Security Research Research Articles

Anomaly detection and defense techniques in federated learning: a comprehensive review

In recent years, deep learning methods based on a large amount of data have achieved substantial success in numerous fields. However, with increases in regulations for protecting private user data, access to such data has become restricted. To overcome this limitation, federated learning (FL) has been widely utilized for training deep learning models without centralizing data. However, the inaccessibility of FL data and heterogeneity of the client data render difficulty in providing security and protecting the privacy in FL. In addition, the security and privacy anomalies in the corresponding systems significantly hinder the application of FL. Numerous studies have been proposed aiming to maintain the model security and mitigate the leakage of private training data during the FL training phase. Existing surveys categorize FL attacks from a defensive standpoint, but lack the efficiency of pinpointing attack points and implementing timely defenses. In contrast, our survey comprehensively categorizes and summarizes detected anomalies across client, server, and communication perspectives, facilitating easier identification and timely defense measures. Our survey provides an overview of the FL system and briefly introduces the FL security and privacy anomalies. Next, we detail the existing security and privacy anomalies and the methods of detection and defense from the perspectives of the client, server, and communication process. Finally, we address the security and privacy anomalies in non-independent identically distributed cases during FL and summarize the related research progress. This survey aims to provide a systematic and comprehensive review of security and privacy research in FL to help understand the progress and better apply FL in additional scenarios.

A hybrid feature weighted attention based deep learning approach for an intrusion detection system using the random forest algorithm.

Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. Multiple network attacks by an intruder are unavoidable. Our research targets the critical issue of class imbalance in intrusion detection, a reflection of the real-world scenario where legitimate network activities significantly out number malicious ones. This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. The primary goal is to create models that are highly sensitive to intrusions while minimizing false alarms, an essential aspect of effective IDS. This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. Our research, by addressing these significant challenges, is positioned to make substantial contributions to cybersecurity, providing valuable insights and applicable solutions in the fight against digital threats and ensuring robustness and relevance in IDS development. An intrusion detection system (IDS) checks network traffic for security, availability, and being non-shared. Despite the efforts of many researchers, contemporary IDSs still need to further improve detection accuracy, reduce false alarms, and detect new intrusions. The mean convolutional layer (MCL), feature-weighted attention (FWA) learning, a bidirectional long short-term memory (BILSTM) network, and the random forest algorithm are all parts of our unique hybrid model called MCL-FWA-BILSTM. The CNN-MCL layer for feature extraction receives data after preprocessing. After convolution, pooling, and flattening phases, feature vectors are obtained. The BI-LSTM and self-attention feature weights are used in the suggested method to mitigate the effects of class imbalance. The attention layer and the BI-LSTM features are concatenated to create mapped features before feeding them to the random forest algorithm for classification. Our methodology and model performance were validated using NSL-KDD and UNSW-NB-15, two widely available IDS datasets. The suggested model's accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. The model's binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. Further, we compared the suggested approach with other previous machine learning and deep learning models and found it to outperform them in detection rate, FPR, and F-score. For both binary and multiclass classifications, the proposed method reduces false positives while increasing the number of true positives. The model proficiently identifies diverse network intrusions on computer networks and accomplishes its intended purpose. The suggested model will be helpful in a variety of network security research fields and applications.

Enhancing security and privacy in smart agriculture: A novel homomorphic signcryption system

