Blockchain Papers

Salam Al-E’mari, Mohammed Anbar, Yousef Sanjalawe, Selvakumar Manickam

No abstract is available for this record.

Philip Huff, Qinghua Li

No abstract is available for this record.

Efthimios-Enias Gojka, Niclas Kannengießer, Benjamin Sturm, Jan Bartsch · 5 authors

No abstract is available for this record.

Luca Caviglione, Michał Choraś, Igino Corona, Artur Janicki · 7 authors

Cyber attacks are currently blooming, as the attackers reap significant profits from them and face a limited risk when compared to committing the “classical” crimes. One of the major components that leads to the successful compromising of the targeted system is malicious software. It allows using the victim’s machine for various nefarious purposes, e.g., making it a part of the botnet, mining cryptocurrencies, or holding hostage the data stored there. At present, the complexity, proliferation, and variety of malware pose a real challenge for the existing countermeasures and require their constant improvements. That is why, in this paper we first perform a detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade. On this basis, we review the evolution of modern threats in the communication networks, with a particular focus on the techniques employing information hiding. Next, we present the bird’s eye view portraying the main development trends in detection methods with a special emphasis on the machine learning techniques. The survey is concluded with the description of potential future research directions in the field of malware detection.

Chen Fang, Zifan Li, Bozhong Li, Chunxue Deng · 8 authors

With the increasing application of the IoT, more and more scenarios need to establish efficient and stable dedicated communication networks. In the 5G era, communication network operators can lease customized optical network slices to users, to meet users' different needs in the application scenarios of the IoT. However, in the multi-user transaction process of optical network slicing, there will be untrusted mechanisms and important data security issues. This paper uses blockchain technology to ensure the optical network slicing lease transaction environment, so that users can complete slicing transactions more safely and build a high-quality dedicated communication network for IoT application scenarios.

Murshedul Arifeen, Abdullah Al Mamun, Tanvir Ahmed, M. Shamim Kaiser · 5 authors

No abstract is available for this record.

Wenbo Zhang, Jiaxing Wang, Guangjie Han, Shuqiang Huang · 6 authors

The continuously increasing number of connected smart devices has led to the emergence of a crucial fault detection challenge to the Internet of Things (IoT). In this study, we aim to identify a method for the effective detection of faults in IoT devices. An IoT network model is first established, and a data edge verification mechanism based on blockchain is proposed; the blockchain is used to ensure that the data cannot be tampered with, and their accuracy is verified using the edge. Finally, a data set accuracy weighted random forest based on particle swarm optimization is proposed. The simulation results demonstrate that the proposed detection algorithm is both effective and efficient.

Qian Liao, Yijun Gu, Junfan Liao, Wenzheng Li

Anomaly detection is one of the research hotspots in Bitcoin transaction data analysis. In view of the existing research that only considers the transaction as an isolated node when extracting features, but has not yet used the network structure to dig deep into the node information, a bitcoin abnormal transaction detection method that combines the node's own features and the neighborhood features is proposed. Based on the formation mechanism of the interactive relationship in the transaction network, first of all, according to a certain path selection probability, the features of the neighbohood nodes are extracted by way of random walk, and then the node's own features and the neighboring features are fused to use the network structure to mine potential node information. Finally, an unsupervised detection algorithm is used to rank the transaction points on the constructed feature set to find abnormal transactions. Experimental results show that, compared with the existing feature extraction methods, feature fusion improves the ability to detect abnormal transactions.

