The couplings of convolutional neural networks (CNN) with random forest (RF), support vector machine (SVM), long short-term memory (LSTM), and extreme gradient boosting (XGBoost) ensemble algorithms were used to construct novel ensemble computational models (CNN-LSTM, CNN-XG, CNN-SVM, and CNN-RF) for flood hazard mapping in the monsoon-dominated catchment, Bangladesh. The results revealed that geology, elevation, the normalized difference vegetation index (NDVI), and rainfall are the most significant parameters in flash floods based on the Pearson correlation technique. Statistical method such as the area under the curve (AUC) was used to evaluate model performance. The CNN-RF model could be a promising tool for precisely predicting and mapping flash floods as it is outperformed the other models (AUC = 1.0). Furthermore, to meet sustainable development goals (SDGs), a blockchain-based technology is proposed to create a decentralized flood management tool for help seekers and help providers during and post floods. The suggested tool accelerates emergency rescue operations during flood events.
Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond initial expectations as daily trades exceed $10 billion. As industries become automated, the need for an automated fraud detector becomes very apparent. Detecting anomalies in real time prevents potential accidents and economic losses. Anomaly detection in multivariate time series data poses a particular challenge because it requires simultaneous consideration of temporal dependencies and relationships between variables. Identifying an anomaly in real time is not an easy task specifically because of the exact anomalistic behavior they observe. Some points may present pointwise global or local anomalistic behavior, while others may be anomalistic due to their frequency or seasonal behavior or due to a change in the trend. In this paper we suggested working on real time series of trades of Ethereum from specific accounts and surveyed a large variety of different algorithms traditional and new. We categorized them according to the strategy and the anomalistic behavior which they search and showed that when bundling them together to different groups, they can prove to be a good real-time detector with an alarm time of no longer than a few seconds and with very high confidence.
S. Giribabu, Vikruthi. Sriharsha, Patan Hussain Basha, K. Suresh · 5 authors
A blockchain network's economics and user confidence can be seriously harmed by fraud. Consensus algorithms like proof of work and proof of stake can verify the legitimacy of a transaction but not the identity of the people who are conducting or verifying it. On a blockchain network, fraud can still occur, as a result of this. One approach to fighting fraud is to make use of machine learning techniques. There are two types of machine learning: supervised and unsupervised. We use a variety of supervised machine learning techniques in this study to distinguish between legitimate and fraudulent purchases. We also compare decision trees, Naive Bayes, logistic regression, multilayer perceptron, and other supervised machine learning techniques in detail for this challenge.
While blockchain technology provides strong cryptographic protection on the ledger and the system operations, the underlying blockchain networking remains vulnerable due to potential threats such as denial of service (DoS), Eclipse, spoofing, and Sybil attacks. Effectively detecting such malicious events should thus be an essential task for securing blockchain networks and services. Due to its importance, several studies investigated anomaly detection in Bitcoin and blockchain networks, but their analyses mainly focused on the blockchain ledger in the application context (e.g., transactions) and targets specific types of attacks (e.g., double-spending, deanonymization, etc). In this study, we present a security mechanism based on the analysis of blockchain network traffic statistics (rather than ledger data) to detect malicious events, through the functions of data collection and anomaly detection. The data collection engine senses the underlying blockchain traffic and generates multi-dimensional data streams in a periodic, real-time manner. The anomaly detection engine then detects anomalies from the created data instances based on semi-supervised learning, which is capable of detecting previously unseen patterns, and we introduce our profiling-based detection engine implemented on top of AutoEncoder (AE). Our experimental results evaluated with real and simulated traffic data support the effectiveness of our security mechanism and design choices based on the AE structure, with the approximate detection performance to the supervised learning methods only through the profiling of normal instances. The measured time complexity is sufficiently cheap to perform real-time analysis, with less than 1.4 msec for per-instance testing on a single core setting.
