In response to the Cybersecurity Law, organizations face numerous management and technical requirements. Detection techniques such as vulnerability scanning and penetration testing are employed to identify risks. Addressing these vulnerabilities demands substantial manpower, time, and financial resources. Security concerns also arise during digital file transmission and remediation efforts. This study proposes a security detection platform with step-by-step implementation guidelines, enabling resource-limited units to replicate the setup and address security gaps. It compares detection results between open-source and commercial tools, highlighting key differences and offering remediation strategies. Numerous digital files (e.g., test reports) are generated during testing. To ensure secure storage and sharing, the system integrates IOTA’s distributed ledger and IPFS, generating HASH values and uploading files on-chain to preserve integrity and authenticity. The objective is to deliver a scalable, cost-effective security detection framework that enhances system resilience while minimizing resource consumption.
Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks.Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements.Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures.To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method.Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure.Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16KB or more, outperforming state-of-the-art methods. CCS Concepts Security and privacy File system security; Management and querying of encrypted data.
May 7, 2025·2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)
S. Neelavathy Pari, M. D. Anto Praveena, Ms. S. Kaaviya, Ms. S. Kanishka
The Internet of Things (IoT) has transformed various industries by enabling seamless connectivity among smart devices, but its open nature exposes it to security vulnerabilities. Blockchain technology, with its decentralized and immutable properties, offers a promising solution to enhance IoT security. However, existing anomaly detection approaches in IoT networks face limitations such as high false positives, scalability issues, and lack of real-time threat mitigation. To address these challenges, this research integrates the Isolation Forest algorithm with a blockchain-based smart contract for efficient anomaly detection. The Isolation Forest algorithm is used to classify network anomalies by classifying normal and abnormal traffic patterns, while smart contracts detect anomalies in real-time network traffic, ensure data integrity, automate threat responses, and provide tamper-proof logging. Experimental evaluations demonstrate the effectiveness of this approach, achieving improved accuracy of 95 percent along with other measures such as precision, recall, and F1-score also achieving good results compared to other traditional methods. The proposed framework enhances IoT security by reducing false alarms, increasing detection sensitivity, and enabling real-time threat identification, making it a scalable and robust solution for modern IoT environments.
This research article introduces a deep learning (DL) for identifying vulnerabilities in the smart contracts, leveraging an optimized DL method. The proposed method, termed LogT BiLSTM, combines bidirectional long short-term memory (BiLSTM) with logistic chaos Tasmanian devil optimization (LogT) for enhancing detection of vulnerability. The evaluation of the suggested approach is conducted using publicly available datasets. Initially, preprocessing steps involve removing duplicate data and imputing missing data. Subsequently, the vulnerability detection process utilizes BiLSTM, with the optimization of the loss function achieved through LogT. Results indicate promising performance in identifying vulnerabilities in SC, highlighting the efficacy of the LogT-BiLSTM approach.
The integration of AI and DLT presents great opportunities and unique challenges in the realm of cybersecurity. This paper will examine how AI strengthens the processes of detection and response in cybersecurity, while DLT presents a decentralized and immutable premise for data management. The convergence of these two technologies may improve security for several sectors; finance, healthcare, and supply chain being prominent examples. AI's nature of being able to analyze large masses of data in the quest for threat anomaly detection complements DLT's decentralization and immutability as it affords more resilience in cybersecurity. Integration fosters technical and ethical challenges as well as regulations that need to be overcome in order for these technologies to be effective. This paper discusses the integrations of AI and DLT: Some of the best case studies and their future potential for augmenting cybersecurity protocols are also explained.