Ishwarlal Hingorani, Rushabh Khara, Deepika Pomendkar, Nataasha Raul

The criminal activities in India are increasing at a rapid rate. Many of these activities go unreported. Even after having an online portal for the police for storing FIRs and NCRs, most of the FIRs are handwritten as a traditional practice. In most of the cases, the complainant has to be present in the police station to file a cognizable offense. An effective system for e-governance was started in 2009 named Crime and Criminal Tracking Network and Systems (CCTNS) for the entire country. However, it is a centralized system for a particular state. Thus there is a need for a completely decentralized system for assuring that there is no central point of failure in the system and complaints are managed securely protected from unauthorized access. Our aim is to propose a blockchain-based solution to manage complaints against both cognizable and non-cognizable offenses. The FIR filed by the police will be encrypted, stored in the IPFS and hash is added to the blockchain network. If the police decide not to file the FIR under pressure or deny receiving any complaint, then the complainant will have strong proof against him/her as the complaint along with its timestamp was stored on the blockchain network. Having all the records stored in an immutable database would remove any chances of the FIR/NCR being tampered and going unnoticed.

Omaji Samuel, Nadeem Javaid, Adia Khalid, Muhammad Imrarn · 5 authors

In a multi-agent system (MAS), the trust of each agent has become hot research issues in the smart grids. The traditional trust systems that use access control and cryptography are not sufficient to handle the dynamic behavior of agents. Also, they are inefficient to solve the computational overhead of the cryptographic primitives. Based on these limitations, this paper proposes a blockchain-based trust management system for MAS. The proposed system consists of two layers: a lower layer that enables an agent to perform direct and indirect trust evaluations of other agents during interactions. Multi-source feedback from the interactions among different aggregators is feed to the blockchain. The upper layer is used to perform trust credibility of agents based on trust distortion, consistency and reliability. The credibility evaluation is used to determine the dynamic behavior of agents and also detect dishonest agents in the system. Trust model and security analysis of the proposed system are provided. Moreover, simulation results evaluate the effectiveness of the proposed trust system while the system is secure against bad-mouthing and on-off attacks.

Ashley Mayle

Existing communication protocols in security networks are highly centralized. While this naively makes the controls easier to physically secure, external actors require fewer resources to disrupt the system because there are fewer points in the system can be interrupted without the entire system failing. We present a solution to this problem using a proof-of-work-based blockchain implementation built on MultiChain. We construct a test-bed network containing visual imagers and microwave sensor information. These data types are ubiquitous in perimeter security systems and allow a realistic representation of a real-world network architecture. The cameras in this system use an object detection algorithm to find important targets in the scene. The raw data from both the sensors and imagers are placed in a transaction. These transactions are then bundled into blocks and broadcast to the rest of the network using the Bitcoin-based MultiChain protocol. We develop five tests to examine the security metrics of our network. We performed the five security metric test using different sized networks from 7 to 39 nodes to determine how the metrics scale with respect to size. We find that when compared to a centralized architecture our implementation provides a resiliency increase that is expected from a blockchain- based protocol without slowing the system so much that a human operator would notice. Furthermore, our approach is able to detect tampering in real time. Based on these results, we theorize that security networks in general could use a blockchain- based approach in a meaningful way.

Wenjun Fan, Younghee Park, Shubham Kumar, Priyatham Ganta · 6 authors

Collaborative intrusion detection system (CIDS) shares the critical detection-control information across the nodes for improved and coordinated defense. Software-defined network (SDN) introduces the controllers for the networking control, including for the networks spanning across multiple autonomous systems, and therefore provides a prime platform for CIDS application. Although previous research studies have focused on CIDS in SDN, the real-time secure exchange of the detection-relevant information (e.g., the detection signature) remains a critical challenge. In particular, the CIDS research still lacks robust trust management of the SDN controllers and the integrity protection of the collaborative defense information to resist against the insider attacks transmitting untruthful and malicious detection signatures to other participating controllers. In this paper, we propose a blockchain-enabled collaborative intrusion detection in SDN, taking advantage of the blockchain's security properties. Our scheme achieves three important security goals: to establish the trust of the participating controllers by using the permissioned blockchain to register the controller and manage digital certificates, to protect the integrity of the detection signatures against malicious detection signature injection, and to attest the delivery/update of the detection signature to other controllers. Our experiments in CloudLab based on a prototype built on Ethereum, Smart Contract, and IPFS demonstrates that our approach efficiently shares and distributes detection signatures in real-time through the trustworthy distributed platform.

