Security Classification Research Articles

Editorial for Vol.32, No.2

The June 2024 (Vol. 32, No. 2) issue of CIT. Journal of Computing and Information Technology brings four papers from the areas of natural language processing, image processing, network security, and biomedical image classification.

Automated Identification of Sensitive Financial Data Based on the Topic Analysis

Data governance is an extremely important protection and management measure throughout the entire life cycle of data. However, there are still data governance issues, such as data security risks, data privacy breaches, and difficulties in data management and access control. These problems lead to a risk of data breaches and abuse. Therefore, the security classification and grading of data has become an important task to accurately identify sensitive data and adopt appropriate maintenance and management measures with different sensitivity levels. This work started from the problems existing in the current data security classification and grading work, such as inconsistent classification and grading standards, difficult data acquisition and sorting, and weak semantic information of data fields, to find the limitations of the current methods and the direction for improvement. The automatic identification method of sensitive financial data proposed in this paper is based on topic analysis and was constructed by incorporating Jieba word segmentation, word frequency statistics, the skip-gram model, K-means clustering, and other technologies. Expert assistance was sought to select appropriate keywords for enhanced accuracy. This work used the descriptive text library and real business data of a Chinese financial institution for training and testing to further demonstrate its effectiveness and usefulness. The evaluation indicators illustrated the effectiveness of this method in the classification of data security. The proposed method addressed the challenge of sensitivity level division in texts with limited semantic information, which overcame the limitations on model expansion across different domains and provided an optimized application model. All of the above pointed out the direction for the real-time updating of the method.

An Analytical study on Navigating Sustainability Challenges and Opportunities in the era of AI and the Gig Economy

The gig economy, characterized by its flexible work arrangements and independent contractors, has witnessed rapid growth in recent years. However, it faces sustainability challenges related to worker welfare, income security, and job classification. This paper explores the emerging role of artificial intelligence (AI) in the gig economy and its potential impact on these sustainability concerns. Through an analytical lens, it examines the current applications of AI in the gig economy across various platforms and tasks, highlighting both efficiencies and potential biases.The anticipated effects of AI on gig workers including job displacement, skill requirements, and potential for wage changes.The implications of AI for the sustainability of the gig economy, analyzing how it might exacerbate existing challenges or pave the way for improvements in worker protections and platform governance.Potential policy considerations and recommendations to ensure a more sustainable and equitable gig economy in light of AI integration. The paper concludes by emphasizing the need for a nuanced understanding of AI's potential in the gig economy, advocating for proactive measures to address potential disruptions and harness its capabilities to create a more sustainable and rewarding future for the flexible workforce.

A Two-Stage Data Security Classification Model: Taking Securities Firm for Example

In the era of big data, maximizing the circulation and sharing of data on the basis of ensuring its security is a challenge. Data security classification provides an effective solution for data security protection. This paper proposes a two-stage data classification method based on nine CIA (Confidentiality, Integrity, Availability) indicators, and introduces an expert scoring mechanism to achieve quantitative and precise data classification. In the empirical research part, this paper applies the new method to G Securities Company and first classifies the company’s data into 76 categories. Then the second stage of grading is carried out based on the scores of each index of different categories. The results show that the proposed method is more accurate and robust in terms of data protection and in terms of increasing the value of data sharing and circulation.

A Comparative Work to Highlight the Superiority of Mouth Brooding Fish (MBF) over the Various ML Techniques in Password Security Classification

A Comparative Work to Highlight the Superiority of Mouth Brooding Fish (MBF) over the Various ML Techniques in Password Security Classification

THE EFFECT OF FOOD ACQUISITION OUTSIDE EXPENDITURES ON THE FOOD SECURITY OF CASSAVA (Manihot utilissima) FARMER HOUSE- HOLDS IN WONOGIRI REGENCY

As the standard of living increases, the orientation of food consumption shifts from fulfilling intake to fulfilling nutrition. The acquisition of sustenance by means other than farming and government aid can enhance nutritional satisfaction, increasing energy consumption as a measure of food security. This study aimed to assess the impact of food acquisition outside expenditure on the food security of cassava farmer households in Wonogiri Regency. The research was conducted in July 2022. The method of analysis was descriptive quantitative with purposive location determination. A representative sample of 80 households was chosen by applying the proportionate allocation formula. Data were collected using 2 x 24-hour Food Recall. The data analysis in this study focused on three key variables: income, the proportion of food spending, and the Wilcoxon test of energy consumption and food security among farmer households. These variables were examined before and after food acquisition from external sources. The results showed that the average monthly income of farmers was Rp4,985,487.00. The proportion of food expenditure amounted to 33.36 percent. Wilcoxon test showed a significant difference in energy consumption before and after food acquisition outside expenditure. The food security classification of cassava farmer households before the acquisition of food outside expenditure showed that 21.25 percent were classified as food secure, 15 percent were classified as food susceptible, 52.5 percent as food insecure, and 11.25 percent as severely food insecure. After the additional food outside expenditure, the food security classification increased, with 45 percent classified as food secure, 23.75 percent as food susceptible, 25 percent as food insecure, and 6.25 percent as severely food insecure. This finding demonstrates that food utilization from farming and the Non-Cash Food Assistance (Bantuan et al./BPNT) program positively impacts the food security conditions of cassava farmer households in Wonogiri Regency.

