection System (IDS): Applications, Challenges and Opportunities

With the growth of numerous technological areas, including sensors, embedded computing, broadband Internet access, wireless communications, distributed services, automatic identification, and tracking, the potential for integrating smart objects into our daily activities through the Internet has increased. The Internet of Things (IoT) is the confluence of the Internet and intelligent objects that can converse and cooperate with one another. IoT is a brand-new example that unifies Cyberspace with actual physical objects from various areas, including, business processes, human health, home automation, and environmental monitoring. It intensifies the use of Internet-connected strategies in our regular lives, carrying with it several advantages as well as security challenges. Intrusion Detection Systems (IDS) have been a crucial device for the defence of systems and material schemes for more than 20 years. However, applying traditional IDS techniques was challenging due to the IoT’s inimitable features, like resource-constrained devices and particular protocol stacks and standards. As a result, this survey will focus on various Deep Learning (DL)-based intrusion detection techniques. This study makes use of 50 research papers that focused on different techniques, and a review of studies that used those techniques was given. This research enables categorizing the methods employed for intrusion detection in IoT based on Convolutional Neural Network (CNN)-based methods, Deep Neural Network (DNN)-based methods, Optimization-based methods, and so on. Moreover, the categorization of approaches, published year, the dataset used, tools used, and the performance metrics are measured for intrusion detection in IoT. On the basis of the software used for implementation, performance achievement, and other factors, a thorough analysis was conducted. The conclusion identifies the research gaps and issues in a way that makes it clear why should create an efficient method for enabling efficient enhancement.

A review of deep learning based intrusion detection systems

Network security is a key issue in the era of rapid Internet development and new technologies are needed to enhance the security protection of network systems. As the first line of defence for the security of network systems, IDS is considered to be one of the important network tools for managing network security. Traditional network intrusion detection methods usually use machine learning techniques. However, deep learning, a more powerful machine learning method, has the advantages of mature technology, wide applicability and high accuracy, and has shown its advantages in various fields such as image recognition, natural language processing and computer vision. It can also be applied to network security detection, as it can effectively handle large-scale, complex datas and detect unknown, sophisticated network attacks. This paper will provide a summary of machine learning techniques and deep learning techniques in intrusion detection systems, as well as examples of IDSs using deep learning, and conclude with a summary and outlook.

AI-enabled intrusion detection systems in IoT networks: Advancing defense mechanisms for resource-constrained devices

With the rapid expansion of Internet of Things (IoT) networks comes an urgent need for advanced security mechanisms to combat ever more sophisticated cyber threats. Although intrusion detection systems (IDS) are vital, more is needed to detect IoT intrusion in resource-limited IoT devices with constrained processing capabilities and memory. In this paper, we discuss how Artificial Intelligence (AI) can be embedded into IDS frameworks to improve the security of IoT networks. Using available resources, machine learning, and deep learning techniques in AI-enabled IDS can enhance detection and mitigate intrusions. The existing methodologies are reviewed comprehensively, implementation challenges are assessed, and potential future research directions are discussed in this study. The conclusions show that AI-based IDS can significantly enable a more secure and resilient IoT ecosystem, provide innovative defense strategies, and open up opportunities for next-generation network security solutions.

A new two-phase intrusion detection system with Naïve Bayes machine learning for data classification and elliptic envelop method for anomaly detection

Technology is pivotal in the rapid growth of services and intensifying the quality of life. Recent technology, like the Internet of Things (IoT), demonstrates an impressive performance in fast-forward development. Intrusion Detection System (IDS) is used as a lifeline to prevent attacks by classifying the activities as normal and suspicious. In this paper, we propose a two-phase IDS for IoT. In the first phase, we categorize data into four sections according to the data types (i.e., nominal, integer, binary, and float). We then classify them using different versions of the Naive Bayes classifier. After that, we use majority voting to choose the final result of the classification. In the second phase, we pass those data which behave normally or are benign in the first phase and classify them using an unsupervised elliptic envelope. We validated our work using the standard NSL-KDD, UNSW_NB15, and CIC-IDS2017 datasets. We found the proposed method more efficient than existing IDS techniques and achieved reasonable accuracy in the first phase. Furthermore, the benign data is sent to the second phase of the analysis. After the second phase, we achieved a 97% accuracy in the NSL-KDD dataset, 86.9% in the UNSW_NB15 dataset, and 98.59% accuracy in the CIC-IDS2017 dataset.

An ensemble deep learning based IDS for IoT using Lambda architecture

The Internet of Things (IoT) has revolutionized our world today by providing greater levels of accessibility, connectivity and ease to our everyday lives. It enables massive amounts of data to be traversed across multiple heterogeneous devices that are all interconnected. This phenomenon makes IoT networks vulnerable to various network attacks and intrusions. Building an Intrusion Detection System (IDS) for IoT networks is challenging as they enable a massive amount of data to be aggregated, which is difficult to handle and analyze in real time mainly because of the heterogeneous nature of IoT devices. This inefficient, traditional IDS approach accentuates the need to develop advanced IDS techniques by employing Machine or Deep Learning. This paper presents a deep ensemble-based IDS using Lambda architecture by following a multi-pronged classification approach. Binary classification uses Long Short Term Memory (LSTM) to differentiate between malicious and benign traffic, while the multi-class classifier uses an ensemble of LSTM, Convolutional Neural Network and Artificial Neural Network classifiers to detect the type of attacks. The model training is performed in the batch layer, while real-time evaluation is carried out through model inferences in the speed layer of the Lambda architecture. The proposed approach gives high accuracy of over 99.93% and saves useful processing time due to the multi-pronged classification strategy and using the lambda architecture.

Analysis on Intrusion Detection Systems to Secure IoT Networks

The amalgamation of physical objects and the internet constitutes the Internet of things(IoT). In the subsequent times, more such physical objects will be connected to the internet and this will lead to the escalation in the attacks carried out on IoT devices and network. This is mainly due to the less memory and lack in built-in security controls. This has made IoT Security, the most researched topic in the past decade. Thus to protect IoT nodes and networks, a security mechanism called Intrusion Detection System(IDS) is brought in use.The advent of Internet of things has played a crucial role in making the lives of the people more easier and comfortable. It has improved the lives of the people in terms of several aspects like comfort and efficiency. This has overall consequently lead to what is called smart environments. When the case of real world smart environment is discussed, both security and privacy comes into play in being the key issues to be considered. But the need demands for the Intrusion Detection Systems (IDSs) to be designed for the IoT networks or environments to mitigate attack related issues. These are the attacks that exploits the vulnerabilities present in the network. The work here demonstrates the need of the fact that despite having previous technologies, we still lack a secure system that could safeguard IoT networks. In this paper, a detailed analysis is provided on IoT, security attacks in IoT, IDS, types of IDS, Machine learning and Deep learning techniques and algorithms used with IDS in IoT and current trends and research on use of IDS in securing Internet of Things is discussed. Lastly future research based on the current analysis is also examined. Keywords: IoT, IDS, Network, Security, Machine Learning, Deep Learning