Blockchain Papers

Mykola Beshley, Mykhailo Klymash, Ilona Scherm, Halyna Beshley · 5 authors

No abstract is available for this record.

Nilesh P. Sable, Vijay U. Rathod

No abstract is available for this record.

S Sheela, S. Shalini, D Sai Harsha, Vani Chandrashekar · 5 authors

This research introduces an approach to detect malware attacks using blockchain technology that integrates signature-based and behavioralbased methods. The proposed system uses a decentralized blockchain network to share and store malware signatures and behavioral patterns. This enables faster and more efficient detection of new malware files. The signature-based method involves storing the signatures in the blockchain and the sharing of the signature of malware files among the user nodes of the p2p blockchain network, while the behavioral-based approach analyzes the behavior and actions of files in a separate virtualized environment to identify suspicious patterns. This system addresses the limitations of conventional signature-based methods, which can be evaded by polymorphic malware, and behavioral-based methods, which may generate false positives. The results of the evaluation indicate that the proposed system achieves high detection rates while maintaining low false positives. Overall, the proposed system offers an effective and efficient approach to malware detection by utilizing the strengths of both signature-based and behavioral-based methods and utilizing the security and transparency benefits of blockchain technology.

Y. Madhusekhar, Pabbala Priyanka, Deena Babu Mandru, T. Srikanth

No abstract is available for this record.

Ahmed A. M. Sharadqh, Hazem Hatamleh, As’ad Mahmoud As’ad Alnaser, Said S. Saloum · 5 authors

Internet of Things (IoT) is an emerging technology and its applications are flattering amidst many users, as it makes everything easier. As a consequence of its massive growth, security and privacy are becoming crucial issues where the IoT devices are perpetually vulnerable to cyber-attacks. To overcome this issue, intrusion detection and mitigation is accomplished which enhances the security in IoT networks. In this paper, we proposed Blockchain entrenched Bi-level intrusion detection and graph based mitigation framework named as HybridChain-IDS. The proposed work embrace four sequential processes includes time-based authentication, user scheduling and access control, bi-level intrusion detection and attack graph generation. Initially, we perform time-based authentication to authenticate the legitimate users using NIK-512 hashing algorithm, password and registered time are stored in Hybridchain which is an assimilation of blockchain and Trusted Execution Environment (TEE) which enhances data privacy and security. After that, we perform user scheduling using Cheetah Optimization Algorithm (COA) which reduces the complexity and then the access control is provided to authorized users by smart contract by considering their trust and permission level. Then, we accomplish bi-level intrusion detection using ResCapsNet which extracts sufficient features and classified effectively. Finally, risk of the attack is evaluated, and then the attacks graphs are generated by employing Enhanced k-nearest neighbor (KNN) algorithm to identify the attack path. Furthermore, the countermeasures are taken based on the attack risk level and the attack graph is stored in Hybridchain for eventual attack prediction. The implementation of this proposed work is directed by network simulator of NS-3.26 and the performance of the proposed HybridChain-IDS is enumerated based on various performance metrics.

Noor Hussein

No abstract is available for this record.

Vu Tuan Truong, Long Bao Le

Metaverse is expected to rely on massive Internet of Things (IoT) connections so it inherits various security threats from the IoT network and also faces other sophisticated attacks related to virtual reality technology. As traditional security approaches show various limitations in the large-scale distributed metaverse, this paper proposes MetaCIDS, a novel collaborative intrusion detection (CID) framework that leverages metaverse devices to collaboratively protect the metaverse. In MetaCIDS, a federated learning (FL) scheme based on unsupervised au-toencoder and an attention-based supervised classifier enables metaverse users to train a CID model using their local network data, while the blockchain network allows metaverse users to train a machine learning (ML) model to detect intrusion network flows over their monitored local network traffic, then submit verifiable intrusion alerts to the blockchain to earn metaverse tokens. Security analysis shows that MetaCIDS can efficiently detect zero-day attacks, while the training process is resistant to SPoF, data tampering, and up to 33% poisoning nodes. Performance evaluation illustrates the efficiency of MetaCIDS with 96% to 99% detection accuracy on four different network intrusion datasets, supporting both multi-class detection using labeled data and anomaly detection trained on unlabeled data.

