Grievance redressal has always been vital for any organization to maintain a good work environment for its stakeholders. Some organizations follow online portals, websites, or mobile applications to register grievances to provide more privacy to the complainant’s identity. However, online platforms provide better solutions to the existing manual methods for grievance redressal. Still, there are a lot of issues and challenges associated with them. This research has comprehensively analyzed the existing grievance redressal systems to identify and discuss all the challenges. After comprehensive analysis, it is found that presently there are several issues such as delayed response, opaque processes, biases, complexity and accessibility issues, lack of personalization, and other privacy and security concerns associated with existing grievance redressal methods. To address all these issues this study is proposing a blockchain-based solution for grievance redressal systems. The proposed solution will be a blockchain-based web and mobile application that consists of multiple entities such as complainants, redressal committee, and higher authorities. This system will provide the necessary privacy and confidentiality to the complainants through the immutable distributed ledger technology and auditability of the entire process with complete transparency.
In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques. Nevertheless, most current machine learning techniques depend on sufficient expert knowledge and solely focus on addressing well-known vulnerabilities. This paper puts forward a systematic literature review (SLR) of existing machine learning-based frameworks to address the problem of vulnerability detection. This SLR follows the PRISMA statement, involving a detailed review of 55 papers. In this context, we classify recently published algorithms under three different machine learning perspectives. We explore state-of-the-art machine learning-driven solutions that deal with the class imbalance issue and unknown vulnerabilities. We believe that algorithmic-level approaches have the potential to provide a clear edge over data-level methods in addressing the class imbalance issue. By emphasizing the importance of the positive class and correcting the bias towards the negative class, these approaches offer a unique advantage. This unique feature can improve the efficiency of machine learning-based solutions in identifying various vulnerabilities in SCs. We argue that the detection of unknown vulnerabilities suffers from the absence of a unique definition. Moreover, current frameworks for detecting unknown vulnerabilities are structured to tackle vulnerabilities that exist objectively.
This comprehensive analysis delves into the intricate impact of cryptocurrency adoption on traditional banking systems. As cryptocurrencies achieve widespread acceptance, they challenge established norms of centralized control in financial transactions, necessitating a reconsideration of the resilience and adaptability of traditional banking models. The disruptive potential, particularly embodied in blockchain technology, prompts a critical evaluation of the ongoing transformation. The article scrutinizes the positive influence of cryptocurrencies on financial inclusion and accessibility, unlocking banking services for previously underserved populations. However, the decentralized and pseudonymous nature of cryptocurrencies introduces regulatory challenges, demanding a nuanced equilibrium between fostering innovation and ensuring compliance with anti-money laundering and know your customer regulations. Traditional banks respond by embracing blockchain technology, entering collaborative endeavors with cryptocurrency projects to augment operational efficiency and transparency. Nevertheless, the inherent volatility of cryptocurrencies poses systemic risks, necessitating adept navigation by traditional banking systems. The imperative for a delicate equilibrium between innovation and regulation emerges as pivotal for the harmonious coexistence of traditional banking and the ever-evolving cryptocurrency ecosystem. As the financial landscape undergoes profound changes, this analysis underscores the necessity for adaptability and a strategic alignment between traditional and innovative financial paradigms.