Deep Fraud Net: A Deep Learning Approach for Cyber Security and Financial Fraud Detection and Classification

Given the growing dependence on digital systems and the escalation of financial fraud occurrences, it is imperative to implement efficient cyber security protocols and fraud detection methodologies. The threat's dynamic nature often challenges conventional methods, necessitating the adoption of more sophisticated strategies. Individuals depend on pre-established regulations or problem-solving processes, which possess constraints in identifying novel and intricate fraudulent trends. Conventional techniques need help handling noise data and the substantial expenses incurred by false positives and true positives. To tackle these obstacles, the present study introduces Deep Fraud Net, a framework that utilizes deep learning to detect and classify instances of financial fraud and cyber security threats. The Deep Fraud Net system model entails the utilization of a deep neural network to acquire intricate patterns and characteristics from extensive datasets through training. The framework integrates noise reduction methods to enhance the precision of fraud detection and improve the quality of input data. The Deep Fraud Net method attains a precision of 98.85%, accuracy of 93.35%, sensitivity of 99.05%, specificity of 93.16%, false positive rate of 7.34%, and true positive rate of 89.58%. The findings suggest that Deep Fraud Net can effectively detect and categorize instances of fraudulent behavior with a reduced occurrence of misclassifications. The method exhibits potential implications for diverse domains that prioritize robust security and fraud detection, including but not limited to banking, e-commerce, and online transactions.

International health security

The paper proposes the classification of health security as one of the non-military security dimensions of the second generation, determined more by globalization processes than by the end of the Cold War (first generation). The cognitive goal of the article is to identify and analyse the elements of the structure of international health security such as 1) the essence and specificity of securitization of threats to health security; 2) health security threats; 3) the referent object or whom it concerns; and 4) measures to ensure it. Specific to this dimension is the political motivation for its securitization. In the world of interrelated and global mobilities, what is significant for health security is the diversity of the development level, preferred values, and, consequently, the diversity of sensitivity and susceptibility of national healthcare systems to cross-border threats.

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

Security is an important field in the Internet of Things (IoT) systems. The IoT and security are topical domains. Because it was obtained 35,077 document results from the Scopus database. Hence, the AI (Artificial Intelligence) has proven its efficiency in several domains including security, digital marketing, healthcare, big data, industry, education, robotic, and entertainment. Thus, the contribution of AI to the security of IoT systems has become a huge breakthrough. This contribution adopts the artificial intelligence (AI) as a base solution for the IoT security systems. Two different subsets of AI algorithms were considered: Machine Learning (ML) and Deep Learning (DL) methods. Nevertheless, it is difficult to determine which AI method and IoT dataset are best (more suitable) for classifying and/or detecting intrusions and attacks in the IoT domain. The large number of existing publications on this phenomenon explains the need for the current state of research that covers publications on IoT security using AI methods. Thus, this study compares the results regarding AI algorithms that have been mentioned in the related works. The goal of this paper is to compare the performance assessment of the existing AI algorithms in order to choose the best algorithm as well as whether the chosen algorithm can be used for classifying or/and detecting intrusions and attacks in order to improve security in the IoT domain. This study compares these methods in term of accuracy rate. Evaluating the current state of IoT security, AI and IoT datasets is the main aim for considering our future work. After that, this paper proposes, as result, a new and general taxonomy of AI techniques for IoT security (classification and detection techniques). Finally, the obtained results from this assessment survey that was dedicated to research conducted between 2018 and 2023 were satisfactory. This paper provides a good reference for researchers and readers in the IoT domain.

An ensemble based data mining model for contingency analysis of power system under STLO

<span lang="EN-US">In a large, interconnected power system, contingency analysis is a useful tool for pinpointing the potential consequences of post-event scenarios on the system's safety. In this work, the Newton-Raphson technique is applied to every single outage of a transmission line to compute the load flows. For the static security classification of the power system, the line voltage stability performance index (LVSI) is used. There are three levels of static security of power system namely: non-critical (the least severe), semi-critically insecure (the next lowest severe), and critical (the next highest severe). The various data mining techniques such as decision trees, bagging-based ensemble methods, and boosting-based ensemble methods were applied to assess the severity of the line under various loading and contingency conditions. Test systems based on the IEEE 30 bus system were used with the proposed machine learning classifiers. The experimental results proved that bagging based ensemble method provided better accuracy compared to the decision tree and the AdaBoost ensemble method for predicting the power system security assessment. The bagging-based ensemble method has a predictive accuracy of 85% and an AUC of 0.94.</span>

CURRENT ISSUES OF THE ORGANIZATION STAFF SECURITY

The scientific article is devoted to the study of topical issues of employee safety in organizations. Within the framework of the research topic, the concepts of "personnel security", "shadow employment", "staff turnover" are considered. In the course of the research, a generalized classification of personnel security is proposed.