Salabat Khan, Fei Luo, Zijian Zhang, Farhan Ullah · 13 authors

Cyber-attacks are becoming more common against Internet users due to the increasing dependency on online communication in their daily lives. X.509 Public-Key Infrastructure (PKIX) is the most widely adopted and used system to secure online communications and digital identities. However, different attack vectors exist against the PKIX system, which attackers exploit to breach the security of the reliant protocols. Recently, various projects (e.g., Let’s Encrypt and Google Certificate Transparency) have been started to encrypt online communications, fix PKIX vulnerabilities, and guard Internet users against cyber-attacks. This survey focuses on classical PKIX proposals, certificate revocation proposals, and their implementation on blockchain as well as ledger technologies. First, we discuss the PKIX architecture, the history of the World Wide Web, the certificate issuance process, and possible attacks on the certificate issuance process. Second, a taxonomy of PKIX proposals, revocation proposals, and their modern implementation is provided. Then, a set of evaluation metrics is defined for comparison. Finally, the leading proposals are compared using 15 evaluation metrics and 13 cyber-attacks before presenting the lessons learned and suggesting future PKIX and revocation research.

Hayam Alamro, Radwa Marzouk, Nuha Alruwais, Noha Negm · 8 authors

An IoT healthcare system refers to the use of Internet of Things (IoT) devices and technologies in the healthcare industry. It involves the integration of various interconnected devices, sensors, and systems to collect, monitor, and transmit health-related data for medical purposes. Blockchain-assisted intrusion detection on IoT healthcare systems is an innovative approach to enhancing the security and privacy of sensitive medical data. By combining the decentralized and immutable nature of blockchain technology with intrusion detection systems (IDS), it is possible to create a more robust and trustworthy security framework for IoT healthcare systems. With this motivation, this study presents Blockchain Assisted IoT Healthcare System using Ant Lion Optimizer with Hybrid Deep Learning (BHS-ALOHDL) technique. The presented BHS-ALOHDL technique enables IoT devices in the healthcare sector to transmit medical data securely and detects intrusions in the system. To accomplish this, the BHS-ALOHDL technique performs ALO based feature subset selection (ALO-FSS) system to produce a series of feature vectors. The HDL model integrates convolutional neural network (CNN) features and long short-term memory (LSTM) model for intrusion detection. Lastly, the flower pollination algorithm (FPA) is exploited for the optimal hyperparameter tuning of the HDL approach, which results in an enhanced detection rate. The experimental outcome of the BHS-ALOHDL system was tested on two benchmark datasets and the outcomes indicate the promising performance of the BHS-ALOHDL technique over other models.

Ayodeji Adeniran, Aziz Mohaisen

No abstract is available for this record.

Zahoor Ali Khan, Sana Amjad, Farwa Ahmed, Abdullah M. Almasoud · 6 authors

Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.

Gulab Sanjay, S. B. Goyal, Prasenjit Chatterjee

Blockchain technology has gained significant attention as a secure and decentralized platform for various applications. However, the immutable and distributed nature of blockchain also presents unique challenges for detecting anomalies and suspicious activities within the network. This research paper proposes a novel approach to anomaly detection in blockchain using machine learning techniques. The goal of this study is to develop an effective and scalable anomaly detection framework that can analyze the vast amount of data generated within a blockchain network and identify irregularities or potential security threats. The proposed framework leverages the power of machine learning algorithms to learn patterns, relationships, and behaviours from historical blockchain data, enabling the detection of anomalous activities in real time.The research paper first focuses on feature extraction techniques tailored specifically for blockchain data. These techniques consider key characteristics of blockchain transactions, such as transaction size, timestamp, and involved addresses, to construct meaningful features that capture the underlying patterns and trends. Various dimensionality reduction techniques are also explored to handle the high-dimensional nature of blockchain data.Subsequently, several machine learning algorithms, including clustering, classification, and anomaly detection methods, are employed to train models using the extracted features. The performance of different algorithms is evaluated using benchmark datasets and real-world blockchain data to assess their accuracy, precision, and recall in detecting anomalies. Additionally, the scalability of the proposed framework is investigated to ensure its effectiveness in large-scale blockchain networks.Furthermore, the research paper investigates the integration of domain-specific knowledge, such as known attack patterns and regulatory compliance rules, into the anomaly detection framework. This hybrid approach combines the strengths of machine learning algorithms with expert knowledge to enhance the accuracy and interpretability of anomaly detection results.The experimental results demonstrate that the proposed anomaly detection framework achieves promising performance in identifying various types of anomalies in blockchain data. It exhibits high detection rates while minimizing false positives, thereby providing valuable insights for blockchain network administrators and regulators to mitigate security risks and safeguard the integrity of blockchain systems. In conclusion, this research paper presents an innovative approach to anomaly detection in blockchain using machine learning. The proposed framework addresses the unique challenges posed by blockchain's decentralized and immutable nature, offering an effective solution for detecting suspicious activities and ensuring the security of blockchain networks. The findings of this study contribute to the growing field of blockchain analytics and have significant implications for real-world blockchain applications in domains such as finance, supply chain management, and healthcare.