Technology driven Smart Agriculture is transforming modern day farming practices through digital tools and real-time data analysis. It monitors plant health in both soil and soilless cultivation, promoting environmentally responsible practices by utilizing integrated sensors and precision methods. Despite the significant potential benefits, challenges persist in ensuring data security in the smart agriculture frameworks. The existing research has explored various mechanisms to cater these issues, yet these solutions susceptible to known attacks i.e., Confidentiality, Authentication, Privacy preservation. Notably, the available schemes in open literature face performance deficiencies due to an overreliance on Rivest-Shamir-Adleman (RSA), bilinear pairing, and elliptic curve (EC). To comprehensively address these challenges, this study presents a novel approach via employing homomorphic Signcryption based on Hyper-Elliptic Curves (HEC). Our HEC based Signcryption technique enhances security with lower computational cost and communication overhead as compared to the existing techniques. Furthermore, our solution provides the best performance in terms of efficiency, addressing the privacy preservation issues with conformance to the (Confidentiality, Integrity, and Availability) CIA triad. For proof of concept and correctness, we have validated the results in Scyther tool, with the assurance that proposed scheme is correct, verified, and resistant against known set of attacks. The proposed scheme achieves a remarkable reduction in both computational and communication costs around 95.08 % and 89.28 % respectively, enhancing practicality and suitability for real-world implementation. Thus, making it a suitable candidate for employing in smart agriculture environments, encompassing both soil and soilless cultivation scenarios. This research contributes a robust, secure, cost effective, and efficient solution to the challenges posed by the evolving landscape of agriculture, paving the way for enhanced data protection and system resilience in smart agriculture systems.

"No point worrying" – The role of threat devaluation in information security behavior

Extant information security research is characterized by a focus on problem-focused security behaviours, while overlooking the internal, and emotion-focused coping responses that humans exhibit. Threat devaluation, where severity is downplayed, is an established dimension of risk perception, and yet it has not been considered in information security research to date. We address this gap by developing and empirically testing a research model of how threat and coping factors from Protection Motivation Theory influence both problem-focused and emotion-focused coping responses. Data was collected from 518 users and PLS was used to reveal the determinants of, and the relationship between, problem-focused and emotion-focused coping behaviors. The results demonstrate that threat devaluation is a measurable outcome of all threat and coping appraisals considered, providing evidence that multiple coping strategies may be involved in a security threat situation. We also find that many of the well-established determinants of information security behavior, such as self-efficacy, are significantly related to emotion-focused responses. This has implications for researchers and practitioners who seek to create secure environments where users are more likely to enact constructive and problem-focused security behaviors.

Security Issues and Research Challenges in Cloud Computing

Cloud Computing is a type of internet based computing which provides services via the internet and accesses the resources within the user enterprise either in a private-own-cloud or on a third-party server On Demand. The model is characterized by three attributes: scalability, pay-per-use, self-services. Many industries such as banking, healthcare, Retail, Education, Manufacturing and business are adopting this cloud technique due to efficiency of services provided by pay-per-use pattern which helps in accessing the networks, storage, servers, services and applications, without physically acquiring them [3]. The circumscribed control over the data may cause various security issues in cloud computing like Data crash, Misuse and reprehensible use of cloud computing, Insecure API, Wicked Insiders, Shared technology issues/multi-tendency nature, Account services and Traffic Hijacking. There are many new technologies, improvements and research proceedings happening every day in order to develop the security and to provide assurance for users [2]. This research paper brings a framework on what cloud computing is, main security risks and issues that are currently present in the field of cloud computing, research challenges, importance in key industries and also the personal hypothesis on future advances in the field of cloud security.

A Lightweight Secure Scheme for Underwater Wireless Acoustic Network

Due to the open underwater channels and untransparent network deployment environments, underwater acoustic networks (UANs) are more vulnerable to hostile environments. Security research is also being conducted in cryptography, including authentication based on asymmetric algorithms and key distribution based on symmetric algorithms. In recent years, the advancement of quantum computing has made anti-quantum attacks an important issue in the field of security. Algorithms such as lattice and SPHINCS+ have become a research topic of interest in the field of security. However, within the past five years, few papers have discussed security algorithms for UANs to resist quantum attacks, especially through classical algorithms. Some existing classical asymmetric and symmetric algorithms are considered to have no prospects. From the perspective of easy deployment in engineering and anti-quantum attacks, our research focuses on a comprehensive lightweight security framework for data protection, authentication, and malicious node detection through the Elliptic Curve and Hash algorithms. Our mechanism is suitable for ad hoc scenarios with limited underwater resources. Meanwhile, we have designed a multi-party bit commitment to build a security framework for the system. A management scheme is designed by combining self-certifying with the threshold sharing algorithm. All schemes are designed based on certificate-less and ad hoc features. The proposed scheme ensures that the confidentiality, integrity, and authentication of the system are well considered. Moreover, the scheme is proven to be of unconditional security and immune to channel eavesdropping. The resource and delay issues are also taken into consideration. The simulations considered multiple variables like number of nodes, attackers, and message length to calculate proper values that can increase the efficiency of this scheme. The results in terms of delay, delivery ratio, and consumption demonstrate the suitability of the proposal in terms of security, especially for malicious node detection. Meanwhile, the computational cost has also been controlled at the millisecond level.