Ruichao Liang, Jing Chen, Cong Wu, Kun He · 9 authors
Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent years, causing massive financial losses. Existing detection methods primarily focus on rule-based approaches and machine learning techniques that utilize static information as features. However, these methods have significant limitations. Rule-based approaches rely on pre-defined rules with limited capabilities and domain knowledge dependency. Using static information like opcodes for machine learning fails to effectively characterize Ponzi contracts, resulting in poor reliability and interpretability. Our research shows no significant difference between Ponzi and non-Ponzi contracts at the opcode level. Moreover, relying on static information like transactions for machine learning requires a certain number of transactions to achieve detection, which limits the scalability of detection and hinders the identification of 0-day Ponzi schemes. In this article, we propose PonziGuard , an efficient Ponzi scheme detection approach based on contract runtime behavior. Inspired by the observation that a contract’s runtime behavior is more effective in disguising Ponzi contracts from the innocent contracts, PonziGuard establishes a comprehensive graph representation called contract runtime behavior graph (CRBG), to accurately depict the behavior of Ponzi contracts. Furthermore, it formulates the detection process as a graph classification task on CRBG, enhancing its overall effectiveness. The experiment results show that PonziGuard surpasses the current state-of-the-art approaches in the ground-truth dataset, achieving a precision of 96.9%, recall of 98.2%, and F1-score of 97.5%. It also exhibits the highest level of interpretability among the current tools. We applied PonziGuard to Ethereum Mainnet and demonstrated its effectiveness in real-world scenarios. Using PonziGuard , we identified 805 Ponzi contracts on Ethereum Mainnet, which have resulted in an estimated economic loss of 281,700 Ether or approximately \($\) 500 million USD. We also found 0-day Ponzi schemes in the recently deployed 10,000 smart contracts.
S. Harihara Gopalan, A. Manikandan, N. P. Dharani, G. Sujatha
Abstract The proposed Blockchain-Based Mitigation of Deauthentication Attacks (BBMDA) Framework aims to enhance the security and trustworthiness of IoT environments by leveraging blockchain technology, the Elliptic Curve Digital Signature Algorithm (ECDSA) for secure authentication, and Multi-Task Transformer (MTT) for efficient traffic classification. This paper presents a novel approach to mitigate de-authentication attacks in IoT ecosystems. The research methodology involves developing and implementing the BBMDA framework, followed by a comprehensive evaluation and comparison with existing techniques. Key findings indicate that the BBMDA framework outperforms traditional methods such as Support Vector Machine (SVM), k-nearest Neighbors (KNN), and Convolutional Neural Network (CNN) in terms of accuracy, false positive rate, false negative rate, precision, recall, and F1-score. These results underscore the effectiveness and efficiency of the proposed framework in enhancing IoT security.
Izdehar M. Aldyaflah, Wenbing Zhao, Shunkun Yang, Xiong Luo
Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities in smart contracts. Furthermore, various ways of encoding the smart contracts for analysis have also been proposed. However, the impact of these input methods has not been systematically studied, which is the primary goal of this paper. In this preliminary study, we experimented with four common types of input, including Word2Vec, FastText, Bag-of-Words (BoW), and Term Frequency–Inverse Document Frequency (TF-IDF). To focus on the comparison of these input types, we used the same deep-learning model, i.e., convolutional neural networks, in all experiments. Using a public dataset, we compared the vulnerability detection performance of the four input types both in the binary classification scenarios and the multiclass classification scenario. Our findings show that TF-IDF is the best overall input type among the four. TF-IDF has excellent detection performance in all scenarios: (1) it has the best F1 score and accuracy in binary classifications for all vulnerability types except for the delegate vulnerability where TF-IDF comes in a close second, and (2) it comes in a very close second behind BoW (within 0.8%) in the multiclass classification.
[Purpose] This article addresses the lack of legislation in India to govern digital currencies, as well as the legality of Bitcoin in comparison to Russia's ban on other digital assets for payment regulation, highlighting the necessity for effective legislation once all around the world. It further delves into potential misuse of private cryptocurrencies in a variety of ways while placing upon the need for both countries to form a new digital currency regulatory authority. [Methodology/Approach/Design] This paper probes into the existing legal regulations for Cryptocurrency in India, Russia and other countries by using primary and secondary data throughout the paper. The primary data have been taken from legitimate government sources such as Russia's federal law and other bills and laws enacted in India, such as the "Banning of Cryptocurrency and Regulation of Official Digital Currency Bill, 2019" and "The Cryptocurrency and Regulation of Official Digital Currency Bill, 2021." Various judgments like the case of Internet and Mobile Association of India v. Reserve Bank of India and Dwaipayan Bhowmick v. Union Of India and Ors. have been taken out from Manupatra which is an online database for legal research. Government reports and notifications from the Indian, Russian and US government have also been examined. The secondary data include numerous news articles from Times of India, The Mirror, India Times, The Moscow Times, Telegraph, The UK News and other new sites. Lastly, articles by various researchers like Bohme et al, Dyhrberg and Kim have also been thoroughly analysed. [Findings] It was concluded that legalising Cryptocurrency through codified laws, appropriate approval for digital currencies through regulatory authorities, establishing clear definition of ‘cryptocurrency’, uniform taxation for all types of Cryptocurrencies, updation of penal laws and setting imprisonment for cryptocurrency regulation violation seem to be some effective solutions to reignite the Indian and Russian Economies. [Practical Implications] The practical implication lies in the fact that the use of cryptocurrencies is increasing on a daily basis, but neither the national government nor the world organizations has made any steps to control the market for virtual currencies.