By nature, a traditional attack method, denial-of-service (DDoS) attack poses a considerable threat to the security of the blockchain network layer. This paper proposes a distributed DDoS-attack traffic detection method based on a cross multilayer convolutional neural network model in the blockchain network layer. The method resolves the low generalisation, high misreporting rate, and low detection efficiency problems of the existing detection methods, which are caused by nondistinctive core features and the high complexity of robust features when detecting DDoS attacks transmitted by mixed protocols on a blockchain network layer. First, the model performs a convolution operation on preprocessed traffic on the blockchain network layer using a cross-layer method based on L2 regularisation. After this operation, the model can perceive the detailed features of attack traffic from multiple levels while enhancing the representational performance of key features; specifically, the parameters with high-variance terms are penalised to limit changes in the model’s weight parameters. The highly robust abstract features of attack traffic are extracted, thereby increasing the generalisation ability and reducing the misreporting rate of the model. Second, parametric encoding of the abstract features is performed by a stacked sparse autoencoder based on Kullback–Leibler divergence, and the sparsity of the model is adjusted to reduce the redundant data and the coupling between abstract features. The outputs of the encoded features are then effectively categorised. Finally, the global optimisation of parameters is performed by an improved random gradient-descent algorithm, which prevents oscillation of the training parameters and accelerates the model convergence. In an experimental evaluation, the proposed method achieved satisfactory binary- and multiclass detection of DDoS-attack traffic on both CSE-CIC-IDS 2018 on the AWS dataset and on the real mixed data of a blockchain network layer.
In today's era, internet-connected things provide immense opportunities to the world for enhancing the quality of lives through better data processing and intelligent decision making. Since the last decade, IoT brought numerous changes in people's personal as well as professional lives. With the enhancement in quality of lives, IoT also comes up with challenges such as security and privacy of data and devices. Every day, the attacker generates new zero-day attacks for IoT devices and data, and it's important to detect and protect the IoT eco-system from this type of attacks. Numerous researchers have proposed security schemes and methods to protect the IoT eco-system through either cryptography way or learning technique based way. AI and ML learning techniques have got immense popularity in handling the IoT security challenges as they are automatic in nature and can outperform provided the sufficient quality and quantity of data. Moreover, the AI techniques, including ML, DL and FL helps in intelligent decision-making and can also generate knowledge through its learning techniques. AI needs data to process, and IoT supplies the necessary data to process. In this paper, we provide a state-of-the-art survey for IoT security solutions proposed based on learning techniques. We provide an in-depth review of available learning techniques to solve critical security challenges such as IoT authentication, access control, anomaly detection and malware analysis. At the end, we also highlighted various futuristic technologies that can invigorate IoT research and help in the design of full proof IoT eco-system.
Olaf Kampers, Abdulhakim A. Qahtan, Swati Mathur, Yannis Velegrakis
As a financial asset, cryptocurrencies innovated the financial industry in different ways. However, the lack of regulations and transparency in cryptocurrency markets is hindering the industry from reaching its full potential. There is a need for extensive technical analysis of the cryptocurrency market data to detect possible market manipulation attempts. Anomaly detection techniques can reveal information about abnormal activities in the market and provide insights on manipulation attempts. In this study, a robust unsupervised anomaly detection tool (ADT) is developed for this purpose. Experiments show that ADT outperforms a set of methods in detecting the anomalies in features extracted from the cryptocurrency exchanges data and on a set of benchmark data sets.
Abbas Yazdinejad, Ali Dehghantanha, Reza M. Parizi, Mohammad Hammoudeh · 6 authors
Nowadays, blockchain-based technologies are being developed in various industries to improve data security. In the context of the Industrial Internet of Things (IIoT), a chain-based network is one of the most notable applications of blockchain technology. IIoT devices have become increasingly prevalent in our digital world, especially in support of developing smart factories. Although blockchain is a powerful tool, it is vulnerable to cyber attacks. Detecting anomalies in blockchain-based IIoT networks in smart factories is crucial in protecting networks and systems from unexpected attacks. In this paper, we use Federated Learning (FL) to build a threat hunting framework called Block Hunter to automatically hunt for attacks in blockchain-based IIoT networks. Block Hunter utilizes a cluster-based architecture for anomaly detection combined with several machine learning models in a federated environment. To the best of our knowledge, Block Hunter is the first federated threat hunting model in IIoT networks that identifies anomalous behavior while preserving privacy. Our results prove the efficiency of the Block Hunter in detecting anomalous activities with high accuracy and minimum required bandwidth.
