Ulysse Pavloff, Yackolley Amoussou-Guenou, Sara Tucci-Piergiovanni
In May 2023, the Ethereum blockchain experienced its first inactivity leak, a mechanism designed to reinstate chain finalization amid persistent network disruptions. This mechanism aims to reduce the voting power of validators who are unreachable within the network, reallocating this power to active validators. This paper investigates the implications of the inactivity leak on safety within the Ethereum blockchain. Our theoretical analysis reveals scenarios where actions by Byzantine validators expedite the finalization of two conflicting branches, and instances where Byzantine validators reach a voting power exceeding the critical safety threshold of one-third. Additionally, we revisit the probabilistic bouncing attack, illustrating how the inactivity leak can result in a probabilistic breach of safety, potentially allowing Byzantine validators to exceed the one-third safety threshold. Our findings uncover how penalizing inactive nodes can compromise blockchain properties, particularly in the presence of Byzantine validators capable of coordinating actions.
K. Revathi, S Priyadharshini, R.A Rahul, Gnana Vignesh K · 6 authors
This research study introduces a blockchain-driven solution to revolutionize college credential verification. Addressing the inefficiencies and vulnerabilities of traditional verification methods, the proposed system utilizes blockchain's immutable ledger, smart contracts, and machine learning automation to ensure secure, transparent, and decentralized verification. Results demonstrate its efficacy in fraud prevention, streamlining processes, and providing a reliable, auditable verification platform. This research signifies a transformative shift towards a trustworthy, user-centric credential verification process with implications for students, institutions, and employers seeking streamlined, secure verification of academic records.
Nowadays, the Internet has become one of the basic human needs of professionals. With the massive number of devices, reliability, and security will be crucial in the coming ages. Routers are common to provide us with the internet. These routers can be operated in different modes. Some routers use the Wifi Security Protocol (WPA) or WPA2, and the Wifi Alliance introduced WPA3 on 25 June 2018. There are a lot of papers regarding Smart Contract (SC)–based IDS as well as Machine Learning-based IDS. Very few discuss combining SC and ML-based IDS for different authentication processes. In this paper, we will discuss how combining SC and ML plays a vital role in authentication. Also, we play the role of embedded IDS system so that existing vulnerabilities of the WPA2 and WPA3 can be reduced to 99.62%.
Yepeng Ding, Arthur Gervais, Roger Wattenhofer, Hiroyuki Satō
Decentralized finance (DeFi) is revolutionizing the traditional centralized finance paradigm with its attractive features such as high availability, transparency, and tamper-proofing. However, attacks targeting DeFi services have severely damaged the DeFi market, as evidenced by our investigation of 80 real-world DeFi incidents from 2017 to 2022. Existing methods, based on symbolic execution, model checking, semantic analysis, and fuzzing, fall short in identifying the most DeFi vulnerability types. To address the deficiency, we propose Context-Sensitive Concolic Verification (CSCV), a method of automating the DeFi vulnerability finding based on user-defined properties formulated in temporal logic. CSCV builds and optimizes contexts to guide verification processes that dynamically construct context-carrying transition systems in tandem with concolic executions. Furthermore, we demonstrate the effectiveness of CSCV through experiments on real-world DeFi services and qualitative comparison. The experiment results show that our CSCV prototype successfully detects 76.25% of the vulnerabilities from the investigated incidents with an average time of 253.06 seconds.
