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This 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. [ABSTRACT FROM AUTHOR]

Robust data security is of paramount importance, particularly for highly classified information. To achieve this, advanced encryption and decryption methods, along with stringent access control and physical security measures, are essential to mitigate the risk of unauthorized access and data exposure. Ghost imaging (GI), a computational imaging technique that operates indirectly, offers a distinct approach compared to traditional spatially resolved imaging techniques. By utilizing computational algorithms, GI enables indirect image reconstruction, making it well‐suited for encryption applications. This proposal introduces a new computational imaging encryption scheme that combines Fourier ghost imaging (FGI) and computational holography. The scheme utilizes an untrained‐tree‐network to generate two camouflaged computer‐generated holograms (CGHs). Integration of CGH‐based steganography and holographic authentication enhances the encoding and decoding processes of FGI encryption. The effectiveness and security of the proposed scheme are rigorously validated through numerical simulations and optical experiments. The results demonstrate promising potential for effectively merging direct and indirect imaging methods in security applications. [ABSTRACT FROM AUTHOR]

Purpose: Paper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms' nutrient source. Mold not only can damage papers' structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper. Methods: Molds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model. Results & Conclusion: In the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation. [ABSTRACT FROM AUTHOR]

The United Nations Social Development Goals ensure the achievement of food security by 2030. Food security is characterized by various aspects, however in this study we focus on economic vulnerability to food insecurity. In particular, we compared two measures: food insecurity levels as defined by the Integrated food security phase classification Global-Partners quantitative scale and those defined by the Latin American and Caribbean Food Security qualitative scale. The qualitative scale only measures nutritional status using specific questions, regardless of food expenditures, while the quantitative scale is based on the share of consumer expenditure spent on food (the ratio between food expenditure and total expenditure). From our analysis on 2018 Mexico data—therefore unaffected by the pandemic—59% of discrepancies were found between the two measures. The qualitative scale does not classify people who spend too much on food as being food insecure, so they are economically vulnerable about housing, healthcare and other important aspects of social life (access to food generate insecurity). The quantitative scale does not identify people who do not have access to high-quality nutrition because they must pay for other fixed expenses, for example for housing or healthcare. These two measures depict two aspects of economic vulnerability to food insecurity, and here we quantify this difference in a developing country. Future research should be at integrating these two aspects into one single indicator based on food and non-food expenditures, so to take into account both the two aspects by using only consumption expenditure data. [ABSTRACT FROM AUTHOR]

National food insecurity early warning systems and food policy interventions need reliable information concerning the classification of food insecurity. The aim of this paper was to produce an acute food insecurity classification in Mozambique, by: i) analyzing food insecurity indicators individually; ii) comparing it with a new integrated analysis of survey-based indicators called the "Matrix Analysis." The Matrix results show more severe classifications than the single indicators for the analyzed districts. The matrix novelty consists on a cross-tabulation of all indicators, allowing a less subjective analysis. Further research is needed on how the Matrix approach could complement national classification systems. [ABSTRACT FROM AUTHOR]

The subject of covert radio agents operating behind enemy lines during World War II has recently taken on a life of its own. In the last five years, there have been no fewer than 16 books written on the subject. It is difficult to pinpoint the reason for the lingering popularity of this genre. Perhaps it is the mystery associated with spies, secrets, clandestine radios, and sabotage, which has been the grist of historical novels and movies for over 80 years. It may also be the fact that highly classified files and documents have been slowly released to the public over the years. Several of the recent authors cite in the prefaces of their books that they are using original information recently uncovered or unclassified. [ABSTRACT FROM AUTHOR]

This article explores the covert intervention by the United States in Guatemala in 1954, which resulted in the overthrow of the country's leftist government. It examines the motivations behind the intervention, questioning whether it was driven by corporate interests or Cold War concerns. The article argues that while the United Fruit Company (UFCO) had significant influence over US decision-makers, the intervention was primarily motivated by fears of communist infiltration. It also discusses new evidence of UFCO's political influence during President Truman's tenure. The article calls for a reevaluation of the impact of business, social, and political relationships on decision-making processes and highlights the extensive influence of UFCO on US policy towards Guatemala. It concludes by discussing the role of US corporations, particularly UFCO, in Guatemala's economy and politics. [Extracted from the article]

This article explores the importance of access to previously classified materials in the field of diplomatic history, particularly in relation to the Cold War period. It discusses the efforts made by organizations like the Society for Historians of American Foreign Relations and the National Security Archive to ensure access to classified materials. The article also examines the significance of public-facing foreign policy statements, such as presidential doctrines, during the Cold War. It provides examples of three different Cold War doctrines: the Eisenhower Doctrine, the Kennedy Doctrine, and the Nixon Doctrine, highlighting their purposes and impacts. The article concludes by discussing the limitations and implications of these doctrines, emphasizing the need to study them to understand the impact of the Cold War on U.S. foreign policy. [Extracted from the article]

