Jie Cai, Bin Li, Jiale Zhang, Xiaobing Sun · 5 authors
Smart contract security has drawn extensive attention in recent years because of the enormous economic losses caused by vulnerabilities. Even worse, fixing bugs in a deployed smart contract is difficult, so developers must detect security vulnerabilities in a smart contract before deployment. Existing smart contract vulnerability detection efforts heavily rely on fixed rules defined by experts, which are inefficient and inflexible.To overcome the limitations of existing vulnerability detection approaches, we propose a GNN based approach for smart contract vulnerability detection. First, we construct a graph representation for a smart contract function with syntactic and semantic features by combining abstract syntax tree (AST), control flow graph (CFG), and program dependency graph (PDG). To further strengthen the presentation ability of our approach, we perform program slicing to normalize the graph and eliminate the redundant information unrelated to vulnerabilities. Then, we use a Bidirectional Gated Graph Neural-Network model with hybrid attention pooling to identify potential vulnerabilities in smart contract functions.
Arkan Hammoodi Hasan Kabla, Mohammed Anbar, Selvakumar Manickam, Taief Alaa Al-Amiedy · 7 authors
Ethereum attracts more investors, researchers, and even scammers for many reasons; this is the first platform that enables the new Decentralized Applications (DApps) to run on top of the blockchain network. However, the rich semantics and applications of DApps inevitably introduce many security issues that have grabbed significant attention from industry and academics due to their destructive impact on DApps in recent years. Therefore, there is a vital need to study the applicability of Intrusion Detection System in detecting Ethereum-based attacks. Hence, this paper is among the first comprehensive review that studies the applicability of IDS in detecting Ethereum-based attacks. In addition, this paper lists all the potential attacks on Ethereum passing through the vulnerabilities that cause those attacks and ending with the consequences of each attack. Besides, this paper analyses all the IDS-based related works of Ethereum attacks detection since the Ethereum platform was launched in 2015. Finally, this paper discusses the open issues regarding vulnerabilities and attacks, challenges, and future directions.
Arkan Hammoodi Hasan Kabla, Mohammed Anbar, Selvakumar Manickam, Shankar Karupayah
Recently, the rapid flourish of blockchain technology in the financial field has attracted many cybercriminals’ attention to launch blockchain-based attacks such as Ponzi schemes, Scam wallets, and phishing scams. Currently, Ethereum is the most prominent blockchain-based platform and the first that supports smart contracts. However, the number of phishing scam accounts are reportedly more than 50% of all cybercrimes in Ethereum. In contrast, this paper proposes a detection mechanism called Ethereum Phishing Scam Detection (Eth-PSD) that attempts to detect phishing scam-related transactions using a novel machine learning-based approach. Eth-PSD tackles some of the limitations in the existing works, such as the use of imbalanced datasets, complex feature engineering, and lower detection accuracy. We also investigated the aspects of constructing a new updated and balanced dataset that can be used for evaluating Eth-PSD effectively. Our experimental results indicate that Eth-PSD could efficiently detect the phishing scam on Ethereum with a detection accuracy of 98.11%, with a very low False Positive Rate of 0.01. Taken together, Eth-PSD showed a superior advantage compared to the existing works in reducing the dimensionality of the dataset by feature engineering and achieved an overall detection accuracy with an improvement of at least 6% compared to other existing solutions from the related work.
Shahid Hussain Danwar, Javed Ahmed Mahar, Aneela Kiran
Election allows the voter of a country to select the most suitable group of candidates to run the government. Election in Pakistan is simply paper-based method but some certain political and socio-economic issues turn that simple process in complicated and disputes once. Solutions of such problems are consisting of many methods including the e-voting system. The e-voting system facilitates the voters to cast their votes by electronic means with very easy and convenient way. This also allows maintaining the security and secrecy of the voter along with election process. Electronic voting reduces the human-involvement throughout the process from start to the end. Such system is not established yet in Pakistan. Hence, every election is disputed always. In this paper we proposed the framework of an e-voting system and simulation with the use of blockchain ledger technology. The proposed framework owns the capacity to deal with ballots in Pakistan. The novelty of the framework is that the e-voting system is linked to National Database and Registration Authority (NADRA) database which assures voter's validation. The fundamental requirements were kept in consideration during the performance evaluation of the system. The results are processed and depicted specially with reference to the number of voters and voting stations, real time of vote casting, network bandwidth and controlling principles. The results support the decision makers and guide them in the foundation and customization of an e-voting system in Pakistan.
