Accurate Intrusion Detection Research Articles

FogNet: Custom CNN with optimal feature selection-based combat model for secured fog computing environment

In recent years, the increasing number of connected devices and the exponential growth of data generated by these devices have posed significant challenges for intrusion detection systems (IDS) in fog computing environments. However, conventional IDS failed to provide better attack detection performance in fog computing networks. So, this work proposes a custom convolutional neural network (CCNN) with optimal feature selection-based combat model for secured fog computing environment. Initially, preprocessing of the dataset aims to enhance the quality of the input data by removing attack labels, reducing dimensionality, and extracting relevant features. Then, comprehensive learning particle swarm-based effective seeker optimization (CLPS-ESO) is employed to extract features by leveraging a comprehensive learning strategy and the powerful optimization capabilities of effective seeker particle swarm optimization. To further enhance the feature selection performance, the random forest-dependent African buffalo optimization with weighted genetic algorithm, here after referred as RF-IABO-WGA, is utilized to identify the most discriminative features from the CLPS-ESO dataset. By selecting a subset of informative features, the computational complexity is reduced, and the detection accuracy is improved. Finally, the selected features are fed into a custom convolutional neural network (CCNN) classifier, which leverages the power of deep learning to accurately classify network traffic as either normal or malicious. The CCNN architecture can effectively learn complex patterns and capture the underlying characteristics of network data, enabling accurate intrusion detection in fog computing environments. Experimental results on a UNSW-NB dataset demonstrate that the proposed FogNet resulted in accuracy of 99.771878 %, precision of 99.770460 %, recall of 99.538589 %, and F1-score of 99.653512 %, which are superior performances compared to existing approaches.

A bizarre synthesized cascaded optimized predictor (BizSCOP) model for enhancing security in cloud systems

Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security.

An optimal secure defense mechanism for DDoS attack in IoT network using feature optimization and intrusion detection system

Now, the Cyber security is facing unprecedented difficulties as a result of the proliferation of smart devices in the Internet of Things (IoT) environment. The rapid growth in the number of Internet users over the past two decades has increased the need for cyber security. Users have provided new opportunities for attackers to do harm. Limited security budgets leave IoT devices vulnerable and easily hacked to launch distributed denial-of-service (DDoS) attacks, with disastrous results. Unfortunately, due to the unique nature of the Internet of Things environment, most security solutions and intrusion detection systems (IDS) cannot be directly adapted to the IoT with acceptable security performance and are vulnerable to various attacks that do not benefit. In this paper we propose an optimal secure defense mechanism for DDoS in IoT network using feature optimization and intrusion detection system (OSD-IDS). In OSD-IDS mechanism, first we introduce an enhanced ResNet architecture for feature extraction which extracts more deep features from given traffic traces. An improved quantum query optimization (IQQO) algorithm for is used feature selection to selects optimal best among multiple features which reduces the data dimensionality issues. The selected features have given to the detection and classification module to classify the traffic traces are affected by intrusion or not. For this, we design a fast and accurate intrusion detection mechanism, named as hybrid deep learning technique which combines convolutional neural network (CNN) and diagonal XG boosting (CNN-DigXG) for the fast and accurate intrusion detection in IoT network. Finally, we validate the performance of proposed technique by using different benchmark datasets are BoNeSi-SlowHTTPtest and CIC-DDoS2019. The simulation results of proposed IDS mechanism are compared with the existing state-of-art IDS mechanism and analyze the performance with respects to different statistical measures. The results show that the DDoS detection accuracy of proposed OSD-IDS mechanism is high as 99.476% and 99.078% for BoNeSi-SlowHTTPtest, CICDDoS2019, respectively.

CFS-AE: Correlation-based Feature Selection and Autoencoder for Improved Intrusion Detection System Performance

The major problem computer network users face concerning data – whether in storage, in transit, or being processed - is unauthorized access. This unauthorized access typically leads to the loss of confidentiality, integrity, and availability of data. Consequently, it is essential to implement an accurate Intrusion Detection System (IDS) for every information system. Many researchers have proposed machine learning and deep learning models, such as autoencoders, to enhance existing IDS. However, the accuracy of these models remains a significant research challenge. This paper proposes a Correlation-Based Feature Selection and Autoencoder (CFS-AE) to enhance detection accuracy and reduce the false alarms associated with the current anomaly-based IDS. The first step involves feature selection for the NSL-KDD and CIC-IDS2017 datasets which are used to train and test our model. Subsequently, an autoencoder is employed as a classifier to categorize data traffic into attack and normal categories. The results from our experimental study revealed an accuracy of 94.32% and 97.71% for the NSL-KDD and CIC-IDS2017 datasets, respectively. These results demonstrate improved performance over existing IDS systems.