"The Declassification Engine: What History Reveals About America's Top Secrets" by Matthew Connelly is a thought-provoking book that sheds light on the state of American archives and access to classified information. Connelly argues that excessive classification of information by the government is hindering public understanding of political events and impeding accountability. He emphasizes the need for a rational, risk management approach to declassification in order to balance national security concerns with the public's right to know. The book also explores the historical origins of secrecy in the US and its implications for democracy. Connelly's research project at Columbia University's History Lab developed algorithms to uncover redacted information in declassified documents, revealing hidden knowledge and challenging the effectiveness of secrecy. The book raises important questions about the role of classification in avoiding accountability and the potential loss of historical records due to the overwhelming volume of classified material. [Extracted from the article]

Introduction Apps that support telemedicine on the battlefield typically run on classified devices and transmit information over classified networks, whereas the medical data the apps create and transmit are unclassified. Current systems treat these data as classified, so a cross-domain solution is required to transfer the data back to an unclassified domain, which adds delays and costs to the process of transmitting critical data needed to treat injured warfighters. To address this gap, ATC-NY developed DroidChamber, which is a software-based Android system that enables multilevel security and which runs on smartphones and tablets. DroidChamber enables warfighters to execute apps in multiple security domains without risking information leakage. Materials and Methods DroidChamber v6 is a collection of mobile device management and security containerization technologies targeting Android 6, and it applies these technologies by leveraging Linux kernel operating-system-level virtualization technologies. DroidChamber v10 takes advantage of Android Work Profiles and Samsung's Knox technology in Android 10, which provides the ability to provision separate "work" and "personal" profiles that securely isolate applications; apps within each profile can further be isolated through the built-in Android controls and the Knox Application Policy. Results DroidChamber is a software-based Android system, so it requires no added hardware to be attached or integrated with an end-user device. DroidChamber's innovation is isolation of specific Android app resources (e.g. networking) enforced with fine-grained security policies. With DroidChamber, a medic can connect their device to different security domains, giving a wider range of access to medical information in a tactical environment. Conclusions DroidChamber improves telemedicine applications by enabling the warfighter to share information without requiring a cross-domain guard that may erroneously block some data. Using DroidChamber, a warfighter can use a single mobile device to manage/transmit data at different security levels, thereby reducing the cost and complexity of a mission. [ABSTRACT FROM AUTHOR]

This article seeks to explore the relation between nuclear physics and secrecy in early Cold War Europe. After World War II, nuclear physics re-emerged from the Manhattan Project as a largely classified field. Over time, the boundary between secret and unclassified information set by the United States moved due to both political and scientific developments. This shifting boundary of secrecy is taken as a place to investigate power relations in the context of Cold War Science. The Netherlands and Norway are two countries with early nuclear programs that tried to move this boundary, in part by building a joint reactor in 1951. Whereas they requested classified information from the US in 1946, their programs developed to a point where the US made requests to classify nuclear information in Europe by 1960. Between 1954 and 1960, the joint reactor program became the site of a multilateral intelligence operation. Secrecy was used as an intelligence tool to spread nuclear disinformation to the Soviet Union. This history shows how (de)classification opened and closed windows of opportunity and sheds light on the effectiveness of classification. [ABSTRACT FROM AUTHOR]

This study analyzes the coverage of the Covid-19 pandemic in two Brazilian television news programs, specifically Fantástico from TV Globo and Domingo Espetacular from Record TV. Our objective is to study how both programs interpreted Jair Bolsonaro's positions during the crisis, marked by populist antagonism that led to a series of attacks on doctors, scientists, and intellectuals through a negationist and anti-science rhetoric. To do so, we conducted a study of a corpus of 308 news articles collected at different moments during the pandemic. The materials were scrutinized based on the intersection of content analysis and framing techniques. This approach allowed us to systematize data on the visibility of the pandemic and understand the frames of meaning proposed by the two channels in the discursivization of the former president's actions. We started with the hypothesis that the coverage exposed Record TV's alignment with the far-right government, which began during the 2018 elections, and highlighted the critical orientation assumed by TV Globo, especially regarding the figure of the president, in the face of his extremism. This sets it apart from a tradition of support for conservative rulers. The results show significant differences in the coverage of the two programs. While TV Globo openly criticized Bolsonaro's positions, aligning with the discourse of science, Record TV, affiliated with the Universal Church of the Kingdom of God, embraced a discourse in line with the negationist agenda, giving voice to anti-science speeches that normalize the populism of the then-president. [ABSTRACT FROM AUTHOR]