The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data exchange, revolutionizing various industries. However, the inherent vulnerabilities in traditional centralized transaction systems pose significant security challenges, particularly when dealing with sensitive data generated by IoT devices. This paper introduces an ICAA (Integrity Consensus Authorization Algorithm) for securing transaction records over the IoT network by leveraging Decentralized Distributed Ledger Technology (DDL), integrating the PICA (Proof-of-Integrity Consensus Algorithm) and CTAP (Context-Aware Transaction Authorization Protocol). The proposed system addresses the limitations of centralized architectures by employing a decentralized ledger, ensuring transparency, immutability, and tamper-resistant transaction records. The Proof-of-Integrity Consensus Algorithm enhances the security of the network by validating and confirming transactions based on the integrity of the data stored in the distributed ledger. This consensus mechanism minimizes the risk of fraudulent activities and unauthorized modifications, making it well-suited for the dynamic and distributed nature of IoT environments. Furthermore, the integration of the Context-Aware Transaction Authorization Protocol enhances the adaptability of the system to the diverse contexts in which IoT devices operate. The synergy between the Proof-of-Integrity Consensus Algorithm and the Context-Aware Transaction Authorization Protocol creates a comprehensive and secure framework for managing transaction records in IoT networks. . The proposed HGGC is 5.026% better than the ECMQV-MAC, 0.4215% better than QKD, and 0.0843% better than OTP in the nodes 200. The proposed model contributes to the establishment of a trustworthy and resilient infrastructure for the IoT, laying the foundation for secure and transparent transactions in the connected world.
Mohammed Ibraheem Hussein, Ohood Saadoon Hlail, Asma Ibrahim Hussein, Amjed Abbas Ahmed · 6 authors
Balancing efficient threat detection with data privacy becomes increasingly difficult as cyber threats develop in complexity. The Adaptive Zero-Knowledge Threat Hunting Framework (AZTH), a revolutionary integration of zero-knowledge proofs (ZKP) and artificial intelligence (AI) for private and secure cybersecurity operations, is presented in the presented study. AZTH maintains strong confidentiality regarding sensitive data yet uses federated learning, quantum-resistant cryptography, and dynamic deception systems to improve threat intelligence sharing as well as real-time threat mitigation. Together with an assessment of its efficacy in several operating situations, the architecture, approach, and possible uses of the framework are given.
As more organizations move to use the multi-tenant cloud infrastructure, the perimeter-based security model is insufficient for the concept of zero-trust security states. Thatently, curing this complex environment, It has “never trust, always verify”. Completely contradicting the conventional models, Zero Trust continually promotes authentication and validation of every access request (inside or outside the network perimeter). As they try to understand how to protect the isolation of tenants, stop alteration movements, and support identity cross services, the paper investigates the challenges and parts of zero trust taking effect in the multi-tenant cloud. Everything must always be authenticated, no matter the connection status, to ensure the user (only the user) has permission to do all the things they need. Further, it shows that Artificial Intelligence (AI) and Machine Learning (ML) technologies can highly enhance the detection of threats and adaptive access control. It shall see an exhibited case study of a SaaS provider going from providing limited risk mitigation against these risks, such as credential stuffing, API abuse, and insider data leakage, to Zero Trust security. This paper discusses decentralized identity (DID), post-quantum cryptography, blockchain as immutable audit trails, and AI-led autonomous zero trust systems as some of the future emerging trends. As the world reaches the multi-tenant cloud architecture, they are ready to enhance cloud security further.
Jinish Patel, Joseph Reiner, Brenden Stilwell, Abdullah Wahbeh · 5 authors
With the growing popularity of cryptocurrencies, detecting potential market manipulation and fraudulent activities has become crucial for maintaining market integrity. In this study, we aim to detect anomalous Bitcoin transactions using an integrated approach by combining clustering techniques with statistical outlier detection. More specifically, anomalies were detected using three approaches: a distance-based method, flagging points with distances greater than the 95th percentile from their cluster centers; a statistical method, identifying transactions with any feature having an absolute Z-score greater than 3; and a hybrid approach, where transactions flagged by either method were considered anomalous. Using sample subset Bitcoin transaction data from 2015, our results showed that the combined approach was able to achieve the best performance with a total of 6492 (6.61%) detected anomalous transactions out of a total of 98,151 transactions.