Manuel Febrero–Bande, Wenceslao González–Manteiga, Brenda Prallon, Yuri F. Saporito
This paper proposes a classification model for predicting the main activity of bitcoin addresses based on their balances. Since the balances are functions of time, we apply methods from functional data analysis; more specifically, the features of the proposed classification model are the functional principal components of the data. Classifying bitcoin addresses is a relevant problem for two main reasons: to understand the composition of the bitcoin market, and to identify addresses used for illicit activities. Although other bitcoin classifiers have been proposed, they focus primarily on network analysis rather than curve behavior. Our approach, on the other hand, does not require any network information for prediction. Furthermore, functional features have the advantage of being straightforward to build, unlike expert-built features. Results show improvement when combining functional features with scalar features, and similar accuracy for the models using those features separately, which points to the functional model being a good alternative when domain-specific knowledge is not available.
Following the recent trend of data-centric AI, we propose a clustering method to offer additional insight into precious and yet less-explored cryptocurrency price time series. While invaluable efforts have been conveyed in the domain of time series clustering, we integrate and harmonize some of the best practices in the field, namely Gramian Angular Field (GAF), Variational AutoEncoders (VAEs), and Deep Embedded Clustering (DEC). We use time series to image transformations as a preprocessing step for VAE to reduce dimensionality. After performing K-means clustering on VAE’s latent space, we provide DEC with cluster centroids from the previous step and retrain our network to do the clustering task. We evaluate the proposed method with the Bitcoin Tick-bar price dataset from 2017 onwards. Results demonstrate that our method leads to financially interpretable clusters and can improve Silhouette Score up to 10 percent compared to non-imaged time series.
Mohammad Javad Shayegan, Hamid Reza Sabor, Mueen Uddin, Chin‐Ling Chen
The popularity and remarkable attractiveness of cryptocurrencies, especially Bitcoin, absorb countless enthusiasts every day. Although Blockchain technology prevents fraudulent behavior, it cannot detect fraud on its own. There are always unimaginable ways to commit fraud, and the need to use anomaly detection methods to identify abnormal and fraudulent behaviors has become a necessity. The main purpose of this study is to use the Blockchain technology of symmetry and asymmetry in computer and engineering science to present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. In this study, a collective anomaly approach was used. Instead of detecting the anomaly of individual addresses and wallets, the anomaly of users was examined. In addition to using the collective anomaly detection method, the trimmed_Kmeans algorithm was used for clustering. The results of this study show the anomalies are more visible among users who had multiple wallets. The proposed method revealed 14 users who had committed fraud, including 26 addresses in 9 cases, whereas previous works detected a maximum of 7 addresses in 5 cases of fraud. The suggested approach, in addition to reducing the processing overhead for extracting features, detect more abnormal users and anomaly behavior.
Industry 4.0 is all about doing things in a concurrent, secure, and fine-grained manner. Internet of Things edge sensors and their associated data play a predominant role in today’s industry ecosystem. Breaching data or forging source devices after injecting advanced persistent threats (APTs) damages the industry owners’ money and loss of operators’ lives. The existing challenges include APT injection attacks targeting vulnerable edge devices, insecure data transportation, trust inconsistencies among stakeholders, incompliant data storing mechanisms, etc. Edge servers often suffer because of their lightweight computation capacity to stamp out unauthorized data or instructions, which, in essence, makes them exposed to attackers. When attackers target edge servers while transporting data using traditional public-key infrastructure-rendered trusts, consortium blockchain (CBC) offers proven techniques to transfer and maintain those sensitive data securely. With the recent improvement of edge machine learning, edge devices can filter malicious data at their end, which largely motivates us to institute a blockchain and artificial intelligence-aligned APT detection system. The unique contributions of this article include efficient APT detection at the edge and transparent recording of the detection history in an immutable blockchain ledger. In line with that, the certificateless data transfer mechanism boosts trust among collaborators and ensures an economical and sustainable mechanism after eliminating existing certificate authority. Finally, the edge-compliant storage technique facilitates efficient predictive maintenance. The respective experimental outcomes reveal that the proposed technique outperforms the other competing systems and models.