The evolution of social media and online platforms created more opportunities to express one's thoughts without any burden. This emergence paved the way to construct more intelligent systems but by taking advantage of these platforms some people display their vengeance by harassing or abusing comments. The attackers leak sensitive information about an individual or an organization through the internet using abusive words, hate comments, threatening information, fake news, etc. So, online shaming detection with an optimum classification mechanism becomes necessary to avoid such negative consequences. In this paper, a novel online shaming detection mechanism using a Convolutional Neural Network Bidirectional Gated Recurrent Unit-based Hybrid Archimedes Tasmanian Devil (CNNBiGRU-HATD) approach is proposed to detect and classify the offensive comments and non-offensive comments. If the comment seems offensive, it is further classified into six categories according to shaming types such as abusive, doxing, Passing Judgment, Ethnic/Religious, Joke/Sarcasm, and Whataboutery. The utilized four Twitter-based datasets are initially preprocessed and feature extracted to improve the classification performance of the CNNBiGRU-HATD approach. After accurate classification, the information from the classified shaming types is collected and authenticated. The proposed approach is simulated using the Python platform. To validate the potential capability of the proposed CNNBiGRU-HATD approach, it is related to the existing methods by various parameters. The experimental result reveals that the proposed CNNBiGRU-HATD method achieves higher accuracy of 96.5% for the labeled hate speech detection dataset. [ABSTRACT FROM AUTHOR]

Classified information is information of vital importance to the State, which must be protected against disclosure, misuse, damage, unauthorized reproduction, destruction, loss or theft in the interest of the State. At present, there are four levels of classification. For each classification level, precise requirements are laid down for the material of the walls, partitions and ceilings of the rooms in which classified information is stored. Several types of materials are defined for each classification level. The objective of this study was to test and determine whether the different types of materials proposed for the Confidential level meet the same level of resistance. A drop weight test via pendulum was used to determine the resistance. A 50 kg weight was used to break through a 60 × 100 cm sample. The impact of the weight was on the exact center of the sample. The result of the tests was that to break through samples of different materials, large differences in the drop height of the weight were required. The most resistant was the specimen made of reinforced concrete, which required 3 impacts from a height of 80 cm to break through. On the contrary, the least resistant were the specimens made of masonry of autoclaved aerated concrete, where after 2 falls from a height of 5 cm, the sample broke into 2 parts. [ABSTRACT FROM AUTHOR]

Based on deep learning, a Dust Ablation Trail Analysis (DATA) code package is developed to detect dust ablation trails in tokamaks, which is intended to analyze a large amount data of tokamak dusts. To validate and benchmark the DATA code package, 2440 plasma jet images are exploited for the training and test of the deep learning DATA code package, since plasma jets resemble the shape and size of dust ablation clouds in tokamaks. After being trained by 1920 plasma jet images, the DATA code package is able to locate 100% plasma jets, classify plasma jets with the accuracy of >99.9%, and output image skeleton information for classified plasma jets. The DATA code package trained by the plasma jet images is also used to analyze the dust ablation trails captured in the Experimental Advanced Superconducting (EAST) tokamak with the satisfactory performance, further verifying its applicability in the fusion dust ablation investigation. Based on its excellent performance presented here, it is demonstrated that our DATA code package is able to automatically identify and analyze dust ablation trails in tokamaks, which can be used for further detailed investigations, such as the three-dimensional reconstruction of dusts and their ablation trails. [ABSTRACT FROM AUTHOR]

The evolution of technology has led to the appearance of smart cities. An essential element in such cities is smart mobility that covers the subjects related to Intelligent Transportation Systems (ITS). The problem is that the ITS vulnerabilities may considerably harm the life quality and safety status of human beings living in smart cities. In fact, software and hardware systems are more exposed to security risks and threats. To reduce threats and secure software design, threat modelling has been proposed as a preventive solution in the software design phase. On the other hand, threat modelling is always criticised for being time consuming, complex, difficult, and error prone. The approach proposed in this study, that is, Automated Security Assistant of Threat Models (ASATM), is an automated solution that is capable of achieving a high level of security assurance. By defining concepts and conceptual modelling as well as implementing automated security assistant algorithms, ASATM introduces a new approach to identifying threats, extracting security requirements, and designing secure software. The proposed approach demonstrates a quantitative classification of security at three levels (insecure, secure, and threat), twelve sub‐levels (nominal scale and colour scale), and a five‐layer depth (human understandability and conditional probability). In this study, to evaluate the effectiveness of our approach, an example with various security parameters and scenarios was tested and the results confirmed the superiority of the proposed approach over the latest threat modelling approaches in terms of method, learning, and model understanding. [ABSTRACT FROM AUTHOR]