In recent years, a large number of on-chain attacks have emerged in the blockchain empowered Web3 ecosystem. In the year of 2023 alone, on-chain attacks have caused losses of over 585 million. Attackers use blockchain transactions to carry out on-chain attacks, for example, exploiting vulnerabilities or business logic flaws in Web3 applications. A wealth of efforts have been devoted to detecting on-chain attack transactions through expert patterns and machine learning techniques. However, in this ever-evolving ecosystem, the performance of current methods is limited in detecting new on-chain attacks, due to the obsoleting of attack recognition patterns or the reliance on on-chain attack samples. In this paper, we propose a universal approach for detecting on-chain attacks even when there are few or even no new on-chain attack samples. Specifically, an in-depth analysis of the transaction characteristics is conducted, and we propose a new insight to train a generic attack transaction detecting model, i.e., transaction reconstruction. Particularly, to overcome the over-fitting in the transaction reconstruction task, we use the web-scale function comments related to transactions as supervision information, rather than expert-confirmed labels. Experimental results demonstrate that the proposed approach surpasses the supervised state-of-the-art by 13% in AUC, with just 30 known on-chain attack samples. Moreover, without any known attack samples, our method can still detect new on-chain attacks in the wild (with a precision of 61.83%). Among attacks detected in the wild, we confirm 1,692 address poisoning attacks, a new type of on-chain attack targeting token holders. Our code is available at: https://github.com/wuzhy1ng/attack_trans_detection_www25.
C. Aparna, S. Radha, C. Aarthi, K. M. Karthick Raghunath
ABSTRACT Mobile Ad hoc networks (MANETs) are key for applications in which flexibility and organization are paramount, but the security of such networks entails threats that can exploit the vulnerability of their open architecture, resulting in various attacks. To address such issues, a novel architectural framework is always required. One such framework is introduced, namely, the HoneyFed Secure Architecture (HFSA), which provides the combination of an advanced honey encryption system with federated learning‐based decentralized security to improve the security of MANET. Honey encryption, on the other hand, employs adaptive deception techniques to generate plausible decoy data on decryption failure, employs dynamic key management for tamper resistance, and provides perfect authentication through multi‐factor methods and zero‐knowledge proofs. We found that federated learning offers decentralized model training, where nodes jointly train local models while exchanging progress updates without exposing raw data, enabling 81.4% more detections of emerging threats while preserving data privacy. Using the proposed HFSA approach achieves a 78% protection improvement against attacks and a 71% reduction in unauthorized access. HFSA offers a robust and scalable framework of security that uses continuous learning and adaptation to the vulnerabilities of the MANETs to enhance network resilience.
The DC-Microgrids (DC-MGs) are increasingly prone to various cyber-attacks due to the advancement of intelligent controlling, monitoring, operation methods. A typical DC-MGs integrates components like batteries, super capacitors, electronic devices, Photovoltaic (PV) systems, and loads. Given these vulnerabilities, cyber-attack detection, and the security of data exchanged in smart DC-MGs, similar to Cyber-Physical Systems (CPS), have become critical areas to focus. This paper proposes a novel approach to detect false data injection attack (FDIAs) in DC-MGs using Wavelet transform and Support Vector Machines (SVMs) with Blockchain technology. The analysis shows that the output voltage dropped from 350 V to 300 V during the False Data Injection Attack (FDIA) at 0.4 s and returned to 350 V by 0.7 s. Significant oscillations observed between 0.4 and 0.7 s and detection model achieved 400 true negatives, 191 true positives, 10 false negatives, and no false positives, demonstrating high accuracy in identifying FDIA instances.