In the rapidly evolving domain of the Internet of Things (IoT) and fog computing, maintaining security, scalability, and efficient operation poses significant challenges. Addressing these issues, this study introduces "BlockFog," a novel blockchain-based framework designed to bolster intrusion defense in IoT fog computing environments. The core objective of BlockFog is to counteract the vulnerabilities inherent in decentralized IoT ecosystems by leveraging blockchain technology for enhanced security and transparency. The framework's innovative design integrates crucial components such as Device Onboarding & Identity Management, Data Integrity & Logging, Smart Contract-Driven Intrusion Detection, Automated Blockchain Responses, Secure Peer-to-Peer Communication, and a Lightweight Consensus Mechanism. These elements work collectively to ensure the security and functionality of IoT devices within the fog computing paradigm. BlockFog stands out for its meticulous approach to handling high transaction volumes with off-chain computations and layer-2 solutions, ensuring data integrity and facilitating seamless audit processes. The framework's resilience is further demonstrated through its robust response to evolving cyber threats, incorporating Over-the-Air (OTA) updates and advanced data protection mechanisms like zero-knowledge proofs. A comparative analysis highlights BlockFog's superior performance against existing models. The results reveal BlockFog's lower latency rates in normal, high traffic, and attack scenarios, its higher throughput efficiency, and its more effective resource utilization in terms of CPU, memory, and bandwidth usage. Moreover, BlockFog exhibits an enhanced ability to detect and respond to malicious activities, including DDoS attacks, with significantly higher accuracy than its counterparts. These findings underscore BlockFog's potential in redefining security and operational paradigms in IoT fog computing, making it a robust, agile, and transparent framework suitable for the current digital landscape.
Umamaheswari Rajasekaran, A. Malini, Vandana Sharma
With the rise of social media and high technology utilisation by different age groups, the digital structure arrangement has also changed from being simple node links to the interconnected complex networks. A digital mesh has been introduced with multiple consumers, interconnected webs, cloud storage, ubiquitous applications, and large data sets. These vast sets of networks can be connected to billions of things from devices to sensors and people to autonomous agents as per the requirements providing various services and information. Blockchain technology has automated payments by encouraging cryptocurrencies and introducing a shared and distributed ledger where data, logs, and transactions can be held in a decentralised manner with a more trusted and secure platform. This paper briefs the basic idea that digital mesh is leading the technology rise and how various applications, devices, services, and objects integrate artificial intelligence in them to automate and process the traditional human activities. Further, an overview of some basic features of blockchain is presented along with benefits of its integration with artificial intelligence.
The Internet of Vehicles (IoV) is a crucial technology for Intelligent Transportation Systems (ITS) that integrates vehicles with the Internet and other entities. The emergence of 5 G and the forthcoming 6 G networks presents an enormous potential to transform the IoV by enabling ultra-reliable, low-latency, and high-bandwidth communications. Nevertheless, as connectivity expands, cybersecurity threats have become a significant concern. The issue has been further exacerbated by the rising number of zero-day (0-day) attacks, which can exploit unknown vulnerabilities and bypass existing Intrusion Detection Systems (IDSs). In this paper, we propose Zero-X, an innovative security framework that effectively detects both 0-day and N-day attacks. The framework achieves this by combining deep neural networks with Open-Set Recognition (OSR). Our approach introduces a novel scheme that uses blockchain technology to facilitate trusted and decentralized federated learning (FL) of the Zero-X framework. This scheme also prioritizes privacy preservation, enabling both CAVs and Security Operation Centers (SOCs) to contribute their unique knowledge while protecting the privacy of their sensitive data. To the best of our knowledge, this is the first work to leverage OSR in combination with privacy-preserving FL to identify both 0-day and N-day attacks in the realm of IoV. The in-depth experiments on two recent network traffic datasets show that the proposed framework achieved a high detection rate while minimizing the false positive rate. Comparison with related work showed that the Zero-X framework outperforms existing solutions.
Abstract: The Internet of Things (IoT) paradigm is rapidly transforming various industries by enabling the interconnection of billions of devices. However, the pervasive deployment of IoT devices also introduces significant security challenges, including data integrity, confidentiality, and device authentication. Blockchain technology, initially popularized by cryptocurrencies, has emerged as a promising solution to enhance the security of IoT ecosystems. This paper provides a comprehensive review of the integration of blockchain technology into IoT security frameworks. We explore various blockchain-based security mechanisms, including distributed ledger technology, smart contracts, consensus algorithms, and cryptographic techniques, and analyze their effectiveness in addressing IoT security concerns. Furthermore, we discuss the current state-of-the-art implementations, challenges, and future research directions for leveraging blockchain in IoT security.