Hao Sui, Jiale Zhang, Xiaobing Sun, Bing Chen · 6 authors

No abstract is available for this record.

Shi Chen, Yong Guan

No abstract is available for this record.

Muhammad Nouman, U. Qasim, Hina Nasir, Abdullah M. Almasoud · 6 authors

In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage.

T. Subburaj, K. Shilpa, Saba Sultana, K. Suthendran · 7 authors

No abstract is available for this record.

Stephen W. Turner, Murat Karakuş, Evrim Güler, Suleyman Uludag

The state of computer network technologies has continually advanced at a rapid pace. Software Defined Networking (SDN) and Blockchain (BC) have emerged as complementary technologies providing support that facilitates greater security and greater network performance for many domains of application, including the Internet of Things (IoT) ecosystem, ideally resulting in an improvement in our collective quality of life. The proliferation of IoT devices, driven by a wide variety of use cases and its ubiquitous availability, combined with the emergence of SDN and BC, presents rich opportunities for various emerging research efforts. This paper presents a comprehensive survey of the studies in which BC and SDN have been integrated into the IoT ecosystem, referred to hereafter as BC-enabled Software- Defined IoT (BC-SDIoT). First, we discuss the motivations and drivers for integrating BC-enabled SDN and BC-SDIoT, as well as the benefits and drawbacks. Second, we categorize the relevant studies according to six key implementation objectives and ideas that combine BC, SDN, and IoT technologies to create smart, secure, and effective frameworks: Security, computing paradigms (edge and fog computing), trust management, access control & authentication, privacy, and networking. In the corresponding sections, we present the categories (i.e., problem domains) of the aforementioned novel taxonomy and discuss related studies (i.e., solutions) in depth. Finally, we outline potential major challenges, open issues, and future prospects that require further research attention and intensive endeavors for complete and ground-breaking frameworks to broaden newer research domains in BC-SDIoT. This survey paper may be a fruitful primer for a reader investigating the exploitation of BC in SDN and IoT ecosystems.

Abdellah Ouaguid

The year 2020 saw remarkable domination of Android devices. Android’s large share of the global market (85%) places it first in the list of preferred targets for mobile cybercrime. Computer attacks try to control and access confidential user information by exploiting the various vulnerabilities present in the various components of the Android ecosystem. This thesis aims to propose a new Framework named ANDROSCANREG (Android Permissions Scan Registry) which incorporates an extensible approach for analyzing Android applications initially based on permissions and deployed in a decentralized and distributed system. The mentioned framework is based on the emerging technology called "Blockchain" whose potential is approved for transparency, availability, security, and reliability without resorting to a central trusted entity. Furthermore, in our efforts to improve the implementation of this Framework, we have proposed a new consensus algorithm called "Proof of Conformity -PoC-" in order to improve the reliability of consensus algorithms whose node weight calculation is based on one (or more) distinctive measurable criterion (stake, power, etc.). This improvement relates to the addition of a new impact factor called "Node Security Metric (NSM)" in the calculation of the node weight. NSM is primarily based on the weight recalculation of each network node based on the security and stability of its respective software and hardware environment. PoC weight recalculation is based on Common Vulnerability Scoring System (CVSS) vulnerabilities, our new approach aims to strengthen the node security index and encourage participants, respecting the recommended security requirements, to take advantage of their proactivity, vigilance, and compliance by increasing their chance of being selected as a Leader (validator) and winning rewards corresponding to the effort deployed. Besides, PoC has been theoretically evaluated via simulation scenarios through which significant results have been obtained showing that our approach ensures more likelihood for the more secure participating nodes to be designated as a validator based on their compliance rates represented by their NSM scores. Moreover, we thought as part of our research axis to equitably reward active participants. Indeed, we have presented a new approach for calculating rewards and penalties for systems based on Blockchain technology. The objective of our proposal is to ensure a new source of income in order to retain participants by guaranteeing them permanent profitability in exchange for their active participation in the stability and security of the Blockchain network to which they belong. We have studied and analyzed existing systems that, in general, favor the monopoly of rewards by attributing them either only to the Leader node (elected by a consensus algorithm) or benefit the Leader from a large part of the rewards and distributing the rest to a shortlist of participants. The result of our study shows that our approach offers more benefits by ensuring permanent, dynamic, and proportional rewards for all participating nodes according to their scores and compliance rate, the latter impacts the gradual penalty system put in place, which verifies the compliance of each node to the Blockchain protocol rules. A new innovative concept of operations execution in a Blockchain network was also proposed in this thesis. Indeed, the new approach improves the traditional data validation processes opted by Blockchain-based systems by allowing their nodes to adopt different and modifiable environments at any time in order to reduce the false positive rate and help identify polymorphic treatments and thus improve the reliability of the final results.

