Due to the concentrated distribution of network traffic, the Internet is highly vulnerable to link flooding attack in Distributed Denial-of-Service attacks (DDoS-LFA), which utilizes the legitimate low-rate attack traffic to block the selected network area. In recent years, building trusted networks has been considered as a promising strategy to address the security issues. Nevertheless, deploying a trusted link defense mechanism in the attacked network area faces many challenges imposed by the smart scheme and legitimate disguise of DDoS-LFA. In order to overcome these challenges, we propose a blockchain-based DDoS-LFA detection and mitigation scheme, named CREDIT, to guarantee the security of attacked area, while existing works only use blockchain to share the detection results of traditional solutions. CREDIT uses blockchain to record and share the information of links and flows in real time, which enables routers in the protected area to easily trace the paths of all active flows and capture the fragile links. On the basis of link features, a credible deep learning method performed on randomly selected nodes is proposed to detect DDoS-LFA against data spoofing. When an attack alarm is raised, CREDIT performs similarity analysis to locate attackers and migrate suspicious traffic based on the flow features of alarm links. Experimental results based on real implementation and attack testbed show that, by integrating blockchain, CREDIT performs better than traditional non-blockchain-based DDoS-LFA defense methods when faced with data tampering.
Muhammad Baqer Mollah, Md Abul Kalam Azad, Yinghui Zhang
Smart devices are considered as an integral part of Internet of Things (IoT), have an aim to make a dynamic network to exchange information, collect data, analysis, and make optimal decisions in an autonomous way to achieve more efficient, automatic, and economical services. Message dissemination among these smart devices allows adding new features, sending updated instructions, alerts or safety messages, informing the pricing information or billing amount, incentives, and installing security patches. On one hand, such message disseminations are directly beneficial to the all parties involved in the IoT system. On the other hand, due to remote procedure, smart devices, vendors, and other involved authorities might have to meet a number of security, privacy, and performance related concerns while disseminating messages among targeted devices. To this end, in this paper, we design STarEdgeChain, a security and privacy aware targeted message dissemination in IoT to show how blockchain along with advanced cryptographic techniques are devoted to address such concerns. In fact, the STarEdgeChain employs a permissioned blockchain assisted edge computing in order to expedite a single signcrypted message dissemination among targeted groups of devices, at the same time avoiding the dependency of utilizing multiple unicasting approaches. Finally, we develop a software prototype of STarEdgeChain and show it's practicability for smart devices. The codes are publicly available at https://github.com/mbaqer/Blockchain-IoT
The coming 6G wireless network is poised to achieve unprecedented data rates, latency, and integration with newer technologies like AI and IoE. On the other hand, along with this kind of growth in the AI domain and the large-scale connectivity in 6G. It is also going to raise many security concerns at the level of intrusion detection and prevention. For intrusion detection, centralized approaches won’t be able to work effectively, therefore there is an utmost need to design decentralized and privacy-preserving solutions. In this work, we propose a novel secure gradients exchange algorithm for distributed intrusion detection in 6G networks. Our method is designed to take into account the use of Federated Learning with secure multi-party computation and blockchain technology. This way ensures that the collaborating parties are able to conduct the training of intrusion detection models in a secure and collaborative manner by retaining privacy in the data. Gradient compression and adaptive secure aggregation strategies are used to further optimize communication overhead and computational complexity. Therefore, our design works in a robust and efficient manner with the high data rates and huge connectivity that 6G networks will provide. To achieve our goal, experiments using the CICIoT2023 dataset were performed, and results showed that our federated learning-based hybrid model composed of CNN1D and a multi-head attention mechanism outperformed other well-known deep learning models in terms of performance. It achieved the highest average accuracy with 79.92%, the highest average detection rate with 77.41%, and a low false alarm rate with 2.55%.
