User Credentials Research Articles

Intelligent IoT-BOTNET attack detection model with optimized hybrid classification model

The botnet have developed into a severe risk to Internet of Things (IoT) systems as a result of manufacturers ‘insufficient security policies and end users’ lack of security awareness. By default, several ports are open and user credentials are left unmodified. ML and DL strategies have been suggested in numerous latest research for identifying and categorising botnet assaults in the IoT context, but still, it has a few issues like high error susceptibility, working only with a large amount of data, poor quality, and data acquisition. This research provided use of a brand-new IoT botnet detector built on an improved hybrid classifier. The proposed work's main components are "pre-processing, feature extraction, feature selection, and attack detection." Following that, the improved Information Gain (IIG) model is used to choose the most reliable characteristics from the received information. To detect an attack, a hybrid classifier is utilized which can be constructed by integrating the optimized Bi-GRU with the Recurrent Neural Network (RNN). To increase the detection accuracy of IoT-BOTNETS, a novel hybrid optimization approach called SMIE (Slime Mould with Immunity Evolution) is created by conceptually integrating two conventional optimization modes: Coronavirus herd immunity optimizer (CHIO) and the Slime mould algorithm. The final output of the hybrid classifier displays the presence or absence of IoT-BOTNET attacks. The projected model's accuracy is 97%, which is 22.6%, 18.5%, 27.8%, 22.6%, and 24.8% higher than the previous models like GWO+ HC, SSO+ HC, WOA+ HC, SMA+ HC, and CHIO+ HC, respectively.

Attacking Websites: Detecting and Preventing HTTP Request Smuggling Attacks

Until the development of HTTP request smuggling in 2005, individual HTTP requests were considered as independent entities and could not be split or merged. This is a security problem caused by inconsistent content length interpretation approach between web servers, or the web server is not fully implemented in accordance with the RFC standard. It is especially dangerous for web services with complex web architectures. It can route the victims to receive malicious responses, amplify the impact of certain low-threat vulnerabilities, steal user credentials, or bypass network devices’ defenses. However, since its concept and implementation are quite difficult to overcome, it is often ignored by many network administrators, making users who browse such websites vulnerable to the HTTP request smuggling attacks. This paper proposes a general solution to deal with various HTTP request smuggling attacks. A reverse proxy implemented by Flask validates and cleans dubious HTTP requests from the client side and ensures that the original requests comply with RFC standards. Therefore, the website administrators no longer need to configure complicated network settings or customize some open-source project codes to resist or minimize the risk of the HTTP request smuggling attacks. A series of experiments demonstrate that this method is effective and practical.

PrivFace: Fast Privacy-Preserving Face Authentication With Revocable and Reusable Biometric Credentials

Privacy concerns of using sensitive biometric data as credentials arise with the wide adoption of user-friendly face authentication. To protect the facial features of users, two important functions, i.e., <i>revocability</i> and <i>reusability</i> , are anticipated to be realized in a privacy-preserving face authentication design. Revocability requires an effective approach to deregister or replace user credentials when the authentication server is compromised; For reusability, the same credentials should appear independently to non-cooperating applications. Accomplishing these two properties is challenging as the uniqueness of facial features. In this article, we present <small>PrivFace</small> , a fast privacy-preserving face authentication, supporting revocable, and reusable biometric credentials. The core innovation is a novel secure inner product protocol that employs a lightweight random masking technique instead of time-consuming public-key cryptographic operations to efficiently measure facial data similarity. We rigorously analyze the security to show that the server cannot acquire the user's sensitive biological features during the authentication. Our experiment with real-world datasets shows that <small>PrivFace</small> is friendly to edge smart devices, which takes less than <inline-formula><tex-math notation="LaTeX">$100 ms$</tex-math></inline-formula> per successful authentication on a common smartphone and outperforms the prior art J. Lei, Q. Pei <i>et al.</i> [1]. by <inline-formula><tex-math notation="LaTeX">$20 \times$</tex-math></inline-formula> . We have made the relevant codes open-source to the community for further research.