Document Security System Using Arduino-Based Fingerprint And Rfid Module

Documents are valuable/important letters that can be used as evidence of printed or written information. The company where the author conducted research is a company operating in the logistics sector, which of course has a special document storage room, namely the Accounting room . The use of plastic folders containing documents and safes as a place to store important documents and files still carries a lot of risk of misuse and loss. The title of this research is Document Security System Using Arduino- Based Fingerprint and RFID Modules . One of the aims of this research is to minimize loss and prevent misuse of documents/files placed in plastic folders. A security system using fingerprint and RFID is very good to apply as document security because it has double security. The system created uses a fingerprint as a signal breaker or RFID tag electromagnetic waves which are attached to a folder containing important documents/files belonging to the company. This system is also supported by an Arduino kit Uno with an Atmega328 microcontroller as the brain for processing data from the fingerprint sensor and RFID Reader functions as an electromagnetic wave identification system for RFID Tags , therefore two pieces of hardware are needed, called Tags and Readers . Apart from the three main components mentioned, there are several other components used in making this document security system, namely micro SD kit, buzzer, push button, servo . Creating this system is very important to limit misuse of documents and the risk of losing documents in a company.

NEUROPERSONALITY APPROACH TO THE ISSUE OF COGNITIVE SECURITY AS A NECESSARY CONDITION FOR WORK-RELATED SOCIALIZATION OF IT-SPECIALISTS

The post-classical paradigm of understanding the system of personality potentials due to virtualization and informatization of most spheres of natural life emphasizes the need to rethink the many positions of determining personal development and resourcefulness of specialists. The conceptual principles of cognitive security research of specialists in the field of IT technologies, whose professional activity is directly related to information culture, information, and cognitive security of others, need to be clarified. Methodology. An empirical study was conducted on two randomized groups of IT specialists. The groups are differentiated according to the criterion of professional experience. Accordingly, two groups were formed: EG1 (n 76) - IT specialists with experience of up to five years and group EG2 (n 83) - IT specialists with experience of more than five years (6-10 years). To generalize the structure of the relationship of categories that reveal the range of factors of cognitive-stylistic determination and neurodynamic conditionality of cognitive security of IT specialists, an expert evaluation was conducted. Research technology involved the assessment of IT experts (22 specialists with 15-22 years of experience) the importance of parameters that identified three main strategies for cognitive security of IT specialists Results. Sixteen neuropersonality types of solving professional problems and ensuring cognitive security of IT specialists differentiated by professional experience were identified. Three strategies of cognitive security of IT specialists: "Life-sustainability-strategicity", "Metacognitive knowledge - control over action" and "Positive refocusing - Metacognitive activity" are specified.