Shereen Ismail, Salah Dandan, Diana W. Dawoud, Hassan Reza
The security of Industrial Supply Chain (ISC) has emerged through the integration of Industrial Internet of Things (IIoT) and Blockchain (BC) technology. This new era involves effectively protecting IIoT systems from various threats and ensuring their smooth operation and resilience against potential cyber-attacks. Within the ISC ecosystem, combining machine learning (ML)-based security models for cyber-attack detection can play a crucial role in enhancing the ISC security and proactively identifying potential threats. This paper presents a BC-enabled ISC that embed ML security model integrated within a multi-layered approach. We conducted a comparative study and performance analysis of several ML classification techniques, with a focus on supervised methods to identify the lightweight model for cyber-attack detection suitable for deployment in resource-constrained IIoT environment. We investigate the performance of Gaussian Naive Bayes (NB), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), and three ensemble techniques, namely Bagging, Stacking, and Boosting. The study employs the WUSTL-IIOT-2021 imbalanced dataset, which contains samples representing four types of attacks, including denial of service (DoS), SQL injection, reconnaissance, and backdoor. The paper addresses the imbalance in class representation by customizing the dataset for training and testing the ML models. Both Mutual Information (MI) and Extra-trees (ET) are applied as a one-stage ensemble feature selection. The performance of the ML models are investigated using classification accuracy (Acc), precision, recall, F1 score, Matthews correlation coefficient (MCC), model size (Mem), training time (TT) and prediction time (PT).
M. Anwar, Noshina Tariq, Muhammad Ashraf, Syed Atif Moqurrab · 7 authors
Cybersecurity challenges pose a significant threat to Healthcare Cyber Physical Systems (CPS) because they heavily rely on wireless communication. Particularly, jamming attacks can severely disrupt the integrity of these CPS networks. This research introduces a decentralized system to address this issue. Therefore, this paper suggested a system that leverages trust and blockchain technology to detect jamming attacks in healthcare CPS effectively. It proposes a layered model to improve CPS networks’ lifetime and performance. In smart healthcare environments, it ensures secure and reliable communication between sensor nodes, wearable sensors, medical devices, and monitoring systems. Results show that the suggested approach outperforms the baseline model in identifying and minimizing jamming assaults, with an average percentage difference of 15.71% more detection rate, 20.21% less packet loss rates, 16.65% less node-level energy consumption, reduced network latency of 8.29%, and 9.63% more network throughput.
Ikram Ud Din, Kamran Khan, Ahmad Almogren, Mahdi Zareei · 5 authors
In order to improve cybersecurity in newly developed network infrastructures, this research investigates the integration of blockchain technology with zero-trust security concepts. The zero-trust paradigm ensures continuous authentication across entities, in contrast to standard security models that often presuppose trust based on a network environment. Blockchain is used to decentralize and impose authentication intensity of communication clarity and honesty. The study compares the performance of the zero trust model enhanced by blockchain to traditional security systems in a number of parameters, such as intrusion detection rates and security breach reaction times, using extensive simulations. The findings demonstrate that the blockchain-enhanced zero-trust architecture performs better than conventional systems in both identifying and countering threats and methodically handling a large volume of transactions when under pressure. These conclusions, which emphasize significant advancements in security applications and system resilience, are predicated on the use of blockchain in zero-trust systems. Subsequent investigations will endeavor to enhance these technologies and investigate their utilization in networks across diverse intricate scenarios.
Ananda Ravuri, M. Sadish Sendil, Moshe Rani, A. Srikanth · 7 authors
Protection of the Internet of Things (IoT) has become a significant concern due to the widespread use of IoT technologies. Conventional Intrusion Detection Systems (IDS) have challenges when used in IoT networks because of resource restrictions and complexities. Blockchain Technology (BCT) has significantly altered organizations' financial behavior and effectiveness in recent years. Data security and system stability are crucial concerns that must be tackled in blockchain systems. The study suggests a mechanism called Deep Blockchain-Enabled Collaborative Anomaly Detection (DBC-CAD) for security-focused distributed Anomaly Detection (AD) and privacy-focused BC with smart contracts in IoT networks. A Modified - Long Short-Term Memory (M-LSTM) based Deep Learning (DL) algorithm with a multi-variable optimization approach has been used for the AD approach. The multi-variable optimization technique has been used to set the hyperparameters. The Ethereum framework creates privacy-focused BC and smart contract techniques that safeguard decentralized AD engines. The proposed M-LSTM model has the highest detection rate of 99.1%. The findings show the effectiveness of the proposed systems in identifying assaults on IoT networks.