An eclipse attack is a common method used to attack the blockchain network layer; however, detecting eclipse attacks is challenging, and the performance of existing methods is inadequate due to uneven sample distribution, incomplete definition of discriminating features, and weak feature perception. Thus, this paper proposes an eclipse attack traffic detection method based in a custom combination of features and deep learning. To describe the behavior characteristics of attack traffic more accurately, traffic attribute features in there levels are defined in combination with the eclipse attack method. Here, the downstream traffic behavior feature of the eclipse attack is described from the conventional traffic feature, and the frequency distribution characteristics of eclipse attack traffic is by introducing the φ ‐entropy divergence algorithm. In addition, the structural characteristics of eclipse attack traffic are mapped from the rate of changes in traffic communication and load features. Then, the improved synthetic minority oversampling technique (ISMOTE) up‐sampling algorithm is employed to eliminate interference caused by the uneven distribution of eclipse attack traffic samples on the detection results. In addition, the ISMOTE algorithm adjusts the sampling weight of minority class samples, supports automatic clustering and efficient up‐sampling of samples, and improves the detection accuracy performance of eclipse attack samples by calculating the local cluster density. Then, deep feature mining is performed on the eclipse attack traffic from the distribution characteristics of space and time series using a CNN and Bi‐LSTM. Simultaneously, mining features are fully integrated into mixed feature using the multihead attention mechanism such that the relevance and complementarity of the two feature distributions can be utilized to enhance the model’s ability to perceive the spatiotemporal relationship of the eclipse attack traffic. Finally, the generated multihead attention items are detected for binary classification, and the results are output. Experimental results demonstrate that the proposed method can comprehensively enhance detection performance and sufficiently detect and classify eclipse attack traffic in the blockchain network layer.
Cryptocurrency networks that provide a new way of securing financial transactions have gained a surge of interest in recent years. However, recent studies reveal that blockchain networks are rampant with frauds and are prone to several privacy and security issues. The public availability of cryptocurrency transaction records provides an unprecedented opportunity for researchers to analyze cryptocurrency transactions. In particular, anomaly detection techniques are promising avenues for fighting illicit activities such as money laundering, terrorist financing, drug trafficking, scams, frauds, and many more.In this dissertation, we address the challenges of detecting anomalous entities on cryptocurrency networks. Firstly, we introduce effective techniques for generating features for network entities that are directly devised from raw data and highlight the utility of those features in detecting illicit accounts on the Ethereum network. Next, we enrich the proposed method by expanding the feature set through the incorporation of graph-based features that embed the relational information of networks. This also enables us to generalize our methods to instances of cryptocurrency networks with different architectural models. Based on the success of our method in anomaly detection in cryptocurrency networks, we further generalize our model to encompass a generic temporal weighted multidigraph and show the state-of-the-art results for anomaly detection in other common domains including rating and social networks. In doing so, we also investigate the challenges of employing node classification techniques for anomaly detection, which is a common practice. Here, we discuss the importance of performance metrics and evaluation settings when interpreting the efficiency of different methods and tasks, which is often overlooked by the community. Finally, we shift our focus to examining the inherent challenges of learning on dynamic networks, which is an important emerging research field with applications in drug discovery, computational finance, social networks, etc. Here, we propose solutions for providing a more robust evaluation setup for dynamic graph learning methods. The key contributions of this dissertation are twofold: First, we describe efficient techniques for detecting anomalies on cryptocurrency networks and generalize them to other real-world complex networks. Second, we focus on the temporal aspect of these networks and investigate how the dynamism of networks affects the downstream tasks and evaluation settings
The purpose of this study is to explore the co-movement between COVID-19 cases and eight cryptocurrencies. Cryptocurrencies (Bitcoin, Ethereum, Tether, Binance Coin, Dogecoin, Ripple, USD Coin and Bitcoin Cash) are selected based on their market capitalisations. Daily data is considered from 30 January 2020 to 19 May 2021. The continuous wavelet transform (wavelet coherence) is used to determine the time-varying co-movement between COVID-19 instances and cryptocurrencies in this research. COVID-19 and cryptocurrency prices are interlinked, as found using the wavelet method. Similar results were discovered for Tether, Binance Coin, and Ripple. Although this seems to be the case, Dogecoin appears to be an alternative investment during COVID-19. The research is unique and adds to the existing body of knowledge, even though some of the results address the function of cryptocurrencies in times of crisis. The research findings indicate that investors and crypto enthusiasts should keep an eye out in the scenario of COVID-19 scenarios when making investments in cryptocurrency marketplaces.