Fostering security-related citizenship through the employee-supervisor relationship: An examination of supervisor security embodiment

Organizational information security performance is increasingly dependent on employees’ security-related citizenship behaviors that stretch beyond the scope of formal organizational prescription and control. Unfortunately, cultivating enactment of these valued behaviors has proven challenging for many companies. The literature has recognized workplace relationships as important determinants of behavioral security outcomes and extra-role security behaviors (ERBs) in particular. Taken further, an employee's relationship with the immediate supervisor is recognized as one of the most influential relational factors shaping a variety of workplace behaviors, including those related to security. Consistent with these notions, scholars have called for making the employee-supervisor relationship a more central component of behavioral security research and practice. Currently however, beyond recognition of this relationship's importance, the knowledge base is unclear about how it shapes ERB enactment. Because employees view supervisors as both organizational agents and as individuals in their own rights, this relationship has the potential to drive productive or counterproductive security behaviors, depending on how aligned the supervisor's security values are with those of the organization. Yet, the security literature has given surprisingly little consideration to the notion that employees can differ in the extent to which they perceive supervisors as embodying organizational information security values. Responding to this gap, the current study examines how employee-supervisor relations and perceived security-related value alignment between supervisors and the broader organization shape employees’ commitment to organizational information security and ultimately, ERB enactment. Grounded in the social identity theory of leadership (SITL), a research model is developed that positions high-quality employee-supervisor exchange as a direct antecedent of affective commitment to organizational information security, which then serves as a central intrinsic motivational mechanism driving ERB enactment. Further, rooted in SITL's principles on leader prototypicality and supervisor organizational embodiment, employee-perceived value alignment between the immediate supervisor and the organization as a whole—referred to here as supervisor security embodiment (SSE)—is introduced as a critical boundary condition influencing the extent to which employee-supervisor relations drive commitment. Results from model testing empirically demonstrate the value of SSE in explicating how this important relationship shapes workplace ERB enactment, through its influence on affective commitment to organizational information security performance.

Network security AIOps for online stream data monitoring

In cybersecurity, live production data for predictive analysis pose a significant challenge due to the inherently secure nature of the domain. Although there are publicly available, synthesized, and artificially generated datasets, authentic scenarios are rarely encountered. For anomaly-based detection, the dynamic definition of thresholds has gained importance and attention in detecting abnormalities and preventing malicious activities. Unlike conventional threshold-based methods, deep learning data modeling provides a more nuanced perspective on network monitoring. This enables security systems to continually refine and adapt to the evolving situation in streaming data online, which is also our goal. Furthermore, our work in this paper contributes significantly to AIOps research, particularly through the deployment of our intelligent module that cooperates within a monitoring system in production. Our work addresses a crucial gap in the security research landscape toward more practical and effective secure strategies.

Inhibition of LSD1 via SP2509 attenuated the progression of rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial hyperplasia, pannus formation, and cartilage and bone destruction. Lysine-specific demethylase 1 (LSD1), an enzyme involved in transcriptional regulation, has an unclear role in synovial inflammation, fibroblast-like synoviocytes migration, and invasion during RA pathogenesis. In this study, we observed increased LSD1 expression in RA synovial tissues and in TNF-α-stimulated MH7A cells. SP2509, an LSD1 antagonist, directly reduced LSD1 expression and reversed the elevated levels of proteins associated with inflammation, apoptosis, proliferation, and autophagy induced by TNF-α. Furthermore, SP2509 inhibited the migratory capacity of MH7A cells, which was enhanced by TNF-α. In CIA models, SP2509 treatment ameliorated RA development, reducing the expression of pro-inflammatory cytokines and alleviating joint pathological symptoms. These findings underscore the significance of LSD1 in RA and propose the therapeutic potential of SP2509.

From awareness to behaviour: understanding cybersecurity compliance in Vietnam

From awareness to behaviour: understanding cybersecurity compliance in Vietnam

The defining features of a robust information security climate

Data breaches have become a common occurrence with serious consequences, making organizational security management critically important. Nonetheless, the research community has not yet clearly defined the characteristics of a strong organizational security climate. This study conducts a comprehensive literature analysis to identify a collection of research-based and managerially relevant constructs that represent the essential components of a strong security climate. We operationalize the identified measures of the constructs and empirically validate them for reliability, construct validity, and nomological validity in terms of their relationships with employees' security awareness, neutralization, and intention to comply with organizational information security policies (ISPs). The results suggest that these integral elements, when embraced by organizations, discourage employees’ use of neutralization to justify violation of ISPs and improve employees’ security awareness and their ISP compliance intention. Organizations can use the identified subcomponents and their corresponding measures as a diagnostic decision support instrument to make a reliable and valid assessment of the strengths and weaknesses of their security climate, to continuously monitor their security climate, and to develop interventions that contribute to a strong security climate.