Scheduling algorithms for data-protection based on security-classification constraints to data-dissemination

Communication networks have played a vital role in changing people’s life. However, the rapid advancement in digital technologies has presented many drawbacks of the current inter-networking technology. Data leakages severely threaten information privacy and security and can jeopardize individual and public life. This research investigates the creation of a private network model that can decrease the number of data leakages. A two-router private network model is designed. This model uses two routers to manage the classification level of the transmitting network packets. In addition, various algorithmic techniques are proposed. These techniques solve a scheduling problem. This problem is to schedule packets through routers under a security classification level constraint. This constraint is the non-permission of the transmission of two packets that belongs to the same security classification level. These techniques are the dispatching rule and grouping method. The studied problem is an NP-hard. Eight algorithms are proposed to minimize the total transmission time. A comparison between the proposed algorithms and those in the literature is discussed to show the performance of the proposed scheme through experimentation. Four classes of instances are generated. For these classes, the experimental results show that the best-proposed algorithm is the best-classification groups’ algorithm in 89.1% of cases and an average gap of 0.001. In addition, a benchmark of instances is used based on a real dataset. This real dataset shows that the best-proposed algorithm is the best-classification groups’ algorithm in 88.6% of cases and an average gap of less than 0.001.

The Classification of Federally Sentenced Women in Canada: Addition of Gender-Informed Variables to the Custody Rating Scale Contributes Incremental Predictive Validity

The Correctional Service of Canada (CSC) uses the Custody Rating Scale (CRS) for initial security classification; it is gender-neutral. Gender-informed scholars contend that gender-neutral assessments are problematic for use with justice-impacted women, as they exclude factors (e.g., victimization) deemed more relevant for women. Using an archival database with 1,555 federally sentenced women in Canada, we examined the extent that gender-informed indicators could yield incremental predictive validity (predicting institutional misconduct) beyond the CRS. Specifically, gender-informed variables from these domains were tested: mental health, substance misuse, relationship dysfunction, personal/emotional difficulties, parental/family issues, and victimization. Results revealed at least one gender-informed variable from each domain significantly predicted institutional misconducts. Composite gender-informed scales were created from the set of significant gender-informed predictors. Area under the curve (AUC) and hierarchical Cox regression analyses revealed the composite gender-informed scales contributed incremental predictive validity above and beyond the CRS. Although the CRS was predictive, it can be improved by including gender-informed variables.

An Efficient Attribute-Based Encryption Scheme with Data Security Classification in the Multi-Cloud Environment

As an increasing number of people and corporations move their data to the cloud side, how to ensure efficient and secure access to data stored on the cloud side has become a key focus of current research. Attribute-Based Encryption (ABE) is largely recognized as the best access control method for safeguarding the cloud storage environment, and numerous solutions based on ABE have been developed successively. However, the majority of current research is conducted within a single cloud provider, and only the limited number of schemes for the multi-cloud environment also fail to support the data security classification on the cloud side. Therefore, we propose an efficient attribute-based encryption scheme with data security classification in the multi-cloud environment. In our scheme, the data owner’s data are divided into two security levels and stored in different cloud providers, which improves the security of outsourcing data. Moreover, based on Ciphertext-Policy Attribute-Based Encryption (CP-ABE), our scheme can not only provide a fine-grained access control for the data user, but also completely exploit the cloud side to facilitate outsourcing decryption to lighten the data user’s computing load. The security analysis showed that our scheme is effective against selective-attribute plaintext attack, as well as protects the privacy of the data. The experimental results also demonstrated that the computational overhead is obviously less than other existing schemes.

A review of secure federated learning: Privacy leakage threats, protection technologies, challenges and future directions

Advances in the new generation of Internet of Things (IoT) technology are propelling the growth of intelligent industrial applications worldwide. Simultaneously, widespread adoption of artificial intelligence (AI) technologies, such as machine and deep learning, is accelerating. Traditional machine learning models rely heavily on massive amounts of data, however collecting and processing massive amounts of data generated by network-edge devices is costly and inefficient, and poses serious risks to data privacy. As a new paradigm for statistical model training in distributed edge networks, federated learning (FL) enables data to participate in federated model training without being localized. This approach can be used to solve traditional machine learning problems of low data utilization, data privacy, and information security caused by data isolation. However, the defects of the FL framework and insecure network environments cause many security and privacy leakage problems in actual application scenarios of FL. First, the concepts, classifications, and fundamental FL principles were described. Second, the mainstream privacy security issues and classification of FL were investigated. Privacy security protection techniques for FL were then identified. Finally, challenges and future research directions for the development of FL privacy security are discussed.