Forensic investigation of Cisco WebEx desktop client, web, and Android smartphone applications.

Digital forensic analysis of videoconferencing applications has received considerable attention recently, owing to the wider adoption and diffusion of such applications following the recent COVID-19 pandemic. In this contribution, we present a detailed forensic analysis of Cisco WebEx which is among the top three videoconferencing applications available today. More precisely, we present the results of the forensic investigation of Cisco WebEx desktop client, web, and Android smartphone applications. We focus on three digital forensic areas, namely memory, disk space, and network forensics. From the extracted artifacts, it is evident that valuable user data can be retrieved from different data localities. These include user credentials, emails, user IDs, profile photos, chat messages, shared media, meeting information including meeting passwords, contacts, Advanced Encryption Standard (AES) keys, keyword searches, timestamps, and call logs. We develop a memory parsing tool for Cisco WebEx based on the extracted artifacts. Additionally, we identify anti-forensic artifacts such as deleted chat messages. Although network communications are encrypted, we successfully retrieve useful artifacts such as IPs of server domains and host devices along with message/event timestamps.

Attribute-based keyword search across encrypted cloud data with multiple authorities

Searchable Encryption (SE) is a crucial technique for cloud data security and usability (SE). When employing Ciphertext Policy Attribute-Based Encryption (CP-ABE) and the Ciphertext Policy Attribute-Based Keyword Search (CP-ABKS), encryption and access control may be done simultaneously. A single attribute authority is responsible for authenticating user certificates and delivering secret keys in current CP-ABKS systems. A single point of failure results in distributed cloud systems. To address these difficulties, we explain in this paper the MABKS system, which is designed to help cloud devices with limited computing and storage capabilities. The MABKS system has added capability for tracing and modifying malicious attribute authority. According to our detailed security investigation, the MABKS system is safe in both selective-matrix and selective-attribute models. Our trials using real-world datasets have shown the MABKS system's efficiency and usefulness.

Any Time Medicine Vending Machine

Abstract: This paper describes the design of any time medicine vending machine as a health device for the purpose of person who in need of medicines and its assistance. This device mainly consists of a card reader and the dispenser which comprises the automatic medicine vending machine through the dispenser as per the doctor’s e-prescription updated into smart card. This vending machine is controlled by the Arduino board which is a single board computer and it can be aspect from online portal for the need person to check his e-prescription and the doctor to generate or update the prescription. When the patient swipes his smart card into the card reader, this device dispenses out the prescribed medicine for the need person only when the user credentials of the patient is validated from the e-prescription. The online portal is made on a telegram and connected to the same user credentials, and even though the patient can view his details and the prescription by logging in with are credentials into the same web page

EVOLUTION AND ANALYSIS OF SECURED HASH ALGORITHM (SHA) FAMILY

With the rapid advancement of technologies and proliferation of intelligent devices, connecting to the internet challenges have grown manifold, such as ensuring communication security and keeping user credentials secret. Data integrity and user privacy have become crucial concerns in any ecosystem of advanced and interconnected communications. Cryptographic hash functions have been extensively employed to ensure data integrity in insecure environments. Hash functions are also combined with digital signatures to offer identity verification mechanisms and non-repudiation services. The federal organization National Institute of Standards and Technology (NIST) established the SHA to provide security and optimal performance over some time. The most well-known hashing standards are SHA-1, SHA-2, and SHA-3. This paper discusses the background of hashing, followed by elaborating on the evolution of the SHA family. The main goal is to present a comparative analysis of these hashing standards and focus on their security strength, performance and limitations against common attacks. The complete assessment was carried out using statistical analysis, performance analysis and extensive fault analysis over a defined test environment. The study outcome showcases the issues of SHA-1 besides exploring the security benefits of all the dominant variants of SHA-2 and SHA-3. The study also concludes that SHA-3 is the best option to mitigate novice intruders while allowing better performance cost-effectively.