Kailash Chandra Bandhu, Ratnesh Litoriya, Murtaza Bagwala, Alakmar Barwaniwala · 5 authors

Building an electronic voting system is a prime requirement of a government election system to avoid the manipulation in the voting machines and make them more secure and efficient. There is a need of cost-efficient, time-saving, and trusted voting system. Blockchain technology is a revolutionary method that provides decentralised, distributed, and immutable ledgers features. The proposed work utilised this recent technology along with smart contract and keccak256 encryption algorithm to implement vote casting. The performance of the system is measured based on the execution time of the smart contract, average voting time per user, and the hidden and visible gas cost for smart contract deployment for voting. The results obtained come to be promising with an average execution time of 6ms per vote and the percentage of visible cost out of voting and contract deployment is 15.42% and hidden costs out of voting and contract deployment is 84.58%.

T. Sathya, N Keertika, Sirikonda Shwetha, Deepti Upodhyay · 5 authors

In recent years, ransomware attacks have become a more significant source of computer penetration. Only general-purpose computing systems with sufficient resources have been harmed by ransomware so far. Numerous ransomware prediction strategies have been published, but more practical machine learning ransomware prediction techniques still need to be developed. In order to anticipate ransomware assaults, this study provides a method for obtaining data from artificial intelligence and machine learning systems. A more accurate model for outcome prediction is produced by using the data science methodology. Understanding the data and identifying the variables are essential elements of a successful model. A variety of machine learning algorithms are applied to the pre-processed data, and the accuracy of each technique is compared to determine which approach performed better. Additional performance indicators including recall, accuracy, and f1-score are also taken into account while evaluating the model. It uses machine learning to predict how the ransomware attack would pan out.

Xiangbin Li, Xiaofei Xing, Guojun Wang, Peiqiang Li · 5 authors

No abstract is available for this record.

David Basin, Daniel Stefan Dietiker, Srđan Krstić, Yvonne-Anne Pignolet · 7 authors

No abstract is available for this record.

A. Gómez Ramírez, Loui Al Sardy, Francis Gomez Ramirez

Blockchain security is becoming increasingly relevant in today's cyberspace as it extends its influence in many industries. This paper focuses on protecting the lowest level layer in the blockchain, particularly the P2P network that allows the nodes to communicate and share information. The P2P network layer may be vulnerable to several families of attacks, such as Distributed Denial of Service (DDoS), eclipse attacks, or Sybil attacks. This layer is prone to threats inherited from traditional P2P networks, and it must be analyzed and understood by collecting data and extracting insights from the network behavior to reduce those risks. We introduce Tikuna, an open-source tool for monitoring and detecting potential attacks on the Ethereum blockchain P2P network, at an early stage. Tikuna employs an unsupervised Long Short-Term Memory (LSTM) method based on Recurrent Neural Network (RNN) to detect attacks and alert users. Empirical results indicate that the proposed approach significantly improves detection performance, with the ability to detect and classify attacks, including eclipse attacks, Covert Flash attacks, and others that target the Ethereum blockchain P2P network layer, with high accuracy. Our research findings demonstrate that Tikuna is a valuable security tool for assisting operators to efficiently monitor and safeguard the status of Ethereum validators and the wider P2P network

Synim Selimi, Blerim Rexha, Kamer Vishi

The rapid evolution of the Internet, particularly the emergence of Web3, has transformed the ways people interact and share data. Web3, although still not well defined, is thought to be a return to the decentralization of corporations' power over user data. Despite the obsolescence of the idea of building systems to detect and prevent cyber intrusions, this is still a topic of interest. This paper proposes a novel conceptual approach for implementing decentralized collaborative intrusion detection networks (CIDN) through a proof-of-concept. The study employs an analytical and comparative methodology, examining the synergy between cutting-edge Web3 technologies and information security. The proposed model incorporates blockchain concepts, cyber non-fungible token (cyberNFT) rewards, machine learning algorithms, and publish/subscribe architectures. Finally, the paper discusses the strengths and limitations of the proposed system, offering insights into the potential of decentralized cybersecurity models.