The Historical Presidency: Rethinking the origins of national security classification

AbstractThis article reconstructs the bureaucratic and legal processes that culminated in the creation of the modern national security classification system in Executive Order No. 10,290, issued by Harry Truman in 1951. It argues that classification was shaped by processes of improvisation endogenous to the federal bureaucracy, which produced the problems of overclassification, definitional vagueness, and ambiguous constitutional status that have haunted the secrecy regime until the present. In so doing, it provides new insight into the development of the modern presidency, the national security state, and American democracy, and suggests possible paths to reform the contemporary pathologies of the classification system.

Food Processing: An overview on links between safety, security, supply chains, and NOVA classification

Food Processing: An overview on links between safety, security, supply chains, and NOVA classification

Kajian Sistem Klasifikasi Keamanan dan Akses Arsip di Depo Arsip Kabupaten Bandung

Archive management is supported by four pillars which will determine the effectivity and efficiency of archival managing work. The pillars consist of Official Script Layout, Archives Classification Codes, Archives Retention Schedules, and Archives Access and Security Classification Systems. This paper mainly discussed one of the pillars that supported archive management activity which was Archives Access and Security Classification Systems. This research was conducted by means of descriptive qualitative methods, which aimed for gaining clarity in regards of the subject of study. As the role model of other archival institutions or work units, ANRI issued a regulation regarding Archives Access and Security Classification Systems to maintain security, confidentiality, and physical form of archives. Result shows that Archives Access and Security Classification Systems issued by the district government of Bandung Regency has covered the means of protecting the confidentiality of archives. The regulation, specifically Regent Regulations Number 130 Year 2018, covered these following aspects; archive classification, access classification, means and infrastructure, along with various types of users. This paper will also include sets of examples published within the official regulation to ensure that the information given is clear and detailed.

Issues of Misinformation, Media Manipulation, Propaganda in Cyberspace

The internet creates a virtual space where individuals strive to capture their dreams, art, and culture. Within it, one can find the greatness and dignity of humanity, but also the baseness and wickedness that signify the decline of the spirit. The internet has drastically diminished the significance of space and distance in social interactions. It has enabled the crossing of temporal boundaries, provided anonymity in contacts, and facilitated access to previously inaccessible information, including open educational resources and sources of cultures different from one’s own. The language of digital media is a system of signs. Without understanding them, contemporary individuals become slaves, and their lives become meaningless and purposeless play. Illiteracy and low computer and media literacy can become sources of social and cultural manipulation on an unprecedented scale. Information networks connect not only universities, businesses, and people, but they have also become tools for playing out cultural, social, economic, political rivalries, as well as criminal and terrorist activities, and more recently, military actions. The COVID-19 pandemic and the war in Ukraine have their dramatic images in the real world, but also in cyberspace. This article fills a gap in this area by addressing the issues of misinformation, media manipulation, propaganda, and infodemics. It presents a classification of security threats to children, youth, and adults and describes selected initiatives undertaken in this regard by the European Union. The author draws upon humanistic and social thought, pointing out avenues for analysis and ways to counteract negative consequences.

Blockchain-Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using Optimal Deep Learning Model

Blockchain (BC) and Machine learning (ML) technologies have been investigated for potential applications in medicine with reasonable success to date. On the other hand, as accurate and early diagnosis of skin lesion classification is essential to gradually increase the survival rate of the patient, Deep-Learning (DL) and ML technologies were introduced for supporting dermatologists to overcome these challenges. This study designed a Blockchain Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using an Optimal Deep Learning (BHESKD-ODL) model. The presented BHESKD-ODL model achieves security and proper classification of skin lesion images using BC to store the medical images of the patients to restrict access to third-party users or intruders. In addition, the BHESKD-ODL method secures the medical images using the mayfly optimization (MFO) algorithm with the Homomorphic Encryption (HE) technique. For skin lesion diagnosis, the proposed BHESKD-ODL method uses pre-processing and the Adam optimizer with a Fully Convolutional Network (FCN) based segmentation process. Furthermore, a radiomics feature extraction with a Bidirectional Recurrent Neural Network (BiRNN) model was employed for skin lesion classification. Finally, the Red Deer Optimization (RDO) algorithm was used for the optimal hyperparameter selection of the BiRNN approach. The experimental results of the BHESKD-ODL system on a benchmark skin dataset proved its promising performance in terms of different measures.