Internet Scam (Phishing): Issues of Counteraction and Prevention

Internet technologies are so common today that they literally extend over almost all areas of life. However, due to the ongoing growth of Internet technologies, security threats to systems and networks are also rising. One of these serious threats is «phishing», in which attackers try to illicitly obtain user credentials. Phishing is a fraudulent practice, the purpose of which is to create a fake website, illicitly asking for passwords, bank account numbers, etc. to induce users to reveal confidential information. This type of scam is increasingly gaining in popularity, since fraudsters incur a minimum amount of expenses, and the tools used by them for illegal purposes are relatively accessible and easy to use. The purpose of this study is to examine common features of phishing attacks, as well as to analyze means and methods of countering these wrongdoings. The article looks into the existing ways of countering phishing attacks and analyzes measures aimed at identifying and suppressing such crimes. The analysis of judicial and investigative practice has shown that in the current state the government needs to provide law enforcement agencies with cutting-edge technical equipment, software, and highly qualified staff in order to successfully overcome new challenges in cyberspace in the context of the digital economy.

Enhanced Dual-Policy Attribute-Based Encryption for Secure Data Sharing in the Cloud

As a promising service paradigm, cloud computing has attracted lots of enterprises and individuals to outsource big data to public cloud. To facilitate secure data using and sharing, dual-policy attribute-based encryption (DP-ABE) is a suitable solution. It allows two access control mechanisms over encrypted data at the same time: one involves access policies over subjective attributes ascribed to user credentials, and the other involves policies over objective attributes ascribed to data. In this work, we are exploring methods to make DP-ABE more flexible, more efficient, and more secure for deployments in cloud scenes. Our proposal features the following achievements simultaneously: (1) beyond the access control mechanisms of DP-ABE, it also supports two flexible features called encryption and key generation in single-policy modes; (2) most operations of key generation, encryption, and decryption are securely outsourced to cloud servers, leaving extremely low overheads for the PKG, data owners, and users; and (3) it realizes the strongest security notion of public-key encryption schemes, namely, CCA security. We formalize the security definition and formally prove its security in the random oracle model. Moreover, we implement the proposed schemes using the Charm framework. The experiment results demonstrate that our schemes are efficient and practical.

Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication

Static authentication methods, like passwords, grow increasingly weak with advancements in technology and attack strategies. Continuous authentication has been proposed as a solution, in which users who have gained access to an account are still monitored in order to continuously verify that the user is not an imposter who had access to the user credentials. Mouse dynamics is the behavior of a user’s mouse movements and is a biometric that has shown great promise for continuous authentication schemes. This article builds upon our previous published work by evaluating our dataset of 40 users using three machine learning and three deep learning algorithms. Two evaluation scenarios are considered: binary classifiers are used for user authentication, with the top performer being a 1-dimensional convolutional neural network (1D-CNN) with a peak average test accuracy of 85.73% across the top-10 users. Multi-class classification is also examined using an artificial neural network (ANN) which reaches an astounding peak accuracy of 92.48%, the highest accuracy we have seen for any classifier on this dataset.

Securing Enterprise Emails in Cloud Platform

Cloud-based email is one of the services offered by cloud computing, and the number of users continues to grow year after year. Because of its working environment, cloud computing raises concerns about security and privacy. User authentication in cloud computing is now predicated on the user's credentials, which are typically username and password. User authentication in cloud computing is currently predicated on the credentials possessed by the user, which are primarily username and password. With the growing usage of cloud emails and numerous allegations of large-scale email leakage occurrences, a security attribute known as forward secrecy has become desirable and necessary for both users and cloud email service providers to strengthen the security of their communications. However, due to the failure of email systems to meet both security and practicality requirements at the same time. A fine-grained revocation capacity is available to an email user. A security key will be provided by the user to prevent hacking of such email addresses. The MAES(Modified Advanced Encryption Standard) algorithm encrypts files and a user's email ID to safeguard their data from a third party or hackers to address this issue more efficiently. This proposed hybrid security method secures the content of emails before they are sent through email using an Advanced Cipher Technique (ACT). The study suggests employing substitution and permutation to secure email content, with the fronts offered by email systems acting as keys.