A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models

The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters.

Weighted Feature Selection for Machine Learning Based Accurate Intrusion Detection in Communication Networks

Weighted Feature Selection for Machine Learning Based Accurate Intrusion Detection in Communication Networks

Artificial Intelligence based Intrusion Detection System – A Detailed Survey

The Internet and communications have rapidly expanded, leading to a significant rise in data generation and heterogeneity. Intrusion detection systems play a crucial role in ensuring the security and integrity of computer systems. These systems have been developed by researchers, academicians, and practitioners to effectively detect and mitigate network attacks. Intrusion detection systems are designed to analyze network traffic and compare it with a baseline of normal behavior, allowing them to identify any deviations or inconsistencies that may indicate an intrusion. Furthermore, the cooperative and distributed architecture of intrusion detection systems enables them to effectively detect attacks and protect the network from unauthorized access. Additionally, to enhance the performance of intrusion detection systems, techniques such as resampling the dataset and utilizing classifier ensemble are used to improve the classification accuracy. Moreover, intrusion detection systems have been integrated with intrusion response systems to ensure a timely and effective response to detected attacks. AI-based Intrusion Detection Systems have emerged as a crucial tool in ensuring network security and combating cyber threats. These systems utilize artificial intelligence algorithms to analyze network traffic, identify patterns of malicious activity, and detect potential cyber-attacks. They have proven to be highly effective in improving the detection accuracy, reducing false alarms, and even detecting previously unknown types of attacks. In summary, the development of accurate and efficient intrusion detection systems is crucial for ensuring network security. In today’s rapidly changing world, the significance of accurate intrusion detection systems cannot be overstated.

Extended Fuzzy Neutrosophic Classifier for Accurate Intrusion Detection and Classification

Intrusion Detection is crucial in contemporary cybersecurity landscapes to proactively thwart and identify possible threats. The risk of data breaches, malicious activities, and unauthorized access escalates as organizations increasingly rely on interconnected systems. Intrusion Detection Systems (IDS) are imperative for the continuous monitoring of system and network activities, quickly identifying patterns or anomalies indicative of cyber threats. IDS acts as a frontline defense mechanism with the ability to identify abnormal behaviors and known attack signatures. Prompt recognition allows for safeguarding sensitive data, timely response, fortifying the overall resilience of IT infrastructures, and reducing the effect of security incidents. The implementation of robust IDS is vital in an era marked by evolving cyber threats to ensure the confidentiality, availability, and integrity of digital assets. This study develops an improved Arithmetic Optimization Algorithm with an Extended Fuzzy Neutrosophic Classifier technique (AOA-EFNSC) for Accurate Intrusion Detection and Classification. The main goal of proposing this model is to recognize the presence of intrusions effectually. A min-max scalar is applied to normalize the input data before using the improved AOA as a feature selection method. For intrusion detection, the proposed model uses the FNSC technique for the recognition and classification of the intrusions. A sequence of experimentations was involved to validate the superior performance of the proposed model. The experimental value pointed out that our proposed approach outperforms the previous models and enhances the intrusion detection results.

A comparative study of cyber security intrusion detection in healthcare systems

Due to the proliferation of network devices and the presence of sensitive information, healthcare systems have become prime targets for cyber attackers. Therefore, it is crucial to design an efficient and accurate intrusion detection system (IDS) specifically tailored for healthcare systems. In this regard, we conducted a comprehensive comparative study on network security intrusion detection in healthcare systems. In order to tackle the challenges arising from information redundancy and noise in feature selection, we developed the Maximum Information Coefficient (MIC) method to effectively analyse the nonlinear relationships among traffic features. This method was utilized in a comparative analysis involving ten models on three datasets. The experiments demonstrated that the detection models using MIC-based feature selection outperformed other feature selection approaches, especially when applied to the WUSTL-EHMS-2020 dataset, which includes patients' biometric features. The MIC-enhanced Extreme Gradient Boosting detection model achieved remarkable results, attaining an accuracy of 95.01%, precision of 94.94%, and recall of 95.01%. These findings underscore the efficacy of our comparative study in safeguarding healthcare systems against cyber attacks. Furthermore, our study highlights the importance of feature selection and the incorporation of patient biometric features in healthcare IDS. It is imperative for medical managers to consider these factors when making informed decisions regarding cyber security measures.