Ahmed Al Guqhaiman, Oluwatobi Akanbi, Amer Aljaedi, C. Edward Chow

Underwater Wireless Sensor Networks (UWSNs) are liable to malicious attacks due to limited bandwidth, limited power, high propagation delay, path loss, and variable speed. The major differences between UWSNs and Terrestrial Wireless Sensor Networks (TWSNs) necessitate a new mechanism to secure UWSNs. The existing Media Access Control (MAC) and routing protocols have addressed the network performance of UWSNs, but are vulnerable to several attacks. The secure MAC and routing protocols must exist to detect Sybil, Blackhole, Wormhole, Hello Flooding, Acknowledgment Spoofing, Selective Forwarding, Sinkhole, and Exhaustion attacks. These attacks can disrupt or disable the network connection. Hence, these attacks can degrade the network performance and total loss can be catastrophic in some applications, like monitoring oil/gas spills. Several researchers have studied the security of UWSNs, but most of the works detect malicious attacks solely based on a certain predefined threshold. It is not optimal to detect malicious attacks after the threshold value is met. In this paper, we propose a multi-factor authentication model that is based on zero-knowledge proof to detect malicious activities and secure UWSNs from several attacks.

Junwei Liang, Maode Ma

Intrusion Detection Systems (IDS) is an important technology for cyber security, as it can mitigate both inner and outer threats in networks. However, a critical problem in IDSs is that the detection capacity is gradually decaying with the emergence of unknown attacks. To constantly retrain IDSs with a more extensive database is critical to make IDSs adaptive with the ever-changing network environment, but the security institutes usually lack the motivation to persistently update and maintain the database for public. Thus, in this paper, a blockchain-based database (bc-DB) is proposed, which is multilaterally maintained by the security institutes and universities using Data Coins (DCoins) as the incentives. In addition, a Lifetime Learning IDS (LL-IDS) is further designed as the supplement of the bc-DB for common IDS users. After being retrained by the latest bc-DB, the LL-IDS can detect the newly discovered attacks while uploading the suspect network packets to the database. Simulation experiments show that the proposed LL-IDS with the bc-DB are secure and effectiveness in attacks detection.

Xueshuo Xie, Yaozheng Fang, Zhaolong Jian, Ye Lu · 6 authors

No abstract is available for this record.

Praveen Gorla, Vinay Chamola, Vikas Hassija, Dusit Niyato

5G communications technologies are the backbone of future communications systems in satisfying different heterogeneous requirements of the industry and consumer applications. These systems rely on standardized protocols and heterogeneous architectures to engineer massive scaling of communication devices. Network Slicing (NS) can be incorporated into 5G to cater to the ever increasing needs of smart communications, ranging from Enhanced Mobile Broadband (eMBB) to Ultra-Reliable Low Latency Communications (URLLC). In this article, we present the performance analysis of such a network using real-world deployment and testing scenarios with state based network slice allocation. To account for transparency and security, a Blockchain-based model is integrated within the network operations. In particular, we carefully account for the latency aware operations of Network Slicing along with its UE state based allocation by telecom providers using Blockchain. Furthermore, provisioning the Blockchain in the Network Slice allocations increases the transparency and efficiency of resource handling operations within the network.