Web based Vulnerabilities Modulation: A Comprehensive Study on Web Vulnerabilities

In today's digital landscape, where online activities like banking, shopping, and social networking dominate, the security of sensitive data and assets is paramount. This responsibility primarily lies with software and web developers, tasked with safeguarding against prevalent vulnerabilities such as SQL injection, XSS (Cross-Site Scripting), Broken Access Control, Command Execution, CSRF (Cross- Site Request Forgery), among others. This research project delves into the world of web application security, aiming to provide a comprehensive understanding of secure development and deployment practices. By simulating and analyzing these vulnerabilities across different modules, the study sheds light on how attackers can exploit these weaknesses to breach data confidentiality, integrity, and availability, thus violating the CIA triad. Keywords: web, vulnerability, XSS, CSFR, SQL injection, Access Control.

Evaluating the status of information security management in faculty libraries: a case study of Allameh Tabatabai University

ABSTRACT Aim This study aims to evaluate the status of Information Security Management in Faculty Libraries at Allameh Tabatabai University, Tehran, Iran. This study is based on the standard of ISO/IEC 27,002. Method The research method is a descriptive survey. Data was collected through a researcher-made questionnaire based on the ISO/IEC standard 27,002 which includes 11 main indicators and 39 sub-components. The statistical population of this study is 45 main and middle managers of faculties’ libraries of Allameh Tabatabai University located in Tehran (Faculties of Law, Psychology and Educational Sciences, Management and Accounting, Physical Education, Literature and Foreign Languages, Communication Sciences, Economics, Social Sciences, Mathematical and Computer Sciences). Due to the limited statistical population, sampling was not performed. Data analysis was done using descriptive statistics, inferential, and SPSS software. Findings The average score of information security management performance in the faculty libraries of Allameh Tabatabaei University located in Tehran according to ISO/IEC27002 is above average. Examination of the average scores of performance evaluation indicators of faculty libraries of Allameh Tabatabai University showed that four indicators including Information Security Management Policy, Information Security Incident Management, Cryptography, and Security Supplier Relationships are not in a favorable situation and need attention from the relevant managers. In other indicators, the performance of faculty libraries is at the desired level. Conclusion The results show a desirable level of information security management in the faculty libraries of Allameh Tabatabai in Tehran. However, some indicators, such as Information Security Policies, Information Security Incident Management, Cryptography, and Supplier Relationships, are still weaker. Therefore, to achieve security goals some suggestions are provided.

Formal Analysis and Detection for ROS2 Communication Security Vulnerability

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 (Robot Operating System 2) can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this study, we adopted a formal approach to analyze the security of the communication mechanism of ROS2. First, we used a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduced a CIA model based on the established vulnerability model and used linear temporal logic to define its security properties. Then, we designed and implemented a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally tested some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

ADVANCING DATA SECURITY IN GLOBAL BANKING: INNOVATIVE BIG DATA MANAGEMENT TECHNIQUES

In the context of the rapidly evolving digital landscape, the banking sector faces unprecedented challenges in data security due to the massive volumes of sensitive information they manage. This paper examines the implementation and efficacy of innovative big data management techniques within global banking institutions to enhance data security. It discusses the integration of predictive analytics, the impact of regulatory changes, and the adoption of emerging technologies like blockchain and advanced encryption, which collectively redefine data security strategies. The study utilises a qualitative case study approach focusing on three central banks—JPMorgan Chase & Co., HSBC Holdings plc, and the Industrial and Commercial Bank of China—highlighting how each bank utilises big data techniques to address specific security challenges, comply with regulations, and enhance customer trust. The findings underscore the crucial role of innovative data management strategies in mitigating risks and safeguarding data against cyber threats, suggesting that these technologies fulfil security needs and offer competitive advantages in customer trust and regulatory compliance. The paper concludes with strategic recommendations for banks to enhance their data security measures and suggests directions for future research in data security within the banking industry.