Blockchain is one of the most anticipated technology revolutions, with immense promise in various applications. It is a distributed and encrypted database that can address a range of challenges connected to online security and trust. While many people identify Blockchain with cryptocurrencies such as Bitcoin, it has a wide range of applications in supply chain management, health, Internet of Things (IoT), education, identity theft prevention, logistics, and the execution of digital smart contracts. Although Blockchain Technology (BT) has numerous advantages for Decentralized Applications (DApps), it is nevertheless vulnerable to abuse, smart contract failures, security, theft, trespassing, and other concerns. As a result, using Machine Learning (ML) models to detect anomalies is an excellent way to detect and safeguard blockchain networks from criminal activity. Adapting ensemble learning methods in ML to create better prediction outcomes is a viable approach for anomaly identification. Ensemble learning, as the name implies, refers to creating a stronger and more accurate classification by combining the prediction results of numerous weak models. As a result, an in-depth evaluation of ensemble learning methodologies for anomaly detection in the blockchain network ecosystem is applied in this paper. It comprises numerous ensemble methods (e.g., averaging, voting, stacking, boosting, bagging). The review collects data from three established databases, which are Scopus, Web of Science (WoS), and Google Scholar. Specific keywords are employed, such as Blockchain, Ethereum, Bitcoin, Anomaly Detection, and Ensemble Learning, employing advanced searching algorithms. The results of the search found 60 primary articles from 2017 to 2022 (30 from Scopus, 20 from the WoS, and 10 from Google Scholar). Based on these findings, we decided to divide our debate into three primary themes: (1) the fundamentals of Blockchain Technology (BT), (2) the overview of ensemble learning, and (3) the integration and analysis of ensemble learning in blockchain networks for anomaly detection. In terms of awareness and knowledge, the results are also discussed in terms of what they mean and where future research should go.
Oumaima Fadi, Karim Zkik, El Ghazi Abdellatif, Mohammed Boulmalf
Smart environments consist of a collection of sensors, actuators, and numerous computing units that improve human life. With the booming of smart environments, data generation has been notably increasing in recent years, which must be managed in a smart and optimal manner. The components (i.e., workstations and cloud) used for data processing are not the best to recommend since it is risky and resource costing. For that matter, enterprises, firms and companies are deploying blockchain technologies (BT) as a more suitable alternative. In fact, blockchain is a distributed transaction ledger ensuring the reliability and transparency of data. However, BT faces some inherent security challenges such as DoS, eclipse and double spending attacks as well as Advanced Persistent Threat (APT) and malware. Thus, advanced anomaly detection and mitigation approaches, especially the ones using artificial intelligence (AI) techniques (e. g. Machine Learning, Deep Learning, Federated Learning) are required to address the aforementioned issues. In combination, AI and BT are capable of detecting anomalies within blockchain networks with high accuracy. In this paper, with a focus on cyber security issues, we explore the challenges of blockchain deployment in smart environments. Additionally, we explore the use of anomaly detection AI-based techniques as a ledger of blockchain technologies to address the security issues in smart environments. Thus, we propose a framework that emphasizes the challenges of BT, values and capabilities of BT-AI integration. We also present research trends to highlight potential research paths for improving the security of blockchain networks using artificial intelligence.
S. Kate Devitt, Jason Scholz, Timo Schless, L Lewis
Abstract Artificial intelligences (AI) will increasingly participate digitally and physically in conflicts yet there is a lack of trusted communications with humans for humanitarian purposes. For example, in disasters and conflicts messaging and social media are used to share information, however, international humanitarian relief organisations treat this information as unverifiable and untrustworthy. Furthermore, current AI implementations can be brittle, with a narrow scope of application and wide scope of ethical risks. Meanwhile, human error can cause significant civilian harms even by combatants committed to compliance with international humanitarian law. AI offers an opportunity to help reduce the tragedy of war and better deliver humanitarian aid to those who need it. However, to be successful, these systems must be trusted by humans and their information systems, overcoming flawed information flows in conflict and disaster zones that continue to be marked by intermittent communications, poor situation awareness, mistrust and human errors. In this paper, we consider the integration of a communications protocol (the ‘Whiteflag protocol’), distributed ledger ‘blockchain’ technology, and information fusion with artificial intelligence (AI), to improve conflict communications called “Protected Assurance Understanding Situation & Entities” (PAUSE). Such a trusted human-AI communication network could provide accountable information exchange regarding protected entities, critical infrastructure, humanitarian signals and status updates for humans and machines in conflicts. Trust-based information fusion provides resource-efficient use of diverse data sources to increase the reliability of reports. AI can catch human mistakes and complement human decision making, while human judgment can direct and override AI recommendations. We examine several realistic potential case studies for the integration of these technologies into a trusted human-AI network for humanitarian benefit including mapping a conflict zone with civilians and combatants in real time, preparation to avoid incidents and using the network to manage misinformation. We finish with a real-world example of a PAUSE-like network, the Human Security Information System (HSIS), being developed by USAID, that uses blockchain technology to provide a secure means to better understand the civilian environment.