The rapid adoption of cloud computing has transformed how organizations store, process, and manage data, shifting from centralized infrastructures to highly distributed environments. This evolution has necessitated a parallel advancement in cloud security strategies to address emerging threats, regulatory demands, and architectural complexities. Initially, cloud security relied on perimeter-based defenses, such as firewalls and VPNs, which proved insufficient as architectures evolved toward hybrid, multi-cloud, and edge computing models. Modern security paradigms now emphasize Zero Trust principles, data-centric protection, and DevSecOps integration, ensuring security is embedded throughout the development lifecycle. Additionally, advancements in AI-driven threat detection, encryption technologies, and identity management have become critical in safeguarding distributed workloads. However, challenges persist, including securing serverless and containerized environments, mitigating supply chain risks, and preparing for post-quantum cryptography. Future trends point toward autonomous security systems, confidential computing, and decentralized identity solutions, reinforcing the need for adaptive, intelligent security frameworks. This paper explores the evolution of cloud security, analyzing past approaches, current best practices, and future directions to ensure robust data protection in an increasingly decentralized digital landscape. Keywords: Cloud Security, Zero Trust, Data-Centric Security, DevSecOps, AI in Cybersecurity, Distributed Environments
Philip Eappen, Adeyemi Abel Ajibesin, Narasimha Rao Vajjhala
This chapter examines future directions and emerging trends in cybersecurity for knowledge management, emphasizing the necessity of advanced protective strategies as organizations increasingly rely on digital platforms to store, share, and manage knowledge. While digital knowledge management offers efficiency and accessibility, it introduces significant security challenges, such as unauthorized access, data breaches, and knowledge theft. This chapter explores three critical emerging technologies—Artificial Intelligence/Machine Learning (AI/ML), Blockchain, and Quantum Computing—that have the potential to enhance knowledge security. AI/ML aids in threat detection, predictive modeling, and automated responses, allowing for rapid identification of cyber threats. Blockchain provides a decentralized, tamper-proof method for managing knowledge and data integrity, ensuring secure data sharing and transaction transparency. Additionally, the chapter discusses quantum-resistant encryption in response to the potential risks posed by quantum computing advancements. The chapter further examines advanced cybersecurity approaches, including Zero Trust Architecture, Privacy-Enhancing Technologies, and Security Automation, which contribute to safeguarding sensitive information in knowledge systems. This chapter also addresses the ethical and regulatory considerations essential for ensuring compliance and accountability. By integrating these cutting-edge technologies and approaches, this chapter provides a holistic framework for future cybersecurity practices in knowledge management, offering valuable insights for professionals, researchers, and policymakers focused on protecting digital knowledge assets in an evolving cyber landscape.
Federated learning (FL) has emerged as a leading methodology for facilitating collaborative edge learning (EL) across Artificial Intelligence of Things (AIoT) devices, enabling efficient model training and bolstering privacy protection. Nevertheless, current EL methods that depend on trusted servers engender apprehensions concerning potential data leakage and misuse. Moreover, the untrusted AIoT environment increases security threats in EL collaboration. In addressing these challenges, we introduce an innovative swarm reputation (SR)-based decentralized autonomous organization (DAO) autonomous FL framework, SRFL. Within SRFL, we utilize DAO nodes as autonomous units for processing local services, effectively diminishing the communication overhead attributed to frequent interactions, the SR-based DAO committee oversees the FL process and ensures model consistency. SRFL seamlessly integrates FL with the distributed consensus process and introduces an SR-based consensus mechanism to enhance the collaboration process’s trustworthiness. SR utilizes a hierarchical reward and punishment mechanism, designed to equitably reward honest participants and hammer penalize those undermining the system’s stability. Through extensive experimentation with SRFL, employing different models and datasets, we have substantiated its superior performance in efficiency and robustness.
Nasir Nadeem, Ahmad Ramli Saad, Zeeha Aslam, Zohaa Naveed
The purpose of this study is to assess this form of technocrimeand identify the misinformation gaps to restrict area suggestions, educational offer frameworks, and legislative proposals aimed at advancing the digital financial literacy of prospective young investors. The research aims to highlight how these scams affect multilateral financial inclusion, economic empowerment, and a reliable digital financial ecosystem. These schemes are targeted at university students who possess low financial literacy and are lured by the prospects of easy money, which endanger their lives in the long run. This descriptive research is based on an online survey conducted among students of Multan, using simple random sampling, collected through an online survey. The study will analyze the relationship between financial literacy and victimization in order to test the hypothesis that those with lower literacy are more susceptible. The research will also look into the disinformation marketing and recruitment strategies on social media and other Internet platforms regarding cryptocurrency. The study aids in accomplishing SDG 8: Decent Work and Economic Growth within the context of Pakistan’s digital economy. This research helps to understand the contribution of technocrime to the obstacles of financial inclusion and helps to develop a strongerdigital economic infrastructure proposal.