Information sharing is one of the huge topics in social media platform regarding the daily news related to events or disasters happens in nature or its human-made. The automatic urgent need identification and sharing posts and information delivery with a short response are essential tasks in this area. The key goal of this research is developing a solution for management of disasters and emergency response using social media platforms as a core component. This process focuses on text analysis techniques to improve the process of authorities in terms of emergency response and filter the information using the automatically gathered information to support the relief efforts. Specifically, we used state-of-art Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) based on supervised and unsupervised learning using social media datasets to extract real-time content related to the emergency events to comfort the fast response in a critical situation. Similarly, the blockchain framework used in this process for trust verification of the detected events and eliminating the single authority on the system. The main reason of using the integrated system is to improve the system security and transparency to avoid sharing the wrong information related to an event in social media.
A smart public transport system is expected to be an integral part of our human lives to improve our mobility and reduce the effect of our carbon footprint. The safety and ongoing maintenance of the smart public transport system from cyberattacks are vitally important. To provide more comprehensive protection against potential cyberattacks, we propose a novel approach that combines blockchain technology and a deep learning method that can better protect the smart public transport system. By the creation of signed and verified blockchain blocks and chaining of hashed blocks, the blockchain in our proposal can withstand unauthorized integrity attack that tries to forge sensitive transport maintenance data and transactions associated with it. A hybrid deep learning-based method, which combines autoencoder (AE) and multi-layer perceptron (MLP), in our proposal can effectively detect distributed denial of service (DDoS) attempts that can halt or block the urgent and critical exchange of transport maintenance data across the stakeholders. The experimental results of the hybrid deep learning evaluated on three different datasets (i.e., CICDDoS2019, CIC-IDS2017, and BoT-IoT) show that our deep learning model is effective to detect a wide range of DDoS attacks achieving more than 95% F1-score across all three datasets in average. The comparison of our approach with other similar methods confirms that our approach covers a more comprehensive range of security properties for the smart public transport system.
Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen
Over the past decade, blockchain technology has attracted a huge attention from both industry and academia because it can be integrated with a large number of everyday applications of modern information and communication technologies (ICT). Peer-to-peer (P2P) architecture of blockchain enhances these applications by providing strong security and trust-oriented guarantees, such as immutability, verifiability, and decentralization. Despite these incredible features that blockchain technology brings to these ICT applications, recent research has indicated that the strong guarantees are not sufficient enough and blockchain networks may still be prone to various security, privacy, and reliability issues. In order to overcome these issues, it is important to identify the anomalous behaviour within the actionable time frame. In this article, we provide an in-depth survey regarding integration of anomaly detection models in blockchain technology. For this, we first discuss how anomaly detection can aid in ensuring security of blockchain based applications. Then, we demonstrate certain fundamental evaluation metrics and key requirements that can play a critical role while developing anomaly detection models for blockchain. Afterwards, we present a thorough survey of various anomaly detection models from the perspective of each layer of blockchain. Finally, we conclude the article by highlighting certain important challenges alongside discussing how they can serve as future research directions for new researchers in the field.
Rihab Habeeb Sahib, Prof. Dr. Eman Salih Al-Shamery
Regular E-voting systems for elections may count the votes in less time,less cost,save the privacy of citizens,but still considered risky as votes can be tampered.E-voting systems based on a network distributed ledger show fast results,more trusted,save privacy,cannot be tampered,and distributed in which no central organization controls the system.This paper illustrate an e-voting system to solve the challenge of a massive ledger that is distributed among network-nodes using a data reduction technique as a security-matching-tool,singular value decomposition(SVD) that handle a copy of election results in another form and matched with the SQL-database results to announce a successful election-event representing a transparency-powerful-secured-system
Recently, phishing scams have posed a significant threat to blockchains. Phishing detectors direct their efforts in hunting phishing addresses. Most of the detectors extract target addresses’ transaction behavior features by random walking or constructing static subgraphs. The random walking methods, unfortunately, usually miss structural information due to limited sampling sequence length, while the static subgraph methods tend to ignore temporal features lying in the evolving transaction behaviors. More importantly, their performance undergoes severe degradation when the malicious users intentionally hide phishing behaviors. To address these challenges, we propose TEGDetector, a dynamic graph classifier that learns the evolving behavior features from transaction evolution graphs (TEGs). First, we cast the transaction series into multiple time slices, capturing the target address’s transaction behaviors in different periods. Then, we provide a fast nonparametric phishing detector (FD) to narrow down the search space of suspicious addresses. Finally, TEGDetector considers both the spatial and temporal evolutions toward a complete characterization of the evolving transaction behaviors. Moreover, TEGDetector utilizes adaptively learned time coefficient to pay distinct attention to different periods, which provides several novel insights. Extensive experiments on the large-scale Ethereum transaction dataset demonstrate that the proposed method achieves state-of-the-art (SOTA) detection performance. The code of TEGDetector is open sourced at https://github.com/Seaocn/TEGDetector.