Preface

With great and successful prestigious history of MAEIE, the 2023 3rd International Conference on Mechanical Automation and Electronic Information Engineering (MAEIE 2023) was held in Nanjing, China during 15th to 17th, December 2023 via virtual format, gathering together about 100 delegates from all over the world.MAEIE 2023 is a great academic platform and is of great significance for disseminating cutting-edge information in disciplines and industry, promoting the transformation of scientific and technological achievements, and condensing academic achievements in the industry at this event.The Conference consisted of 3 plenary keynote speeches addressed by well-known scholars internationally (by Prof. Xiaofang Yuan from Hunan University, China, Assoc. Prof. He Chen from Hebei University of Technology, China, Prof. Aniruddha Bhattacharjya from SGT University, India, respectively), 8 oral and 18 poster presentations, in which a wide range of topics were covered and the most recent significant results were presented. They discussed such topics as Research on Three-dimensional Path Planning Methods for Autonomous Vehicles, Control Research for Underactuated Cranes with State Constraints, Use of Blockchain Technology in IoT Maintaining Cia Triad in Data Communication, among others. Besides, the attending scholars, experts, and researchers exchanged deeply on the latest research achievements and advanced experience in the industry development and shared their unique insights into development trends and research directions of the industry.MAEIE 2023 received submissions from home and abroad, but unfortunately some manuscripts did not pass the final review, which therefore doesn’t appear in this Conference Proceedings. The selected works included in the Proceedings are published in Journal of Physics: Conference Series, covering topics on Automated Instrumentation and Equipment, Molding Manufacturing and Automation, Intelligent Manufacturing Technology, Power Electronics Technology, Electronics and Communication Engineering, among others. They show readers the latest research results and application cases of mechanical automation and electronic information engineering. We firmly believe that by reading these papers, readers will deepen their understanding of these fields and appreciate their enormous significance and potential impact on contemporary society.We wish to acknowledge all the participants, and the support of our sponsors. We would also like to take this opportunity to thank the committee members, as well as the expert reviewers, for their endeavor in making the Conference possible and reviewing the manuscripts submitted. What’s more, we hope that the conference series MAEIE shall continue to the next many sessions and deliver its purpose as the arena for presenting and exchanging the ideas of research in the fields of mechanical automation and electronic information engineering and anything related.The Committee of MAEIE 2023List of Committee Member is available in this pdf.

Reflecting on Research Practices

Encouraging the security research community to engage in meta-research.

Information security policy effectiveness: a managerial perspective of the financial industry in Vietnam

Purpose Information security (InfoSec) policy violations are of great concern to all organisations worldwide, especially in the financial industry. Although the importance of InfoSec policy has been highlighted for many decades, InfoSec breaches still occur due to a low level of employee compliance and a lack of engagement and competence in high-level management. However, previous studies have primarily investigated the behavioural aspects of InfoSec policy compliance at the individual level rather than the managerial factors involved in constructing InfoSec policy and developing its effectiveness. Thus, drawing on neo-institutional theory and a transformational leadership framework, this research investigated the influence of external mechanisms and transformational leadership on InfoSec policy effectiveness. Design/methodology/approach The research model was implemented using field survey data from professional managers in the financial sector. Findings The results reported that neo-institutional mechanisms and transformational leadership shape InfoSec policy effectiveness in an organisation. Originality/value This study broadens current InfoSec policy research from an individual level to a managerial perspective and enhances the existing literature on neo-institutional and transformational leadership in the context of InfoSec. It highlights the need to evaluate InfoSec policy based on external factors and to support transformational leadership styles that promote InfoSec policy enforcement and effectiveness.