This conceptual paper delves into the intriguing intersection between Ponzi schemes and cryptocurrency, shedding light on the unique dynamics, challenges, and implications arising from their convergence. While Ponzi schemes have long been recognized as fraudulent investment schemes, the emergence of cryptocurrency has introduced novel avenues for perpetrating such schemes. This paper examines the underlying mechanisms that facilitate the fusion of Ponzi schemes and cryptocurrency. It explores the emergence, characteristics, impact, and regulatory challenges. This paper contributes to a deeper understanding of the complex dynamics at play and provides insights into safeguarding investors and promoting the integrity of cryptocurrency markets.
Kithmini Godewatte Arachchige, Philip Branch, Jason But
The Internet of Things (IoT) and blockchain are emerging technologies that have attracted attention in many industries, including healthcare, automotive, and supply chain. IoT networks and devices are typically low-powered and susceptible to cyber intrusions. However, blockchains hold considerable potential for securing low-power IoT networks. Blockchain networks provide security features such as encryption, decentralisation, time stamps, and ledger functions. The integration of blockchain and IoT technologies may address many of the security concerns. However, integrating blockchain with IoT raises several issues, including the security vulnerabilities and anomalies of blockchain-based IoT networks. In this paper, we report on our experiments using our blockchain test bed to demonstrate that blockchains on IoT platforms are vulnerable to DDoS attacks, which can also potentially lead to device hardware failures. We show that a number of anomalies are visible during either a DDoS attack or IoT device failure. In particular, the temperature of IoT hardware devices can exceed 90 °C during a DDoS attack, which could lead to hardware failure and potential fire hazards. We also found that the Block Transaction Rate (BTR) and network block loss percentage can increase due to corrupted hardware, with the BTR dropping to nearly zero blocks/sec and a block loss percentage of over 50 percent for all evaluated blockchains, and as high as 81.3 percent in one case. Our experiments demonstrate that anomalous temperature, latency, bandwidth, BTR, and network block loss percentage can potentially be used to identify DDoS attacks.
Sabuj Saha, Ahmed Rizvan Hasan, Alvi Mahmud, Nujhat Ahmed · 6 authors
The use of cryptocurrency for financial crimes has increased in recent years because of its decentralized and anonymous nature. This study extracted scholarly articles from the Scopus database and adopted bibliographic and content analysis to review financial fraud research in cryptocurrency. In addition, this study discussed the top ten cryptocurrency scams, potential reasons for falling into those traps, and associated theories to explore scammers’ behavior and outlined comprehensive future research guidelines for a safer financial world. Since 2018, the publication trend of revealing cryptocurrency frauds has gained momentum, and research on this topic has increased significantly in the last two years. The USA is the most significant contributor to cryptocurrency scam research. We found that both developed and developing countries are fairly concerned about combatting crypto fraudsters even though there are no regulated guidelines across the countries. The research potential has shifted from malware, bitcoin, and blockchain to fintech-based crimes such as money laundering, pump-and-dump schemes, and phishing. We observed that ICO fraud, money laundering, Ponzi schemes, phishing, darknet market transactions, ransomware, and pumps and dumps are some of the predominant crimes in crypto and that investor overconfidence, speculative expectations, low barriers to entry, decentralization, and anonymity are the primary reasons for crimes in cryptocurrency. This study suggests studying the socioeconomic impacts of cryptocurrencies, the necessity for standardized global regulation, and the integration of interdisciplinary research. Future research should emphasize exploring the innovation cycle in cryptocurrency assets, understanding cybercrime dynamics, guarding against crypto market manipulation, and developing automated scam prevention.