On an Ethereum node, txpool (a.k.a. mempool) is a buffer storing unconfirmed transactions and controls what downstream services can see, such as mining and transaction propagation. This work presents the first security study on Ethereum txpool designs.
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.
Phan The Duy, Hien Do Hoang, Do Thi Thu Hien, Anh Gia-Tuan Nguyen · 5 authors
Software-Defined Network (SDN) is a new arising terminology of network architecture with outstanding features of orchestration by decoupling the control plane and the data plane in each network element. Even though it brings several benefits, SDN is vulnerable to a diversity of attacks. Abusing the single point of failure in the SDN controller component, hackers can shut down all network operations. More specifics, a malicious OpenFlow application can access to SDN controller to carry out harmful actions without any limitation owing to the lack of the access control mechanism as a standard in the Northbound. The sensitive information about the whole network such as network topology, flow information, and statistics can be gathered and leaked out. Even worse, the entire network can be taken over by the compromised controller. Hence, it is vital to build a scheme of access control for SDN's Northbound. Furthermore, it must also protect the data integrity and availability during data exchange between application and controller. To address such limitations, we introduce B-DAC, a blockchain-based framework for decentralized authentication and fine-grained access control for the Northbound interface to assist administrators in managing and protecting critical resources. With strict policy enforcement, B-DAC can perform decentralized access control for each request to keep network applications under surveillance for preventing over-privileged activities or security policy conflicts. To demonstrate the feasibility of our approach, we also implement a prototype of this framework to evaluate the security impact, effectiveness, and performance through typical use cases.
Henry Vargas, Carlos Lozano-Garzón, Germán A. Montoya, Yezid Donoso
Internet of Things (IoT) networks have been integrated into industrial infrastructure schemes, positioning themselves as devices that communicate highly classified information for the most critical companies of world nations. Currently, and in order to look for alternatives to mitigate this risk, solutions based on Blockchain algorithms and Machine Learning techniques have been implemented separately with the aim of mitigating potential threats in IIoT networks. In this paper, we sought to integrate the previous solutions to create an integral protection mechanism for IoT device networks, which would allow the identification of threats, activate secure information transfer mechanisms, and it would be adapted to the computational capabilities of industrial IoT. The proposed solution achieved the proposed objectives and is presented as a viable mechanism for detecting and containing intruders in an IoT network. In some cases, it overcomes traditional detection mechanisms such as an IDS.
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.
Blockchain, as a distributed ledger technology, becomes increasingly popular, especially for enabling valuable cryptocurrencies and smart contracts. However, the blockchain software systems inevitably have many bugs. Although bugs in smart contracts have been extensively investigated, security bugs of the underlying blockchain systems are much less explored. In this paper, we conduct an empirical study on blockchain's system vulnerabilities from four representative blockchains, Bitcoin, Ethereum, Monero, and Stellar. Specifically, we first design a systematic filtering process to effectively identify 1,037 vulnerabilities and their 2,317 patches from 34,245 issues/PRs (pull requests) and 85,164 commits on GitHub. We thus build the first blockchain vulnerability dataset. We then perform unique analyses of this dataset at three levels, including (i) file-level vulnerable module categorization by identifying and correlating module paths across projects, (ii) text-level vulnerability type clustering by natural language processing and similarity-based sentence clustering, and (iii) code-level vulnerability pattern analysis by generating and clustering code change signatures that capture both syntactic and semantic information of patch code fragments. Our analyses reveal three key findings: (i) some blockchain modules are more susceptible than the others; notably, each of the modules related to consensus, wallet, and networking has over 200 issues; (ii) about 70% of blockchain vulnerabilities are of traditional types, but we also identify four new types specific to blockchains; and (iii) we obtain 21 blockchain-specific vulnerability patterns that capture unique blockchain attributes and statuses, and demonstrate that they can be used to detect similar vulnerabilities in other popular blockchains, such as Dogecoin, Bitcoin SV, and Zcash.
Intrusion Detection Systems (IDS) have been the industry standard for securing IoT networks against known attacks. To increase the capability of an IDS, researchers proposed the concept of blockchain-based Collaborative-IDS (CIDS), wherein blockchain acts as a decentralised platform allowing collaboration between CIDS nodes to share intrusion related information, such as intrusion alarms and detection rules. However, proposals in blockchain-based CIDS overlook the importance of continuous evaluation of the trustworthiness of each node and generally work based on the assumption that the nodes are always honest. In this paper, we propose a decentralised CIDS that emphasises the importance of building trust between CIDS nodes. In our proposed solution, each CIDS node exchanges detection rules to help other nodes detect new types of intrusion. Our architecture offloads the trust computation to the blockchain and utilises a decentralised storage to host the shared trustworthy detection rules, ensuring scalability. Our implementation in a lab-scale testbed shows that the our solution is feasible and performs within the expected benchmarks of the Ethereum platform.