Tudor Andreica, Adrian Musuroi, Alfred Anistoroaei, Camil Jichici · 5 authors
The development of Intrusion Detection Systems (IDS) for in-vehicle buses has gained a lot of momentum in recent years as the number of reported vulnerabilities and the degree of interconnectivity for modern vehicles are on the rise. Since intrusion detection is resource consuming, it can be performed on computationally capable Android head units that are now present inside vehicles. Moreover, these units are connected to the internet, which enables the use of more complex algorithms that run in cloud environments. In this work we develop one such approach: an IDS that consists of a locally installed copy, running on head units, and a centralized instance of it that runs in the cloud and monitors traffic for groups of similar vehicles. Additionally, the centralized instance is part of a cloud service for intrusion detection which is continuously updated with the most recent types of attacks. The classification results of the cloud-based service are further analyzed by an incident response team which confirms the presence of known attacks, analyzes new types of attacks and assesses their impact. The output of this activity is stored on the Blockchain as ISO/SAE 21434 compliant reports, ensuring the transparency and traceability of the reported incidents.
Integrating healthcare into traffic accident prevention through predictive modeling holds immense potential. Decentralized Defense presents a transformative vision for combating cyberbullying, prioritizing user privacy, fostering a safer online environment, and offering valuable insights for both healthcare and predictive modeling applications. As cyberbullying proliferates in social media, a pressing need exists for a robust and innovative solution that ensures user safety in the cyberspace. This paper aims toward introducing the approach of merging Blockchain and Federated Learning (FL), to create a decentralized AI solutions for cyberbullying. It has also used Alloy Language for formal modeling of social connections using specific declarations that are defined by the novel algorithm in the paper on two different datasets on Cyberbullying and are available online. The proposed novel method uses DBN to run established relation tests amongst the features in two phases, the first is LSTM to run tests to develop established features for the DBN layer and second is that these are run on various blocks of information of the blockchain. The performance of our proposed research is compared with the previous research and are evaluated using several metrics on creating the standard benchmarks for real world applications.
This review presents a comprehensive analysis of contemporary scholarship pertaining to instant messaging (IM) user behavior and security protocols. Through meticulous selection, the authors highlight critical studies that illuminate optimized message consumption strategies and delve into the evolving landscape of IM security models. Focusing on the past four years, the review meticulously dissects cutting-edge advancements in this domain. A significant insight emerges: achieving optimal communication security necessitates the synergistic convergence of three fundamental techniques: end-to-end encryption for data confidentiality, decentralized authentication for independent user verification, and zero-knowledge proof for identity obscurity. The review postulates that the simultaneous integration of these elements within the application architecture is paramount for robust privacy and heightened security in the realm of IM.
Reentrancy, a notorious vulnerability in smart contracts, has led to millions of dollars in financial loss. However, current smart contract vulnerability detection tools suffer from a high false positive rate in identifying contracts with reentrancy vulnerabilities. Moreover, only a small portion of the detected reentrant contracts can actually be exploited by hackers, making these tools less effective in securing the Ethereum ecosystem in practice. In this paper, we propose BlockWatchdog, a tool that focuses on detecting reentrancy vulnerabilities by identifying attacker contracts. These attacker contracts are deployed by hackers to exploit vulnerable contracts automatically. By focusing on attacker contracts, BlockWatchdog effectively detects truly exploitable reentrancy vulnerabilities by identifying reentrant call flow. Additionally, BlockWatchdog is capable of detecting new types of reentrancy vulnerabilities caused by poor designs when using ERC tokens or user-defined interfaces, which cannot be detected by current rule-based tools. We implement BlockWatchdog using cross-contract static dataflow techniques based on attack logic obtained from an empirical study that analyzes attacker contracts from 281 attack incidents. BlockWatchdog is evaluated on 421,889 Ethereum contract bytecodes and identifies 113 attacker contracts that target 159 victim contracts, leading to the theft of Ether and tokens valued at approximately 908.6 million USD. Notably, only 18 of the identified 159 victim contracts can be reported by current reentrancy detection tools.