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

An unsolicited means of digital communications in the internet world is the spam email, which could be sent to an individual or a group of individuals or a company. These spam emails may cause serious threat to the user i.e., the email addresses used for any online registrations may be collected by the malignant third parties (spammers) and they expose the genuine user to various kinds of attacks. Another method of spamming is by creating a temporary email register and receive emails that can be terminated after some certain amount of time. This method is well suited for misusing those temporary email addresses for sending free spam emails without revealing the spammers real account details. These attacks create major problems like theft of user credentials, lack of storage, etc. Hence it is essential to introduce an efficient detection mechanism through feature extraction and classification for detecting spam emails and temporary email addresses. This can be accomplished through a novel Natural Language Processing based Random Forest (NLP-RF) approach. With the help of our proposed approach, the spam emails are reduced and this method improves the accuracy of spam email filtering, since the use of NLP makes the system to detect the natural languages spoken by people and the Random Forest approach uses multiple decision trees and uses a random node for filtering the spams.

Microsoft Teams desktop application forensic investigations utilizing IndexedDB storage

While the COVID-19 virus remolded the routines of the establishments, remote collaboration and distant communication gained more popularity. As the way electronic communications are handled changes drastically, new applications and storage mechanisms are introduced. Microsoft Teams is an application offered within the scope of Microsoft Office 365 that offers services for hosting virtual meetings, team communication, and comprehensive team resource management. It is prevalently used by organizations and indicates a great potential to be a source of digital forensic investigations. This paper scrutinizes the artifacts created by Microsoft Teams in IndexedDB persistent storage. IndexedDB is a fast-growing client-side storage technology that is relatively new as a source for digital forensic investigations. A single-case pretest-posttest quasi experiment was conducted to produce artifacts in Microsoft Teams IndexedDB storage. The artifacts were extracted without user credentials indicating security flaws in the application. Extracted artifacts were processed based on signature patterns and evaluated for their significance. Traditional database queries were utilized to link and present the information clustered according to their relevancy. A time-frame analysis was constructed to display information in a suitable format for investigators. The results indicate that Microsoft Teams IndexedDB storage artifacts contain significant potential for digital investigations with extraction of complete contents of private chat messages, voice mails, and team extensions with efficient time-frame analysis.

A Consortium Blockchain-Based Secure and Trusted Electronic Portfolio Management Scheme.

In recent times, electronic portfolios (e-portfolios) are being increasingly used by students and lifelong learners as digital online multimedia résumés that showcase their skill sets and achievements. E-portfolios require secure, reliable, and privacy-preserving credential issuance and verification mechanisms to prove learning achievements. However, existing systems provide private institution-wide centralized solutions that primarily rely on trusted third parties to issue and verify credentials. Furthermore, they do not enable learners to own, control, and share their e-portfolio information across organizations, which increases the risk of forged and fraudulent credentials. Therefore, we propose a consortium blockchain-based e-portfolio management scheme that is decentralized, secure, and trustworthy. Smart contracts are leveraged to enable learners to completely own, publish, and manage their e-portfolios, and also enable potential employers to verify e-portfolio credentials and artifacts without relying on trusted third parties. Blockchain is used as an immutable distributed ledger that records all transactions and logs for tamper-proof trusted data provenance, accountability, and traceability. This system guarantees the authenticity and integrity of user credentials and e-portfolio data. Decentralized identifiers and verifiable credentials are used for user profile identification, authentication, and authorization, whereas verifiable claims are used for e-portfolio credential proof authentication and verification. We have designed and implemented a prototype of the proposed scheme using a Quorum consortium blockchain network. Based on the evaluations, our solution is feasible, secure, and privacy-preserving. It offers excellent performance.