Mohamed Ali, Ibrahim A. Abd El-Moghith, Mohamed N. El-Derini, Saad M. Darwish
Routing is a key function in Wireless Sensor Networks (WSNs) since it facilitates data transfer to base stations. Routing attacks have the potential to destroy and degrade the functionality of WSNs. A trustworthy routing system is essential for routing security and WSN efficiency. Numerous methods have been implemented to build trust between routing nodes, including the use of cryptographic methods and centralized routing. Nonetheless, the majority of routing techniques are unworkable in reality due to the difficulty of properly identifying untrusted routing node activities. At the moment, there is no effective way to avoid malicious node attacks. As a consequence of these concerns, this paper proposes a trusted routing technique that combines blockchain infrastructure, deep neural networks, and Markov Decision Processes (MDPs) to improve the security and efficiency of WSN routing. To authenticate the transmission process, the suggested methodology makes use of a Proof of Authority (PoA) mechanism inside the blockchain network. The validation group required for proofing is chosen using a deep learning approach that prioritizes each node's characteristics. MDPs are then utilized to determine the suitable next-hop as a forwarding node capable of securely transmitting messages. According to testing data, our routing system outperforms current routing algorithms in a 50% malicious node routing scenario.
Binary Attestation (BA) ist ein Sicherheitsmechanismus, der schon lange zur Sicherstellung der Integrität der auszuführenden Software auf einem Computerssystem verwendet wird. Dies wird durch die Anwendung der Konzepte von Trusted Computing Technologie erreicht. Die Konzepte der Trusted Computing Technologie sind: Trusted Platform Module (TPM), measured/trusted boot und Linux Integrity Measurement Architecture (IMA). Damit die Vertrauenswürdigkeit des Betriebszustandes des Systems überprüft werden kann, wird eine Logdatei erstellt. Diese enthält den Dateinamen und die Hash-Summe der Softwarekomponenten, welche an das TPM gebunden wird. Zu einem späteren Zeitpunkt kann eine entfernte Partei (Verifier) die Evidenz verifizieren, um zu sehen, ob der Betriebszustand des Systems vertrauenswürdig ist. Dies wird als Remote Attestation (RA) bezeichnet.Auf den Systemen im Bereich der Cyber-physische Systeme (z. B.: Energie Netze und Kernkraftwerke) und im Bereich der Verkehrsmittel (z. B.: Züge und Automobil Fahrzeuge) laufen mehrere Anwendungen von verschiedenen Herstellern. Bei bisherigen Ansätzen der BA, werden alle Einträge der Logdatei von allen laufenden Anwendungen aller Hersteller während der RA an die entfernte Partei preisgegeben. Daher gewährleistet die Binary Remote Attestation keine Privatheit. Diese Masterarbeit adressiert die genannte Schwäche von der Binary Remote Attestation indem es eine Privacy-Preserving Remote Attestation Ansatz erstellt. Im Kern der Arbeitsteht die Konzeptionierung eines Ansatzes, welches den Trusted Computing Sektor mit Privacy-Enhancing Technologie verknüpft. Der Ansatz bewahrt die Privatheit, indem alle Logeinträge verschleiert werden. Dabei werden dem Verifier nur die jeweilig zugehörigen Einträge offengelegt. Dadurch kann der Attester dem Verifier durch die Anwendung des Schnorr Non-Interactive Zero-Knowledge Proofs über eine elliptische Kurve (Schnorr-Signatur) beweisen, dass es sich bei den ausgeblendeten Einträgen um die tatsächlich laufende Anwendung auf dem System des Attesters handelt, ohne die anderen Einträge preiszugeben. Dieser Ansatz wird in dieser Arbeit als Proof-of-Concept umgesetzt. Die Leistung und die Privatheit des Proof-of-Concepts wurden in dieser Arbeit analysiert. Hierdruch wird gezeigt, dass die Privatheit gegenüber der entfernten Partei gewährleistet wird, während die Integrität und Authentizität des Betriebszustands des Systems vom Attester sichergestellt wird. Die Gewährleistung der Privatheit geht allerdings mit einem Anstieg der benötigten Ressourcen einher.