Anand Singh Rajawat, S. B. Goyal, Manoj Kumar, Thipendra P. Singh
INTRODUCTION: In this research, we present a novel method for strengthening the security of blockchain networks through the use of AI-driven technology. Blockchain has emerged as a game-changing technology across industries, but its security flaws, particularly in relation to Sybil and Distributed Denial of Service (DDoS) attacks, are a major cause for worry. To defend the blockchain from these sophisticated attacks, our research centres on creating a strong security solution that combines networks of Long Short-Term Memory (LSTM) and Self-Organizing Maps (SOM). OBJECTIVES: The main goal of this project is to create and test an AI-driven blockchain algorithm that enhances blockchain security by utilising LSTM and SOM networks. These are the objectives that the research hopes to achieve: In order to assess the shortcomings and weaknesses of existing blockchain security mechanisms. The goal is to create a new approach that uses LSTM sequence learning and SOM pattern recognition to anticipate and stop security breaches. In order to see how well this integrated strategy works in a simulated blockchain setting against different types of security risks. METHODS: The methods used in our study are based on social network analysis. A combination of support vector machines (SOM) for pattern recognition and long short-term memory (LSTM) networks for learning and event sequence prediction using historical data constitutes the methodology. The steps involved in conducting research are: The current state of blockchain security mechanisms is examined in detail. Creating a virtual blockchain and incorporating the SOM+LSTM algorithm. Putting the algorithm through its paces in order to see how well it detects and defends against different security risks. RESULTS: Significant enhancements to blockchain network security are the primary outcomes of this study. Important results consist of: Using the SOM+LSTM technique, we were able to increase the detection rates of possible security risks, such as Sybil and DDoS attacks. Enhanced reaction times when compared to conventional security techniques for attack prediction and prevention. Demonstrated ability of the algorithm to adapt and learn from new patterns of attacks, assuring long-term sustainability. CONCLUSION: This paper's findings highlight the efficacy of enhancing blockchain security through the integration of artificial intelligence technologies such as LSTM and SOM networks. In addition to improving blockchain technology's detection and forecasting capabilities, the SOM+LSTM algorithm helps advance the platform toward greater security and reliability. This study provides a solid answer to the increasing worries about cyber dangers in the modern era and opens the door to more sophisticated AI uses in blockchain security.
With the advancement of blockchain technology and growing concerns about the vulnerabilities and mistrust in centralized financial services, decentralized finance (DeFi) and decentralized exchanges (DEXs) have emerged as promising alternatives. This paper delves into the challenges and issues within DeFi, with a particular focus on Uniswap. We highlight the susceptibility to Maximal Extractable Value (MEV) attacks, providing a background on the current state of DeFi and DEXs. Our approach includes a detailed transaction analysis on Uniswap to identify and analyze MEV attack patterns, alongside a method for detecting bots. The results offer critical insights into the nature of various attacks in DEXs and the correlation between internal and external blockchain events and MEV attack patterns. This research provides valuable guidelines for enhancing DEX security and mitigating MEV risks, serving as an essential resource for stakeholders in the DeFi ecosystem.
Kainat Ansar, Mansoor Ahmed, Saif Ur Rehman Malik, Markus Helfert · 5 authors
Context Data breaches caused by insiders are on the rise, both in terms of frequency and financial impact on organizations. Insider threat originates from within the targeted organization and users with authorized access to an organization’s network, applications, or databases commit insider attacks. Motivation Insider attacks are difficult to detect because an attacker with administrator capabilities can change logs and login records to destroy the evidence of the attack. Moreover, when such a harmful insider attack goes undetected for months, it can do a lot of damage. Such data breaches may significantly impact the affected data owner’s life. Developing a system for rapidly detecting data breaches is still critical and challenging. General Data Protection Regulation (GDPR) has defined the procedures and policies to mitigate the problems of data protection. Therefore, under the GDPR implementation, the data controller must notify the data protection authority when a data breach has occurred. Problem Statement Existing data breach detection mechanisms rely on a reliable third party. Because of the presence of a third party, such systems are not trustworthy, transparent, secure, immutable, and GDPR-compliant. Contributions To overcome these issues, this study proposed a GDPR-compliant data breach detection system by leveraging the benefits of blockchain technology. Smart contracts are written in Solidity and deployed on a local Ethereum test network to implement the solution. The proposed system can generate alert notifications against every data breach. Results We tested and deployed our proposed system, and the findings indicate that it can accomplish the insider threat mitigation objective. Furthermore, the GDPR compliance analysis of our system was also evaluated to make sure that it complies with the GDPR principles (such as right to be forgotten, access control, conditions for consent, and breach notifications). The conducted analysis has confirmed that the proposed system offers capabilities to comply with the GDPR from an application standpoint.