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

AbstractCloud computing is one example of a technological revolution that has drastically altered the way traditional commercial operations are conducted. Because cloud platforms are fundamentally revolutionizing the area of computing, they must provide safe transmission and storage of user data by assuring the confidentiality, integrity, and availability of the data. As a result, establishing a secure cloud computing framework is crucial, and security cannot only rely on the user's credentials being verified. To overcome this problem, this article presents a multi‐population genetic algorithm optimized dynamic kernel convolutional neural network to analyze the user's behavior to identify their malicious intent. This article describes a two‐stage malicious detection and prediction scheme. The proposed system's efficiency is measured by its ability to discriminate between nine different types of attacks in the UNSW‐NB15 dataset (abnormal behavior exhibited by users). The average true positive rate, false positive rate, precision, and F‐measure for the nine attack classes were 99.22%, 99.11%, 99.11%, and 99.22%, respectively, when evaluated using the UNSW‐NB15 dataset. The experimental results demonstrate that this technique is effective in detecting malicious behaviors in the cloud environment with higher accuracy.

Towards Secure IoT-Based Payments by Extension of Payment Card Industry Data Security Standard (PCI DSS)

IoT emergence has given rise to a new digital experience of payment transactions where physical objects like refrigerators, cars, and wearables will make payments. These physical objects will be storing the cardholder credentials and will directly make payments with the vendors over insecure public networks. For such payment transactions, government regulations and standards organizations require to implement PCI DSS for adapting similar set of security measures at the global level. The current version of PCI DSS is not suitable for IoT-based payment systems due to characteristics of IoT such as resource-constrained nature of devices and updating software/firmware of so many physical devices. Also, there arises an emergent need of implementing PCI DSS requirements and assessments for security of all stakeholders that store or process the user credentials in a payment. This paper is an initial effort to bring the researcher’s attention to make upcoming versions of PCI DSS suitable for IoT and thus securing the new ways of IoT-based payment systems. The paper has reviewed the traditional payment process along with considerations for IoT-based payment systems to make recommendations to modify the PCI DSS in a suitable way for IoT.

Secure Key Management Based Mobile Authentication in Cloud

Authentication is important to the security of user data in a mobile cloud environment. Because of the server’s user credentials, it is subject to attacks. To maintain data authentication, a novel authentication mechanism is proposed. It consists of three independent phases: Registration, login, and authentication and key agreement. The user registers with the Registration Center (RC) by producing a secret number that isn’t stored in the phone, which protects against privileged insider attacks. The user and server generate a nonce for dynamic user identity and agree on a session secret key for safe communication. The passwords are not stored on the computer or provided in plain text, they are resistant to replay, guessing, and stolen verification attacks. The suggested protocol uses a one-way hash function and XOR operations, with the client having remote access to a large number of servers over a secure communication channel. Concentrates on HMAC and SHA3 for Collision Free Hashing and to overcome length extension attacks. HMACs are substantially less affected by collisions than their underlying hashing algorithms alone. So adding an HMAC to an MD5 or SHA hash would make it substantially more difficult to break via a rainbow table.

A Versatile, Secure, and Sustainable All-in-One Biobank-Registry Data Solution: The A3BC REDCap Model.