Yusuf Muhammad Tukur, Dhavalkumar Thakker, Irfan‐Ullah Awan

Abstract Internet of Things (IoT) platforms are responsible for overall data processing in the IoT System. This ranges from analytics and big data processing to gathering all sensor data over time to analyze and produce long‐term trends. However, this comes with prohibitively high demand for resources such as memory, computing power and bandwidth, which the highly resource constrained IoT devices lack to send data to the platforms to achieve efficient operations. This results in poor availability and risk of data loss due to single point of failure should the cloud platforms suffer attacks. The integrity of the data can also be compromised by an insider, such as a malicious system administrator, without leaving traces of their actions. To address these issues, we propose in this work an edge‐based blockchain enabled anomaly detection technique to prevent insider attacks in IoT. The technique first employs the power of edge computing to reduce the latency and bandwidth requirements by taking processing closer to the IoT nodes, hence improving availability, and avoiding single point of failure. It then leverages some aspect of sequence‐based anomaly detection, while integrating distributed edge with blockchain that offers smart contracts to perform detection and correction of abnormalities in incoming sensor data. Evaluation of our technique using real IoT system datasets showed that the technique remarkably achieved the intended purpose, while ensuring integrity and availability of the data which is critical to IoT success.

Suparna Dhar, Indranil Bose

The benefits ushered through the use of IoT come with its share of security concerns. IoT networks are heterogeneous and existing network security tools and controls are inadequate for them. The IoT devices and the data transmitted to and from these devices remain exposed to a multitude of threats. In this paper, we review the security concerns involved in the implementation of IoT and propose a framework for security of IoT devices based on Zero Trust and blockchain. A risk-based segmentation of IoT network increases the homogeneity of IoT device attributes and communication protocols. Zero Trust extends the perimeter of trust beyond the IT/OT network. Blockchain improves the device identification and access control capabilities of the IoT network. We support the conceptual framework with a case study on the implementation of IoT security using Zero Trust and blockchain. We provide a risk-scoring method and a five-point recommendation for management of IoT security. Our proposed IoT security framework will help implementors of IoT to overcome existing security concerns and will benefit academic researchers and practitioners alike.

Mahdi Alkaeed, Zaid Alamro, Muhammed Al-Ali, Hasan Abbas Al-Mohammed · 5 authors

Nowadays cryptography and their technologies have been with us for a long time. This field of science is developing day by day. Blockchain has come a long way since the birth of its first application. This application is Bitcoin which depends on Blockchain or DLT (Distributed Ledger Technology). Bitcoin is the most famous cryptocurrency intended for mass which appeared the first time in 2008. This innovation led to sparked in the digital revolution, which provides for many sectors and industries the security features, decentralization, and a reliable mechanism for transmission and storing data. This new technology led to the meteoric rise of Bitcoin and other such cryptocurrencies, so this has aroused the interest of governments and emerging companies to get an effective role in competition. We can apply blockchain in different sectors and different Internet of things (IoT). Cryptocurrency mining has become a major concern for computer professionals and also for those who earn more money from an additional source. Mining is a process for settle mathematical issues that depend on the strength and speed of the devices to solve those problems, which by resolving them leads to getting rewards in the form of a digital currency. In this paper, we will highlight the benefits of using both CPU (Central Processing Unit) and GPU (Graphical Processing Unit) and a practical comparison between them to find out the best and fastest based on previous studies and on the characteristics and architecture of each.

Amit Kumar Tyagi, Terrance Frederick Fernandez, S Aswathy

The frequency of election has been increased in multiple parts of the country. Being the largest democracy, India takes several months for accomplishing elections. Additionally, the elections demand government works, development, cost, tenure, etc. Since 1999, Electronic Voting Machine [EVM]have deployed widely to ensure that the voting can also be made tamper-proof. India has introduced Electronic Voting Machines (EVM) way back in 2004 for its parliamentary election with 380 million networked voters. EVM, being an electronic system was used to record votes. Only Bharat Electronics Limited and Electronics Corporation of India Limited conduct EVM and VVPATs (Voter Verifiable Paper Audit Trail) checks. The sole solution to the issue is introduction of Aadhaar; a unique identification number. The proposed research work introduces a novel concept that provides tamperless and paperless election in India. In summary, this work provides a simple, reliable concept using Blockchain technology and Aadhaar for deploying paperless electronic voting in India.