In recent years, the convenience and potential use of crypto-assets such as Bitcoin and Ethereum have attracted increasing attention. On the other hand, there have been reports of attacks on the blockchain networks that support crypto-assets in an attempt to steal other users’ assets. In the past, research on attack observation against blockchains has used techniques such as holding real crypto-assets to lure attackers into honeypots or falsifying balances to attackers. However, these methods risk losing crypto-assets to attackers or being exposed as honeypots to attackers. To solve these problems, we propose a new RPC (Remote Procedure Call) honeypot method that returns the wallet address of another partya. holding a high balance in response to an attacker’s request, thereby luring the attacker without having the real crypto-assets. Our experimental evaluation shows that this method can attract more attackers than the method with zero-balance wallets and can observe more sophisticated attacks. Furthermore, we proposed a risk reduction strategy for crypto-asset theft by applying the idea of our method. In the log analysis process, we devised a new clustering method using the number of times an attacker executes a specific method as a feature. By applying this method, we successfully classified attackers based on their objectives, demonstrating the efficient analysis of vast amounts of log data.
Wireless Sensor Networks (WSNs) are essential for data collection across various domains but face growing risks from replication attacks, which introduce new vulnerabilities and security challenges. To address this issue, we propose a novel hybrid approach that integrates Distributed Ledger Technology (DLT) with adaptive Machine Learning (ML) methods, aiming to bolster both security and trustworthiness within WSNs. Specifically, our approach utilizes DLT to secure voting records and manage rewards, while adaptive ML models detect replica nodes by analyzing network parameters, including location, signal strength, and transmission rate. We present and evaluate three ML-based models for detecting replication attacks: 1) Random Forest Model (RFM), 2) Adaptive Weighted Random Forest Model based on Predicted Replica Nodes (AWRFM-PRN), and 3) Adaptive Weighted Random Forest Model based on Predicted Good and Replica Nodes (AWRFM-PGRN). The AWRFM-PRN and AWRFM-PGRN models enhance detection accuracy through iterative weight adjustments based on previous predictions. Our simulations show that the hybrid approach significantly improves detection performance compared to traditional methods. We evaluated our models by increasing the dataset size with varying proportions of replica nodes across ten subsets. We found that the AWRFM-PGRN model achieved around 71% accuracy when replica nodes comprised 50% or more of the network. Meanwhile, the AWRFM-PRN model demonstrated high effectiveness with accuracy ranging from 80% to 99% for replica nodes constituting 15% to 40% of the network. Furthermore, all models delivered nearly 99.9% accuracy when the proportion of replica nodes was between 5% and 10%. This innovative integration of DLT with adaptive ML modeling establishes a benchmark for robust and tamper-proof security in WSNs, offering significant enhancements over traditional ML techniques such as RFM, particularly in scenarios with high replica node counts.
The rapid adoption of Internet of Things (IoT) devices in smart homes has led to growing security vulnerabilities, primarily due to the limitations of traditional, static access control mechanisms. This paper presents a novel, dynamic access control policy that leverages the immutable and transparent nature of Blockchain technology, specifically Ethereum, along with machine learning algorithms to enhance security measures. By integrating machine learning algorithms like Support Vector Machines (SVM) and Neural Networks, the proposed system can adapt and respond to changing behavioural patterns and potential threats in real time. Additionally, a caching mechanism implemented on the Ethereum Blockchain is introduced to optimize system performance and reduce latency. Experimental results demonstrate significant improvements in access control security, system efficiency, and adaptability. The findings of this paper not only contribute to the advancement of secure access control policies for IoT smart homes but pave the way for future research in integrating Blockchain and machine learning for robust and scalable IoT security solutions.