M. K. Ghosh, Chirag Dinesh Jain, Raju Halder, Joydeep Chandra
Phishing scams on the Ethereum network have become a serious threat, especially with the influx of new users into the cryptocurrency market. Current detection methods are mainly focused on long-term consistent transaction patterns with smooth temporal dynamics. However, these methods often struggle to differentiate between phishing and non-phishing users, whose behaviours may appear deceptively similar. Additionally, they face challenges such as network sparsity and data leakage, leading to significant performance limitations. To address these issues, we introduce TEMPER, a novel sequential learning framework designed to jointly capture the subtle distinctions between long- and short-term user behaviours and their correlations to provide more comprehensive insights. TEMPER effectively generates distinguishable user embeddings, enabling the accurate identification of phishing users. Unlike previous approaches, TEMPER mitigates data leakage through a novel sequential transaction sampling algorithm and addresses network sparsity with short-term temporal learning. Through extensive experimentation on three real-world Ethereum datasets, TEMPER demonstrates its efficacy by achieving a 3-4% improvement in the F1-Score compared to existing baseline models, representing a significant advancement in Ethereum phishing user detection.
ABSTRACT IoT is a rapidly developing technology with a wealth of creative application possibilities. However, IoT wireless sensor networks are vulnerable to Denial of Service (DoS) attacks due to their insecure nature. Although network integrity and security have been ensured through the use of distributed ledger and blockchain technologies, privacy preservation concerns frequently arise with traditional approaches. So, deep learning‐based Physics‐informed neural networks (PINN) and Honesty‐based Distributed Proof‐of‐Authority (HDPoA) are developed to enhance transaction security and detect intrusions. Initially, the mobile nodes were deployed in different regions to gather transactions and an intrusion detection system to analyze attacks. First, the Intrusion Detection System (IDS) uses a deep learning approach for detecting the intrusion in the network. For that, the collected data from the deployed nodes are pre‐processed using Variational auto‐encoder and min‐max normalization to standardize input dataset values. Then the features are selected using wild horse optimization and classified using PINN to predict data attack or non‐attack. After that, Homomorphic variable tag generation is used for normal transactions with multiple copies in the same document, which are then converted into hash values using the Keccak hashing function. The miner validates transactions based on rank‐based priority. Honesty‐based Distributed Proof‐of‐Authority (HDPoA) was used for network security, making it suitable for deployment in blockchain‐based IoT applications. The proposed deep learning‐based PINN classifier reached 97.2% accuracy and 96.52% specificity. Homomorphic variable (HV) tag generation takes 0.4 s, while the Keccak algorithm takes 0.3 s for hash generation, and the HDPoA protocol has 420 s for block generation time.
ABSTRACT Ensuring the security and privacy of sensitive health data in Internet of Things (IoT)‐based healthcare systems (HCS) is a critical challenge. This paper proposes a robust security framework by integrating blockchain mechanisms and deep learning (DL) approaches to enhance security and data privacy. The proposed framework leverages the Ethereum blockchain with zero knowledge proof (ZKP) to ensure data integrity and confidentiality, while the interplanetary file system (IPFS) provides secure and efficient data storage. Additionally, a novel At‐GAN‐BiLSTM model is introduced for intrusion detection by combining the attention mechanism, generative adversarial networks (GAN) and bidirectional long short‐term memory (Bi‐LSTM) to improve detection accuracy and also help to enhance model robustness. The proposed model is evaluated by two different benchmark datasets, namely CICIDS‐2018 (D1) and ToN‐IoT (D2), achieving accuracies of 99.9% and 99.1%, respectively. Comparative investigation shows that the proposed approach reduces false alarm rates (FAR) and performs better than current models in identifying impersonation, insider, and man‐in‐the‐middle (MITM) attacks. By integrating blockchain and DL, the proposed framework significantly enhances intrusion detection, data security, and overall system resilience, addressing key vulnerabilities in IoT‐based healthcare security.