TBGD: Deep Learning Methods on Network Intrusion Detection Using CICIDS2017 Dataset

With the development of science and technology, more and more personal information is uploaded to the Internet, which poses a serious threat to our personal and property security. As machine learning and deep learning techniques continue to develop, they become increasingly powerful at extracting data and improving the accuracy of classifying malicious traffic. This paper proposes an intrusion detection model based on Transformer, BiGRU, and DNN, referred to as the TBGD model. The Multi-Head Attention mechanism and Feedforward Neural Network in Transformer help capture global relationships and information; BiGRU models sequential information in sequences; DNN learns complex nonlinear relationships and generates accurate intrusion detection predictions. To solve the problem of data set imbalance, we adopted the RUSK sampling mechanism, in which we used Random-Under-Sampler for majority class samples and K-SMOTE oversampling for minority class samples to balance the data set. In addition, the experiment uses the CICIDS2017 dataset. The experiment shows that after the RUSK sampling mechanism balances the data set, the TBGD model has a higher overall classification accuracy and a higher recognition rate for minority classes than the machine learning and deep learning algorithms compared to the experiment.

A Network Intrusion Detection Approach Using Extreme Gradient Boosting with Max-Depth Optimization and Feature Selection

Network intrusion detection system (NIDS) has become a vital tool to protect information anddetect attacks in computer networks. The performance of NIDSs can be evaluated by the numberof detected attacks and false alarm rates. Machine learning (ML) methods are commonly usedfor developing intrusion detection systems and combating the rapid evolution in the pattern ofattacks. Although there are several methods proposed in the state-of-the-art, the development ofthe most effective method is still of research interest and needs to be developed. In this paper,we develop an optimized approach using an extreme gradient boosting (XGB) classifier withcorrelation-based feature selection for accurate intrusion detection systems. We adopt the XGBclassifier in the proposed approach because it can bring down both variance and bias and hasseveral advantages such as parallelization, regularization, sparsity awareness hardware optimization,and tree pruning. The XGB uses the max-depth parameter as a specified criterion toprune the trees and improve the performance significantly. The proposed approach selects thebest value of the max-depth parameter through an exhaustive search optimization algorithm.We evaluate the approach on the UNSW-NB15 dataset that imitates the modern-day attacks ofnetwork traffic. The experimental results show the ability of the proposed approach to classifyingthe type of attacks and normal traffic with high accuracy results compared with the currentstate-of-the-art work on the same dataset with the same partitioning ratio of the test set.

IoT Cybersecurity Threats and Detection Mechanisms: A Review

Now a day the internet of thing (IoT) grab the attention of many researchers and companies due to different direction of utilization. The cyber security of IoT become one of the aspects of the critical challenges. There are many intrusion detection systems (IDSs) to solve different issues of IoT-Cyber security threats. In this article, we review the state-of-the-art of IoT-IDS, focusing on the strategy that was devised and executed, the dataset that was utilized, the findings, and the assessment that was undertaken. Additionally, the surveyed articles undergo critical analysis and statements in order to give a thorough comparative review. Machine learning and deep learning methods, as well as new classification and feature selection methodologies, are studied and researched. Thus far, each technique has proved the capability of constructing very accurate intrusion detection models.

A New Surveillance and Security Alert System Based on Real-Time Motion Detection

Due to the frequent and widespread instances of burglary, effective and accurate intrusion detection with an alarm system is now necessary. In this study, the proposed system is used for secret and vital places that need a good monitoring system based on human motion detection. The system can protect museums that contain critical historical objects and libraries with old historical books. The security alert system is also essential to the protection of banks. The system uses a two-frame difference algorithm and a simple background modeling algorithm for motion detection with the help of AForge.NET. Whenever the system detects motion, it interprets the frames of motion to determine humans by using YOLO. The security alert system activates the alerts whenever it detects human motion, like sending an email to an authorized person, triggering the LED alert and sound alert, recording a video of that place, taking a motion picture, and saving it within the databases. The system has the advantages of low cost, a user-friendly interface, and high security. The system was thoroughly tested to detect human movement in unauthorized areas. The system successfully detected human activity indoors and outdoors and activated the alarms. Also, the system could detect human movement in all four directions and alert observers directly at the time of the incident. In low light or minimum illumination, the system can determine human motion. The evaluation results showed that the system's accuracy in detecting human movement was 95%, which shows that the system is able to protect vital and secret places and can successfully monitor areas.

Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach

Data scarcity hinders the usability of data-dependent algorithms when tackling IoT intrusion detection (IID). To address this, we utilise the data rich network intrusion detection (NID) domain to facilitate more accurate intrusion detection for IID domains. In this paper, a Geometric Graph Alignment (GGA) approach is leveraged to mask the geometric heterogeneities between domains for better intrusion knowledge transfer. Specifically, each intrusion domain is formulated as a graph where vertices and edges represent intrusion categories and category-wise interrelationships, respectively. The overall shape is preserved via a confused discriminator incapable to identify adjacency matrices between different intrusion domain graphs. A rotation avoidance mechanism and a centre point matching mechanism is used to avoid graph misalignment due to rotation and symmetry, respectively. Besides, category-wise semantic knowledge is transferred to act as vertex-level alignment. To exploit the target data, a pseudo-label election mechanism that jointly considers network prediction, geometric property and neighbourhood information is used to produce fine-grained pseudo-label assignment. Upon aligning the intrusion graphs geometrically from different granularities, the transferred intrusion knowledge can boost IID performance. Comprehensive experiments on several intrusion datasets demonstrate state-of-the-art performance of the GGA approach and validate the usefulness of GGA constituting components.

Enhancement of an IoT hybrid intrusion detection system based on fog-to-cloud computing

Nowadays, with the proliferation of internet of things-connected devices, the scope of cyber-attacks on the internet of things has grown exponentially. So, it makes it a necessity to develop an efficient and accurate intrusion detection system that should be fast, dynamic, and scalable in an internet of things environment. On the other hand, Fog computing is a decentralized platform that extends Cloud computing to deal with the inherent issues of the Cloud computing. As well, maintaining a high level of security is critical in order to ensure secure and reliable communication between Fog nodes and internet of things devices. To address this issue, we present an intrusion detection method based on artificial neural networks and genetic algorithms to efficiently detect various types of network intrusions on local Fog nodes. Through this approach, we applied genetic algorithms to optimize the interconnecting weights of the network and the biases associated with each neuron. Therefore, it can quickly and effectively establish a back-propagation neural network model. Moreover, the distributed architecture of fog computing enables the distribution of the intrusion detection system over local Fog nodes with a centralized Cloud, which achieves faster attack detection than the Cloud intrusion detection mechanism. A set of experiments were conducted on the Raspberry Pi4 as a Fog node, based on the UNSW-NB15 and ToN_IoT data sets for binary-class classification, which showed that the optimized weights and biases achieved better performance than those who used the neural network without optimization. The optimized model showed interoperability, flexibility, and scalability. Furthermore, achieving a higher intrusion detection rate through decreasing the neural network error rate and increasing the true positive rate is also possible. According to the experiments, the suggested approach produces better outcomes in terms of detection accuracy and processing time. In this case, the proposed approach achieved an 16.35% and 37.07% reduction in execution time for both data sets, respectively, compared to other state-of-the-art methods, which enhanced the acceleration of the convergence process and saved processing power.

Hierarchical LSTM-Based Network Intrusion Detection System Using Hybrid Classification

Most existing network intrusion detection systems (NIDSs) perform intrusion detection using only a partial packet data of fixed size, but they suffer to increase the detection rate. In this study, in order to find the cause of a limited detection rate, accurate intrusion detection performance was analyzed by adjusting the amount of information used as features according to the size of the packet and length of the session. The results indicate that the total packet data and all packets in the session should be used for the maximum detection rate. However, existing NIDS cannot be extended to use all packet data of each session because the model could be too large owing to the excessive number of features, hampering realistic training and classification speeds. Therefore, in this paper, we present a novel approach for the classifier of NIDSs. The proposed NIDS can effectively handle the entire packet information using the hierarchical long short-term memory and achieves higher detection accuracy than existing methods. Performance evaluation confirms that detection performance can be greatly improved compared to existing NIDSs that use only partial packet information. The proposed NIDS achieves a detection rate of 95.16% and 99.70% when the existing NIDS show the highest detection rate of 93.49% and 98.31% based on the F1-score using two datasets. The proposed method can improve the limitations of existing NIDS and safeguard the network from malicious users by utilizing information on the entire packet.