Introduction: A key element in the big data revolution is large-scale biobanking and the associated development of high-quality data collections and supporting informatics solutions. As such, in establishing the Australian Arthritis and Autoimmune Biobank Collaborative (A3BC), we sought to establish a low-cost, nation-scale data management system capable of managing a multisite biobank registry with complex longitudinal sample and data requirements. Materials and Methods: We assessed several international commercial and nonprofit software platforms using standardized system requirement criteria and follow-up interviews. Vendor compliance scoring was prioritized to meet our project-critical requirements. Consumer/end-user codesign was integral to refining our system requirements for optimized adoption. Customization of the selected software solution was performed to optimize field auto-population between participant timepoints and forms, using modules that are transferable and that do not impact core code. Institutional and independent testing was used to ensure data security. Results: We selected the widely used research web application Research Electronic Data Capture (REDCap), which is "free" (under nonprofit license agreement terms), highly configurable, and customizable to a variety of biobank and registry needs and can be developed/maintained by biobank users with modest IT skill, time, and cost. We created a secure, comprehensive participant-centric biobank-registry database that includes: (1) best practice data security measures (incl. multisite access login using institutional user credentials), (2) permission-to-contact and dynamic itemized electronic consent, (3) a complete chain of custody from consent to longitudinal biospecimen data collection to publication, (4) complex longitudinal patient-reported surveys, (5) integration of record-level extracted/linked participant data, (6) significant form auto-population for streamlined data capture, and (7) native dashboards for operational visualizations. Conclusion: We recommend the versatile, reusable, and sustainable informatics model we have developed in REDCap for prospective chronic disease biobanks or registry biobanks (of local to national complexity) supporting holistic research into disease prediction, precision medicine, and prevention strategies.

A Blockchain-Based Dynamic Consent Architecture to Support Clinical Genomic Data Sharing (ConsentChain): Proof-of-Concept Study.

BackgroundIn clinical genomics, sharing of rare genetic disease information between genetic databases and laboratories is essential to determine the pathogenic significance of variants to enable the diagnosis of rare genetic diseases. Significant concerns regarding data governance and security have reduced this sharing in practice. Blockchain could provide a secure method for sharing genomic data between involved parties and thus help overcome some of these issues.ObjectiveThis study aims to contribute to the growing knowledge of the potential role of blockchain technology in supporting the sharing of clinical genomic data by describing blockchain-based dynamic consent architecture to support clinical genomic data sharing and provide a proof-of-concept implementation, called ConsentChain, for the architecture to explore its performance.MethodsThe ConsentChain requirements were captured from a patient forum to identify security and consent concerns. The ConsentChain was developed on the Ethereum platform, in which smart contracts were used to model the actions of patients, who may provide or withdraw consent to share their data; the data creator, who collects and stores patient data; and the data requester, who needs to query and access the patient data. A detailed analysis was undertaken of the ConsentChain performance as a function of the number of transactions processed by the system.ResultsWe describe ConsentChain, a blockchain-based system that provides a web portal interface to support clinical genomic sharing. ConsentChain allows patients to grant or withdraw data requester access and allows data requesters to query and submit access to data stored in a secure off-chain database. We also developed an ontology model to represent patient consent elements into machine-readable codes to automate the consent and data access processes.ConclusionsBlockchains and smart contracts can provide an efficient and scalable mechanism to support dynamic consent functionality and address some of the barriers that inhibit genomic data sharing. However, they are not a complete answer, and a number of issues still need to be addressed before such systems can be deployed in practice, particularly in relation to verifying user credentials.

An adaptive approach for internet phishing detection based on log data

The Internet has become one of the most important daily socials, financial and other activities. the number of customers who use the Internet to conduct their business and purchases is very large. This results in billions of dollars being transferred every day online. Such a large amount of money attracts the attention of cybercriminals to carry out their illegal activities. “Fraud” is one of the most dangerous of these methods, especially phishing, where attackers try to steal user credentials using fraudulent emails, fake websites, or both. The proposed system for this paper includes efficient data extraction from the web file through data collection and preprocessing. and web usage mining procedure to extract features that demonstrate user behavior. and feature-extracting URL analysis to detect website phishing addresses. After that, the features from the above two parts are combined to make the number of features sixty-three. Finally, a classification algorithm (Random Forests) is applied to determine if website addresses are phishing or legitimate. Suggested algorithms performance is determined by using a confusion matrix and a number of metrics that shows the robustness of the proposed system.