With the growth of the Internet of Things (IoT), millions of users, devices, and applications compose a complex and heterogeneous network, which increases the complexity of digital identity management. Traditional centralized digital identity management systems (DIMS) confront single points of failure and privacy leakages. The emergence of blockchain technology presents an opportunity for DIMS to handle the single point of failure problem associated with centralized architectures. However, the transparency inherent in blockchain technology still exposes DIMS to privacy leakages. In this paper, we propose the privacy-protected IoT DIMS (PPID), a novel blockchain-based distributed identity system to protect the privacy of on-chain identity data. The PPID achieves the unlinkability of identity-credential-verification. Specifically, the PPID adopts the Zero Knowledge Proof (ZKP) algorithm and Shamir secret sharing (SSS) to safeguard privacy security, resist replay attacks, and ensure data integrity. Finally, we evaluate the performance of ZKP computation in PPID, as well as the transaction fees of smart contract on the Ethereum blockchain.
Open access
Blockchain Technology Applications and Security
Network Security and Intrusion Detection
Advanced Steganography and Watermarking Techniques
Machine learning (ML) based network attack traffic detection is an emerging security paradigm, which is capable of capturing various advanced network attacks according to the features of traffic. When leveraging such promising security application to protect P2P services, particularly distributed cryptocurrency systems, one detection model should be deployed on many nodes to handle various unseen traffic patterns generated by nodes around the world. However, unseen yet benign traffic patterns are commonly classified as attack traffic, and thus trigger massive false-positive (FP) alarms. Unfortunately, the common practice of retraining models to reduce FPs is not salable for large-scale P2P networks, which incurs prohibitive labor efforts of collecting traffic on each node individually. To effectively deploy ML based attack traffic detection systems to protect distributed networks, we present tNeuron that automatically identifies FPs triggered by unseen traffic via neuron activation pattern analysis, such that it significantly improves the performance on various nodes. Specifically, we construct a shadow model with Transformer encoders to extract the knowledge of traffic patterns. Afterward, we train a model that learns how to classify FPs among alarms raised by ML models according to neuron activation patterns of the shadow model. Our experiments on real Ethereum nodes show that tNeuron can reduce 83.40% FP for seven state-of-the-art ML based attack detection systems, when detecting 15 kinds of P2P network attacks, thereby significantly improving detection accuracy in nine different metrics. In addition, tNeuron is robust against various adversarial examples constructed by existing evasion attacks. Besides, it achieves real-time detection and is capable of handling massive FPs generated by many nodes in large-scale distributed networks.
Blockchain networks have become a cornerstone of decentralized finance and digital asset management, yet they remain susceptible to fraudulent activities, money laundering, and illicit financial transactions. Traditional anomaly detection methods, including rule-based systems and supervised machine learning models, often struggle to generalize across evolving blockchain transaction patterns due to their reliance on static heuristics and manually engineered features. Graph-based learning techniques offer a more robust approach by leveraging the inherent structure of blockchain transactions, where wallets and transactions form a dynamic graph.This study proposes a novel Spatial-Temporal Graph Neural Network (STGNN)-based anomaly detection framework for blockchain transactions. By modeling transaction flows as evolving graphs, the proposed system captures both spatial dependencies between wallets and temporal patterns in transaction sequences. The framework employs Graph Convolutional Networks (GCN) or Graph Attention Networks (GAT) to extract spatial representations, while Gated Recurrent Units (GRU) or Temporal Convolutional Networks (TCN) model the time-dependent evolution of transaction behaviors. The fusion of these spatial-temporal features enables the detection of anomalous transactions that deviate from expected network behaviors.Experimental evaluations on real-world blockchain datasets demonstrate that the STGNN-based model achieves higher detection accuracy, lower false positive rates, and better adaptability than traditional fraud detection techniques. The study further explores the system's scalability and generalization across different blockchain networks, revealing its potential for real-time monitoring of illicit financial activities. These findings highlight the effectiveness of graph-based deep learning models in strengthening blockchain security and provide a foundation for future research in decentralized fraud detection, anti-money laundering (AML) compliance, and intelligent financial surveillance.