Xiong Yang, Yuling Chen, Xiaobin Qian, Tao Li · 5 authors
The distributed deployment of wireless sensor networks (WSNs) makes the network more convenient, but it also causes more hidden security hazards that are difficult to be solved. For example, the unprotected deployment of sensors makes distributed anomaly detection systems for WSNs more vulnerable to internal attacks, and the limited computing resources of WSNs hinder the construction of a trusted environment. In recent years, the widely observed blockchain technology has shown the potential to strengthen the security of the Internet of Things. Therefore, we propose a blockchain-based ensemble anomaly detection (BCEAD), which stores the model of a typical anomaly detection algorithm (isolated forest) in the blockchain for distributed anomaly detection in WSNs. By constructing a suitable block structure and consensus mechanism, the global model for detection can iteratively update to enhance detection performance. Moreover, the blockchain guarantees the trust environment of the network, making the detection algorithm resistant to internal attacks. Finally, compared with similar schemes, in terms of performance, cost, etc., the results prove that BCEAD performs better.
Detection of unknown malware and its variants remains both an operational and a research challenge in the Internet of Things (IoT). The Internet of Medical Things (IoMT) is a particular type of IoT network which deals with communication through smart healthcare (medical) devices. One of the prevailing problems currently facing IoMT solutions is security and privacy vulnerability. Previous malware detection methods have failed to provide security and privacy. In order to overcome this issue, the current study introduces a novel technique called biserial correlative Miyaguchi-Preneel blockchain-based Ruzicka-index deep multilayer perceptive learning (BCMPB-RIDMPL). The present research aims to improve the accuracy of malware detection and minimizes time consumption. The current study combines the advantages of machine-learning techniques and blockchain technology. The BCMPB-RIDMPL technique consists of one input layer, three hidden layers, and one output layer to detect the malware. The input layer receives the number of applications and malware features as input. After that, the malware features are sent to the hidden layer 1, in which feature selection is carried out using point biserial correlation, which reduces the time required to detect the malware. Then, the selected features and applications are sent to the hidden layer 2. In that layer, Miyaguchi-Preneel cryptographic hash-based blockchain is applied to generate the hash value for each selected feature. The generated hash values are stored in the blockchain, after which the classification is performed in the third hidden layer. The BCMPB-RIDMPL technique uses the Ruzicka index to verify the hash values of the training and testing malware features. If the hash is valid, then the application is classified as malware, otherwise it is classified as benign. This method improves the accuracy of malware detection. Experiments have been carried out on factors such as malware detection accuracy, Matthews's correlation coefficient, and malware detection time with respect to a number of applications. The observed quantitative results show that our proposed BCMPB-RIDMPL method provides superior performance compared with state-of-the-art methods.
With the integration of the Internet of Things (IoT) in the field of transportation, the Internet of Vehicles (IoV) turned to be a vital method for designing Smart Transportation Systems (STS). STS consist of various interconnected vehicles and transportation infrastructure exposed to cyber intrusion due to the broad usage of software and the initiation of wireless interfaces. This study proposes a federated deep learning-based intrusion detection framework (FED-IDS) to efficiently detect attacks by offloading the learning process from servers to distributed vehicular edge nodes. FED-IDS introduces a context-aware transformer network to learn spatial-temporal representations of vehicular traffic flows necessary for classifying different categories of attacks. Blockchain-managed federated training is presented to enable multiple edge nodes to offer secure, distributed, and reliable training without the need for centralized authority. In the blockchain, miners confirm the distributed local updates from participating vehicles to stop unreliable updates from being deposited on the blockchain. The experiments on two public datasets (i.e., Car-Hacking, TON_IoT) demonstrated the efficiency of FED-IDS against state-of-the-art approaches. It reveals the credibility of securing networks of intelligent transportation systems against cyber-attacks.