The disaster recovery mechanism is employed in various industries but in critical infrastructure scenarios, the process should be reliable and resilient. This research paper proposes a novel framework for disaster recovery methods for as Supervisory control and data acquisition systems using communication channels created by blockchain technology. The permission blockchain technology is integrated with as Supervisory control and data acquisition devices for configuring the data and event-sharing techniques. The research work presents the process of configuring and implementing the disaster recovery process of risk assessment, backup creation and event logging. Further, the events in the critical infrastructure are controlled by smart contract automation as part of the multichain blockchain. The consensus for fault tolerance is achieved by keeping 90% of the nodes operational in the network. The proposed solution is simulated by creating a private communication channel among as Supervisory control and data acquisition devices and the performance is evaluated with successful execution rate and average time of block confirmation by the disaster recovery process. The mining diversity, number of required active nodes, creation of stream and creation of distributed ledger is controlled by single administrative node in the proposed architecture which can be configured according to the requirements of the SCADA network architecture.
Due to the rapid advancement of cyber technology, virtual property is becoming more susceptible to hostile activity. Security structures that are mostly built on block chains (BCSSs) have gained traction. Disbursed ledger structures protected by encryption and consensus procedures are checked by BCSSs to protect industries against cyber attacks, data loss, and unwanted access. They provide users with a distributed ledger machine that is encrypted, shared, and may be used to manage virtual risks and digital property. It follows objectives to investigate how BCSSs affect cyber security. One way to perform a qualitative synthesis of the literature is to examine and analyze relevant academic materials and publications that discuss the challenges and advantages faced by BCSSs in the field of cyber security. The evaluation's findings will help determine the overall effect of BCSS on cyber security and offer recommendations for BCSS setups that will improve cyber security defense. To identify gaps in current cyber security measures, all findings will be compared with existing studies. It will provide invaluable analysis and insights into how best to use BCSSs for increased cyber security in comparison to traditional security architectures. When enforcing BCSSs, it will also recognize remarkable practices and tactics for businesses and professional clients.
This paper proposes a novel hybrid method to increase trust and privacy in the Onion Router (Tor) network by integrating blockchain technology and Zero Knowledge Proofs (ZKPs). Leveraging principles from trust-based anonymous communication, the proposed method aims to establish a decentralized trust layer within the Tor network, enhancing integrity and reliability. By incorporating ZKPs’ power of privacy and authentication, blockchain's immutability and Tor's anonymity, the method seeks to address trust and privacy concerns within the Tor network. This hybrid approach offers a comprehensive solution to increase trust and privacy in the Tor network, aligning with their growing importance in the digital age.
Internet Traffic Analysis and Secure E-voting
Network Security and Intrusion Detection
Advanced Steganography and Watermarking Techniques
Collaborative cybersecurity relies on organizations sharing information to boost security, but trust management is a key concern. Decentralized solutions like distributed ledgers, particularly blockchain, are crucial for eliminating single points of failure. However, the existing literature on blockchain-based collaborative cybersecurity is limited, lacking comprehensive insights. This paper addresses this gap by surveying blockchain's role in collaborative cybersecurity from 2016 to 2023. It explores various applications, trends, and the evolution of blockchain technology, focusing on access control, data validation policies, underlying tech, and consensus mechanisms. A key finding is the fragmentation of the field with no dominant research group or venue. Many recent projects poorly select consensus protocols for their blockchain. To aid researchers and practitioners, this paper offers guidelines for choosing the right blockchain for specific purposes and highlights open research areas and lessons learned from past blockchain applications in collaborative cybersecurity, encouraging further exploration in this field.