Peer-to-Peer (P2P) cryptocurrency exchanges are two-sided marketplaces, similar to eBay, where individuals can offer to sell cryptocurrencies in exchange for payment. Due to disintermediation, these marketplaces trade off increased privacy for higher risk (e.g., scams/fraud). Although these marketplaces use feedback systems to encourage healthier transactions, anecdotal evidence suggests that feedback often fails to capture vendor-associated risks. This work documents the online safety of cryptocurrency P2P marketplaces, identifies underlying issues in feedback-based reputation systems, and proposes improved mechanisms for predicting/monitoring risky accounts. We collect data from two cryptocurrency marketplaces, Paxful and LocalCoinSwap (LCS) for 12 months (06/2022--06/2023). The data includes over 396,000 listings, 67,000 vendors, and 4.7 million feedback for Paxful; and about 52,000 listings, 14,000 users, and 146,000 feedback for LCS.First, we show that the current feedback system does not sufficiently convey enough information about risky vendors, and is susceptible to reputation manipulation through user collusion and automation. Second, combining various publicly available information, we build machine learning models to predict account suspension, and achieve a 0.86 F1-score and 0.93 AUC for Paxful. Third, while our models appear to have limited transferability across markets, we identify which features most help account suspension across platforms. Finally, we perform a month-long online evaluation to show that our models are significantly more successful than mere feedback-based reputation schemes at predicting which users will be suspended in the future.
Mrs. D. Thamizhisai, S. Bharathi, U. Bhuvaneshwaran, S. Mervin Immanuvel · 5 authors
The incorporation of blockchain technology into forensic investigations represents a significant advancement, tackling critical challenges within the legal and criminal justice systems. Central to this integration are smart contracts, which automate and secure essential aspects of investigations. These self-executing agreements operate based on predefined rules, ensuring integrity and transparency in tasks such as evidence tracking, chain of custody management, and access control. A key advantage lies in the substantial enhancement of data security. Blockchain's cryptographic principles and decentralized structure make it highly resistant to unauthorized access and tampering, crucial in maintaining evidence integrity. Additionally, blockchain's immutability ensures the reliability of information; once recorded, data becomes virtually unalterable, providing an indisputable ledger of events. In summary, this innovative integration streamlines operations, reduces errors and disputes, and strengthens the trustworthiness of forensic investigations by offering an unforgeable and transparent chain of custody and evidence history within the legal and criminal justice framework.
As the digital environment continues to evolve with the increasing frequency and complexity of cybersecurity threats, there is growing interest in using blockchain (BC) technology. BC is a technology with desirable properties such as decentralization, integrity, and transparency. The decentralized nature of BC eliminates single points of failure, reducing the vulnerability of critical systems to targeted attacks. The complex and rapidly evolving nature of cyber threats requires an earlier and adaptive approach. This review paper examined several papers collected from official websites. Focusing on using BC technology to improve cybersecurity, the main keywords of the review paper were BC technology, supply chain management, proof of work, and proof of stake. This review paper aims to investigate the security components through a threat assessment that compares the security of BC in different classes and real attack environments. It highlights the potential of BC to strengthen cybersecurity measures, citing unique features. The review paper also points out that there is a lack of focus on addressing security challenges related to computer data and digital systems and calling for a deeper discussion on problem-solving.
I explore how criminals use cryptocurrencies in ransomware operations and leverage the vulnerability of virtual currencies to evade legal restrictions and international scrutiny. I do so by examining three drivers of the ‘merger’ between ransomware and cryptocurrency. First, criminal groups have embraced cutting-edge technologies to make their attacks more effective and maximise benefits that cryptocurrency presents, which include the convenience of fast payment and money laundering and the ease of hacking the currencies themselves. Second, ransomware groups have exploited the legal vacuum in the widespread use of rapidly circulating monetary instruments. Finally, groups have adopted cryptocurrencies because states – primary regulators of international financial transactions – remain in such disagreement over the control of digital activities that they have failed to address problems associated with them. In sum, this article presents a set of technical, legal and political reasons why groups have incorporated crypto in their operations.