An Improved Intrusion Detection in Wireless Sensor Networks Using Hybrid Multiclass Over-Sampling and Deep Neural Networks

With the emergence of new attacks, there is a continual need for innovative approaches that can closely monitor and swiftly adapt to evolving threats. IDSs can be broadly categorized into misuse detection and anomaly detection, each utilizing machine-learning methods. Machine learning algorithms, particularly those relying on datasets like DARPA and KDD Cup 1999,have gained popularity. However, challenges include dataset limitations, overfitting, and the requirement for substantial computational power. This study focuses on the specific problem of class imbalance in Wireless Sensor Networks (WSN) datasets for intrusion detection. Existing techniques, such as oversampling and undersampling, have limitations, and the imbalance poses challenges for accurate intrusion detection model development. The research aims to develop an enhanced intrusion detection model addressing multiclass imbalance through an optimized KNN-SMOTE oversampling technique integrated with a Deep Learning model. The significance lies in its potential to greatly enhance the accuracy of intrusion detection systems, contributing to the security ofcomputer networks. The study's scope involves static dataset analysis and multiclass classification of intrusion attacks. The research questions revolve around the development of a multiclass oversampling technique, a hybrid model, and the performance evaluation of the developed system in comparison to existing IDS.The research proposes a novel OK-SMOTE-DL model combining the potential of the KNN model, firefly algorithm, and the SMOTE model fused with Deep Learning techniques to help create a better model for handling multiclass intrusion datasets that can similarly detect minority classes effectively in wireless sensornetworks

Design of Intrusion Detection and Prevention Model Using COOT Optimization and Hybrid LSTM-KNN Classifier for MANET

INTRODUCTION: MANET is an emerging technology that has gained traction in a variety of applications due to its ability to analyze large amounts of data in a short period of time. Thus, these systems are facing a variety of security vulnerabilities and malware assaults. Therefore, it is essential to design an effective, proactive and accurate Intrusion Detection System (IDS) to mitigate these attacks present in the network. Most previous IDS faced challenges such as low detection accuracy, decreased efficiency in sensing novel forms of attacks, and a high false alarm rate. OBJECTIVES: To mitigate these concerns, the proposed model designed an efficient intrusion detection and prevention model using COOT optimization and a hybrid LSTM-KNN classifier for MANET to improve network security. METHODS: The proposed intrusion detection and prevention approach consist of four phases such as classifying normal node from attack node, predicting different types of attacks, finding the frequency of attack, and intrusion prevention mechanism. The initial phases are done through COOT optimization to find the optimal trust value for identifying attack nodes from normal nodes. In the second stage, a hybrid LSTM-KNN model is introduced for the detection of different kinds of attacks in the network. The third stage performs to classify the occurrence of attacks. RESULTS: The final stage is intended to limit the number of attack nodes present in the system. The proposed method's effectiveness is validated by some metrics, which achieved 96 per cent accuracy, 98 per cent specificity, and 35 seconds of execution time. CONCLUSION: This experimental analysis reveals that the proposed security approach effectively mitigates the malicious attack in MANET.

Intrusion Detection in Big Data Era: A Multi- Objective Approach for Longer Model Lifespans

Despite highly accurate intrusion detection schemes based on machine learning (ML) reported in the literature, changes in network traffic behaviour quickly yield low accuracy rates. An intrusion detection model update is not easily feasible due to the enormous amount of network traffic to be processed in near real-time for high- speed networks, in particular, under big data settings. In this paper, we propose a new scalable long-lasting intrusion detection architecture for the processing of network content and the building of a reliable ML-based intrusion detection model. Experiments performed through the analysis of five years of network traffic, about 20 TB of data, have shown that our approach extends the lifespan of our model by up to six weeks. That occurs because the average accuracy rate of our proposal lasted eight weeks after the training phase, and traditional ones reached only two weeks after the model building. Additionally, our proposal achieves up to 10 Gbps of detection throughput in a 20-core big data processing cluster. KEYWORDS: Machine Learning, Intrusion Detection, Big Data, High-Speed Networks.

Scalable Collaborative Intrusion Detection in Autonomous Vehicular Networks: A hierarchical framework based on game theory

Autonomous Vehicles (AVs) networks are vulnerable to security attacks targeting data integrity and service availability. To successfully operate in emerging automated highway systems (AHS), securing communications among the AVs and with the transportation infrastructure is paramount. An Intrusion Detection System (IDS) is the foremost defense artifact against malicious AV activities. However, individual IDS on board of the AV may not be sufficient enough against diverse and increasingly emerging new threats. In addition, not all security vendors synchronize their update of new IDS security features. Therefore, the collaboration among individual IDSs of AVs enables the vehicles to benefit from the collective knowledge and information of their neighbors and achieve more accurate intrusion detection. This paper presents a hierarchical framework for a Collaborative Intrusion Detection Network (CIDN) that allows the AV platoons to share detection knowledge based on trust and increase the accuracy of their individual IDS. The proposed CIDN involves intra-platoon and inter-platoon collaborations to improve scalability. The experimental results show that collaborative IDSs yield higher accuracy than individual IDSs in detecting misbehaviors in AV networks while achieving scalability.