Heng Qi, Junxiao Wang, Wenxin Li, Yuxin Wang · 5 authors

Nowadays, more and more sensors, devices and applications are connected in Industrial Internet of Things (IIoT), producing massive real-time flows which need to be scheduled for Quality-of-Service provision. To realize application-aware and adaptive flow scheduling, the problem of traffic classification must be addressed at first. When edge computing paradigm is introduced into IIoT, the traffic classification service can be deployed on edge node in the near-end. Recently, deep-learning-based IIoT traffic classification methods show better performance, but the computational cost of deep learning model is too high to be deployed on edge node. Moreover, increasingly unknown flows generated by new devices and emerging industrial APPs lead to frequent training of traffic classifiers. It is difficult to migrate the complex process of classifier training from cloud server to edge nodes with limited resources. To address these issues, we take the benefits of hash mechanism and consensus mechanism in blockchain to design a lightweight IIoT traffic classification service, which is more applicable for edge computing paradigm. First, inspired by the hash mechanism in blockchain and the learning to hash for big data, we propose a new learning-to-hash method named extension hashing. By this method, we can build the set of binary coding tress (BCT set), then generating hash table for more efficient k-nearest neighbor-based classification without complex classifier training. Then, we design a new voting-based consensus algorithm to synchronize the BCT sets and the hash tables across edge nodes, thereby providing the traffic classification service. Finally, we conduct data-driven simulations to evaluate the proposed service. By comparing traffic classification results on public data set, we can see that the proposed service achieves the highest classification accuracy with the minimal time cost and memory usage.

Jiahao Sun, Chengrong Wu, Jiawei Ye

Compared to the virtual machine, containers that share the host's operating system kernel is a more lightweight virtualization technology. The container technology makes it easier and faster to deploy and update applications and the container orchestration technology provide people with a powerful tools to manage containers which make the clouds based on containers become popular. But at the same time, the container technology also introduces many security challenges. Among them, the vulnerabilities and malware in container images, as well as some wrong settings that violate security compliance rules, have become main potential security threats. In addition, tampered malicious images are likely to become entry points for the attacks to the cloud platform infrastructure and other containers. In this paper, we proposes a container cloud security enhancement system, based on blockchain technology. On the one hand, this system combines multi-apsects security checks to prevent the upload of images that contain security threats to the cloud environment. on the other hand, Using blockchain technology to verify the integrity of the image and record the information about security to prevent the generation of malicious containers based on tampered images. In addition, the security of the container cloud environment is further improved by scanning the images in the image repositories periodically, patching the images and upgrading the running containers in the cloud.

Kazuki Hara, Teppei Sato, Mitsuyoshi Imamura, Kazumasa Omote

Blockchain is a service operated by a peer-to-peer type distributed network, and protocol control such as JSON-RPC is implemented as the interface for flexibility and operability. However, attacks that use protocol control against vulnerable and unmanaged interfaces have been reported. One of the methods to track cyber attacks on such a malicious user's network service is a honeypot that imitates the service and acquires attacker's behavior information. In this research, focusing on the Ethereum network, the behavior of malicious users is clarified using malicious communication history sent to simple honeypots installed in nine countries, Ethereum network information and darknet arrival packets. By analyzing these, the behavior of attackers and the tendency of requests were elucidated, and primary safety measures were established.

Muhammad Asaad Cheema, Nouman Ashraf, Asad Aftab, Hassaan Khaliq Qureshi · 6 authors

Voting is a central component of a country's political life cycle. Privacy, authentication and integrity of citizens' votes and their data are considered to be essential to any e-voting program. In order to resolve these concerns, we propose a stable e-voting system based on the principles of blockchain and machine learning. We use blockchain to ensure the integrity and security of votes, machine learning model to detect intrusion in voting data centers and e-voting stations. In the proposed model, we use the concepts of personal and public blockchain. The personal blockchain is used for the purposes of voter registration and voting. The public blockchain is used to maintain the integrity of the personal data of the voters by storing the root hash derived from the Merkle hash tree and revealing the results of the voting stations as soon as the voting process is completed. The proposed blockchain-based e-voting system offers transparency, treasury, confidence and prevents intrusion into the information exchange network.