Student, CSE, Sir MVIT, B Sumangala, Aman Raj, Amritanshu Bhardwaj · 6 authors
Abstract - CrypticReport is a decentralized crime reporting system designed to make public reporting safer, more transparent, and free from identity risks. Citizens often avoid reporting crimes due to fear of exposure, harassment, or data misuse. CrypticReport overcomes these challenges by combining blockchain technology, decentralized IPFS storage, artificial intelligence or zero-knowledge–based authentication. Using Anon Aadhaar, users can verify their identity without revealing any personal information. AI models classify reports to block spam and detect duplicate submissions. All verified reports and evidence are stored in IPFS, and their hashes are recorded on the blockchain for tamper-proof storage. The platform uses a React interface for reporting, a Flask backend for AI processing, Ethereum smart contracts for record immutability, and the Waku protocol for real-time updates between citizens and authorities. Testing shows that the system improves trust, preserves anonymity, and ensures that no data can be altered once submitted. CrypticReport proves that decentralized systems can make crime reporting more secure, reliable, and citizen-friendly. Key Words: Blockchain, IPFS, Anonymous Reporting, AI Classification, Zero-Knowledge Proof, Decentralized Systems
The purpose of this article is to look at recent developments in forensic accounting that have to do with preventing and investigating fraud. The following new developments in forensic accounting are being studied by doing a thorough literature review: data analytics, cyber forensic accounting, and the impact of blockchain and cryptocurrencies on the field. We take a close look at each new trend, breaking it down into its uses, pros, disadvantages, and ethical implications. Case studies and real-world examples back up the findings, showing how effective these fraud prevention and investigation tendencies are. Investigations into financial crimes employing information technology have their own set of challenges, which the report sheds light on. Blockchain technology’s capacity to increase accountability, traceability, and transparency in financial transactions is also explored. To improve fraud detection and prevention efforts, the study finishes with suggestions for researchers, practitioners, and policymakers to adapt to and take advantage of these new trends. To effectively identify and discourage financial crime in the constantly evolving world of new technology, the study finishes by stressing the necessity for continuous research and innovation, highlighting the dynamic character of forensic accounting.
This study investigates the human errors that enable hackers to exploit and carry out social engineering attacks on the non-fungible token (NFT) ecosystem. The aim is to improve the design of decentralized applications that use NFTs to help non-technical users follow security best practices and address remaining user-side vulnerabilities. The study methods included a survey examining participants’ expertise regarding NFTs and cybersecurity, a remote security usability study investigating the pain points and common security best practices and a follow-up interview to examine participants’ experience with a crypto wallet configuration. The results show how human cognitive bias affects users’ decision to be cautious, users’ difficulty with security methods, and improvements to lessen users’ cognitive load. As NFTs expand beyond the cryptocurrency circle, multiple scams and thefts arise due to late adopters not knowing the security best practices. Therefore, increasing the public’s NFT security awareness is key to mitigating potential threats.
This paper explores the evidentiary significance of blockchain records and the procedural implications of integrating this technology into the U.S. judicial system, as several states have undertaken legislative measures to facilitate the admissibility of blockchain evidence. We employ a comprehensive methodological approach, including legislative analysis, comparative case law analysis, technical examination of blockchain mechanics, and stakeholder engagement. Our study suggests that blockchain evidence may be categorized as hearsay exceptions or non-hearsay, depending on the specific characteristics of the records. The paper proposes a specialized consensus mechanism for standardizing blockchain evidence authentication and outlines strategies to enhance the technology’s trustworthiness. It also highlights the importance of expert testimony in clarifying blockchain’s technical aspects for legal contexts. This study contributes to understanding blockchain’s integration into judicial systems, emphasizing the need for a comprehensive approach to its admissibility and reliability as evidence. It bridges the gap between technology and law, offering a blueprint for standardizing legal approaches to blockchain and urging ethical and transparent technology use.