Cloud-based deployments face increasing threats from various types of attacks, necessitating robust anomaly detection frameworks to safeguard against potential security breaches. Existing solutions, such as RSSI, GTM, and APG, though effective to a certain extent, exhibit limitations in terms of precision, accuracy, and scalability. To address these shortcomings, this paper proposes a novel anomaly detection framework that integrates multimodal feature analysis, deep learning models, and QoS-aware sidechains to enhance the prediction accuracy of cloud attacks and optimize blockchain-based cloud installations. By maximizing feature variance across different sample types and leveraging advanced deep learning techniques, the proposed approach significantly outperforms conventional methods in terms of precision, accuracy, recall, and AUC performance. Furthermore, the framework demonstrates superior efficiency in block mining delay, energy consumption, and throughput, making it highly suitable for real-time cloud attack prediction scenarios. The proposed methodology represents a significant advancement in anomaly detection and cloud security, offering a comprehensive solution for addressing challenges in blockchain-based cloud deployments. Thus, the proposed anomaly detection framework employs both Deep Learning and Blockchain technologies. Using Recurrent Neural Networks (RNN) with Convolutional Neural Networks (CNN), the system examines system logs and identifies unusual behavior patterns associated with different attacks. Using Blockchain technology, the framework ensures the transparency and integrity of system logs, and Deep Learning models provide precise and timely anomaly detection. The decision to combine Deep Learning and Blockchain technology is justified by the merits of each technique. The distributed, immutable ledger provided by blockchain technology makes it impossible to tamper with system logs and ensures the accuracy of anomaly detection. While, deep learning models, have exceptional pattern recognition abilities and can adapt to changing attack methods, resulting in high precision, accuracy, recall, and AUC metrics. Analyses of experimental data demonstrate that the proposed framework is effective. The framework achieves impressive performance metrics, such as low delays, 98.5% precision, 99.4% accuracy, 98.3% recall, and 99.2% Area Under the Curve (AUC).
As the deployment of IPv6 networks continues to expand, managing security threats becomes increasingly intricate due to the protocol’s extensive address space and dynamic traffic patterns. This paper presents a novel blockchain-driven decentralized anomaly detection algorithm designed explicitly for IPv6 networks. By leveraging the inherent properties of blockchain—immutability, transparency, and decentralization—our approach enhances security monitoring capabilities. Integrating traffic analysis with a distributed ledger facilitates improved accuracy in anomaly detection and robust resilience against distributed denial-of-service (DDoS) attacks and other threats. Experimental evaluations conducted in a simulated IPv6 environment demonstrate that the proposed methodology outperforms traditional centralized detection systems, significantly improving detection accuracy, attack mitigation, and data integrity.
Remote attestation (RA) protocols have been widely used to evaluate the integrity of software on remote devices.Currently, the state-of-the-art RA protocols lack a crucial feature: transparency.This means that the details of the final attestation verification are not openly accessible or verifiable by the public.Furthermore, the interactivity of these protocols often limits attestation to trusted parties who possess privileged access to confidential device data, such as pre-shared keys and initial measurements.These constraints impede the widespread adoption of these protocols in various applications.In this paper, we introduce zRA, a non-interactive, transparent, and publicly provable RA protocol based on zkSNARKs.zRA enables verification of device attestations without the need for pre-shared keys or access to confidential data, ensuring a trustless and open attestation process.This eliminates the reliance on online services or secure storage on the verifier side.Moreover, zRA does not impose any additional security assumptions beyond the fundamental cryptographic schemes and the essential trust anchor components on the prover side (i.e., ROM and MPU).The zero-knowledge attestation proofs generated by devices have constant size regardless of the network complexity and number of attestations.Moreover, these proofs do not reveal sensitive information regarding internal states of the device, allowing verification by anyone in a public and auditable manner.We conduct an extensive security analysis and demonstrate scalability of zRA compared to prior work.Our analysis suggests that zRA excels especially in peer-to-peer and Pub/Sub network structures.To validate the practicality, we implement an open-source prototype of zRA using the Circom language.We show that zRA can be securely deployed on public permissionless blockchains, serving as an archival platform for attestation data to achieve resilience against DoS attacks.
Cryptocurrencies are crucial in modern commerce and finance, whether at the national, corporate, or individual level. They serve as fundamental currencies for buying and selling, enabling various business transactions. However, the rise of cybercrime has brought about concerns regarding their operations, potential breaches in encrypted currencies, and the security systems managing them. The frequency of attack tactics and the motivation of attackers seeking financial gain are well-known. Many cryptocurrencies lack the necessary algorithms, techniques, and knowledge to effectively detect and mitigate malware, making them vulnerable targets for hackers. In this study, machine learning techniques are employed to detect malicious code in digital currencies. Additionally, a comparison of these techniques is conducted to determine the most suitable algorithm and technology, Furthermore, this study highlights the importance of effective malware detection in securing cryptocurrencies. Three datasets of different sizes were used, each yielding distinct results based on dataset size. The AdaBoost model demonstrated superior performance when applied to the short dataset, while the decision tree model performed best with the medium-sized dataset. Conversely, the Naive Bayes model consistently produced the worst results, while the large-size KNN model achieved the highest performance.