Operators of networks are striving to provide functional network-based services, while keeping the cost of deploying the service to a minimum. Network Function Virtualization (NFV) is considered to be a promising model to modify such employment by separating network functions from the basic hardware properties, after which they are converted into the style of software. These are eventually referred to as Virtual Network Functions (VNFs). This separation offers numerous benefits, including the decrease of Capital Expenditure (CAPEX) and Operation Expense (OPEX), in addition to the enhanced elasticity of service preparation. Network Functions Virtualization (NFV) is found to cause a remarkable development or even a technological revolution in terms of network-based services, leading to a decrease in deployment costs for network operators. NFV reduces hardware tool costs and energy exhaustion, and it improves its operational performance whereby the network configuration is part of this optimization. Even so, there are a number of possible security problems which are the main focus in NFV. The present study surveys the applications and opportunities of NFV in terms of IoT, SDN, cloud computing and blockchain. A description of the NFV architecture is presented, and several possibilities of NFV security issues and challenges are discussed. Finally, a systematic idea is provided on the design of a Blockchain Network Virtualization System.
Blockchain technology has developed significantly over the last decade. One of the reasons for this is its sustainability architecture, which does not allow modification of the history of committed transactions. That means that developers should consider blockchain vulnerabilities and eliminate them before the deployment of the system. In this paper, we demonstrate a statistical model checking approach for the verification of blockchain systems on three real-world attack scenarios. We build and verify models of DNS attack, double-spending with memory pool flooding, and consensus delay scenario. After that, we analyze experimental results and propose solutions to avoid these kinds of attacks.
The Bitcoin cryptocurrency is a worldwide prevalent virtualized digital currency conceptualized in 2008 as a distributed transactions system. Bitcoin transactions make use of peer-to-peer network nodes without a third-party intermediary, and the transactions can be verified by the node. Although Bitcoin networks have exhibited high efficiency in the financial transaction systems, their payment transactions are vulnerable to several ransomware attacks. For that reason, investigators have been working on developing ransomware payment identification techniques for bitcoin transactions’ networks to prevent such harmful cyberattacks. In this paper, we propose a high performance Bitcoin transaction predictive system that investigates the Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks. Specifically, our system makes use of two supervised machine learning methods to learn the distinguishing patterns in Bitcoin payment transactions, namely, shallow neural networks (SNN) and optimizable decision trees (ODT). To validate the effectiveness of our solution approach, we evaluate our machine learning based predictive models on a recent Bitcoin transactions dataset in terms of classification accuracy as a key performance indicator and other key evaluation metrics such as the confusion matrix, positive predictive value, true positive rate, and the corresponding prediction errors. As a result, our superlative experimental result was registered to the model-based decision trees scoring 99.9% and 99.4% classification detection (two-class classifier) and accuracy (multiclass classifier), respectively. Hence, the obtained model accuracy results are superior as they surpassed many state-of-the-art models developed to identify ransomware payments in bitcoin transactions.
Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data mining has already been present in IT for many decades. Data stored in a blockchain can also be considered to be big data, whereas data mining methods can be applied to extract knowledge hidden in the blockchain. In a nutshell, this paper presents the interplay of these two research areas. In this paper, we surveyed approaches for the data mining of blockchain data, yet show several real-world applications. Special attention was paid to anomaly detection and fraud detection, which were identified as the most prolific applications of applying data mining methods on blockchain data. The paper concludes with challenges for future investigations of this research area.
Exchange of data in networks necessitates provision of security and confidentiality. Most networks compromised by intruders are those where the exchange of data is at high risk. The main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed network. Malicious activities are monitored and detected by the Intrusion Detection System (IDS) that operates with nodes connected to a distributed network. The IDS operates in two phases, where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as Random forest, Convolutional neural network, and XGBoost along with genetic algorithm to improve the performance of IDS. The novel attacks detected in this phase are converted into signatures and exchanged further through the network using the blockchain framework in the second phase. This phase uses the cryptosystem as part of the blockchain to store data and secure it at a higher level. The blockchain is implemented using the Hyperledger Fabric v1.0 and v2.0, to create a prototype for secure signature transfer. It exchanges signatures in a much more secured manner using the blockchain architecture when implemented with version 2.0 of Hyperledger Fabric. The performance of the proposed blockchain system is evaluated on UNSW NB15 dataset. Blockchain performance has been evaluated in terms of execution time, average latency, throughput and transaction processing time. Experimental evidence of the proposed IDS system demonstrates improved performance with accuracy, detection rate and false alarm rate (FAR) as key parameters used. Accuracy and detection rate increase by 2% and 3% respectively whereas FAR reduces by 1.7%.