Luo Feng, Ruijie Luo, Ting Chen, Ao Qiao · 8 authors
Smart contracts are integral to blockchain's growth, but their vulnerabilities pose a significant threat. Traditional vulnerability detection methods rely heavily on expert-defined complex rules that are labor-intensive and dificult to adapt to the explosive expansion of smart contracts. Some recent studies of neural network-based vulnerability detection also have room for improvement. Therefore, we propose SCVHunter, an extensible framework for smart contract vulnerability detection. Specifically, SCVHunter designs a heterogeneous semantic graph construction phase based on intermediate representations and a vulnerability detection phase based on a heterogeneous graph attention network for smart contracts. In particular, SCVHunter allows users to freely point out more important nodes in the graph, leveraging expert knowledge in a simpler way to aid the automatic capture of more information related to vulnerabilities. We tested SCVHunter on reentrancy, block info dependency, nested call, and transaction state dependency vulnerabilities. Results show remarkable performance, with accuracies of 93.72%, 91.07%, 85.41%, and 87.37% for these vulnerabilities, surpassing previous methods.
Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep learning to mitigate this threat. They treat each input contract as an independent entity and feed it into a deep learning model to learn vulnerability patterns by fitting vulnerability labels. It is a pity that they disregard the correlation between contracts, failing to consider the commonalities between contracts of the same type and the differences among contracts of different types. As a result, the performance of these methods falls short of the desired level. To tackle this problem, we propose a novel Contrastive Learning Enhanced Automated Recognition Approach for Smart Contract Vulnerabilities, named Clear. In particular, Clear employs a contrastive learning (CL) model to capture the fine-grained correlation information among contracts and generates correlation labels based on the relationships between contracts to guide the training process of the CL model. Finally, it combines the correlation and the semantic information of the contract to detect SCVs. Through an empirical evaluation of a large-scale real-world dataset of over 40K smart contracts and compare 13 state-of-the-art baseline methods. We show that Clear achieves (1) optimal performance over all baseline methods; (2) 9.73%-39.99% higher F1-score than existing deep learning methods.
Smart contracts are self-executing programs that can run on a blockchain. Due to the fact of being immutable after their deployment on blockchain, it is crucial to ensure their correctness. For this reason, various approaches for static analysis of smart contracts have been proposed, but they may be on the one hand imprecise or on the other hand difficult to train. In this paper, we propose a novel approach for detecting smart contract vulnerabilities using OpenAI's Generative Pre-trained Transformer 3 (GPT-3) language model. Our approach, called VulntHunt-GPT, uses GPT-3 to examine Ethereum smart contracts in order to identify the most popular vulnerabilities according to OWASP. We train VulntHunt-GPT on a dataset of smart contract functions and vulnerabilities to improve its accuracy. Our experiments show that VulntHunt-GPT outperforms almost all the existing state-of-the-art approaches in detecting a variety of vulnerabilities, including reentrancy attacks, integer overflow, and uninitialized storage. In addition, we conduct a case study to demonstrate the effectiveness of VulntHunt-GPT in detecting real-world smart contract vulnerabilities. We show that VulntHunt-GPT can identify previously unknown vulnerabilities in popular smart contracts, highlighting its potential for improving smart contract security. Our approach provides a promising direction for using natural language processing techniques to improve smart contract security and reduce the risk of smart contract exploits.
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.
Abstract: Criminal activities in India are on the rise, with many incidents going unreported. Despite the availability of an online portal for storing the First Information Reports (FIRs) handwritten FIRs always will remain common due to some of the traditional methods and practices. And probably in most cases, the complainants should personally visit the police stations in order to file a offense report. Crime and Criminal Tracking Network and Systems (CCTNS) was launched in 2010 for national wide e-governance, it will operates on a centralized system and it is particularly limited to some individual states. Therefore, there is a need for the decentralized solution in order to ensure failure of a single point and secure managing of criminal complaints from the unauthorized access.