The article focuses on food security in the least developed countries, through a case study of the Democratic Republic of Congo (D.R. Congo), a low-income country with a food deficit, with one of the lowest rates of gross domestic product per capita in the world. The article presents the context after gaining independence in 1960, the last five years, starting with 2017 and the current context of D.R. Congo in 2022, briefly developing the demographic situation, which have a direct impact on food security. The study has as inputs the data provided by the Integrated Food Security Phase Classification (IPC). The D.R. Congo continues to live in a particularly unstable political, economic, and social climate. If currently the western part of the country, including the capital Kinshasa, is no longer the scene of violent clashes and demonstrations, in many other provinces armed gangs, non-governmental militias, ex-military and tribal groups are active, carrying out assaults and raids with massacres of civilians. However, in the D.R. Congo, most deaths are not caused by the violence of the ongoing conflict in the African country, but rather by malnutrition and a poor medical system. Thus, food insecurity is one of the first two factors generating deaths in the D.R. Congo. [ABSTRACT FROM AUTHOR]

Phishing is an attack that deceit online users by means of masquerading as a genuine website to pilfer their classified or personal information. This is one among the recognized cybercrime. Disparate phishing website detection systems were recently developed for the purpose of detecting the phishing websites. However, they fail to attain the desired output and are suffered from countless drawbacks like lower accuracy and higher training time. For trouncing such drawbacks, this paper proposes an effectual Hybrid Deep Learning (HDL)-centric Phishing Detection System (PDS) using the MCS-DNN classifier. At first, pre-processing is done on the input dataset for ameliorating its quality. Subsequently, clustering and feature selection (FS) are performed to lessen the processing time and elevate the accuracy using CoK-means and CM-WOA, respectively. The features which are chosen during FS are fed into the MCS-DNN classifier, which classifies the legitimate websites and phishing websites. Lastly, the K-fold cross-validations (KCV) are employed for effectively predicting the proposed system's accurateness. The outcomes highlight the robustness and predictive ability of the proposed PDS to distinguish the phishing as well as legitimate sites. [ABSTRACT FROM AUTHOR]

Background: Although visual locomotion scoring is inexpensive and simplistic, it is also time consuming and subjective. Automated lameness detection methods have been developed to replace the visual locomotion scoring and aid in early and accurate detection. Several types of sensors are measuring traits such as activity, lying behavior or temperature. Previous studies on automatic lameness detection have been unable to achieve high accuracy in combination with practical implementation in a on farm commercial setting. The objective of our research was to develop a prediction model for lameness in dairy cattle using a combination of remote sensor technology and other animal records that will translate sensor data into easy to interpret classified locomotion information for the farmer. During an 11-month period, data from 164 Holstein-Friesian dairy cows were gathered, housed at an Irish research farm. A neck-mounted accelerometer was used to gather behavioral metrics, additional automatically recorded data consisted of milk production and live weight. Locomotion scoring data were manually recorded, using a one-to-five scale (1 = non-lame, 5 = severely lame). Locomotion scores where then used to label the cows as sound (locomotion score 1) or unsound (locomotion score ≥ 2). Four supervised classification models, using a gradient boosted decision tree machine learning algorithm, were constructed to investigate whether cows could be classified as sound or unsound. Data available for model building included behavioral metrics, milk production and animal characteristics. Results: The resulting models were constructed using various combinations of the data sources. The accuracy of the models was then compared using confusion matrices, receiver-operator characteristic curves and calibration plots. The model which achieved the highest performance according to the accuracy measures, was the model combining all the available data, resulting in an area under the curve of 85% and a sensitivity and specificity of 78%. Conclusion: These results show that 85% of this model's predictions were correct in identifying cows as sound or unsound, showing that the use of a neck-mounted accelerometer, in combination with production and other animal data, has potential to replace visual locomotion scoring as lameness detection method in dairy cows. [ABSTRACT FROM AUTHOR]

The purpose of this article is to introduce two items. 1) The Civil Participation Tool, developed by the Council of Europe’s Centre of Expertise for Good Governance (2020) for the purposes of: selecting, evaluating, and classifying civil actors (stakeholders) into groups; selecting the most appropriate forms of participation in decision-making processes; providing accurate information for classified civil actors (stakeholders); developing the most appropriate strategies to activate the processes of participation for different civil actors (stakeholders) based on the assessment of their potential to engage; and enhancing the level of participation. 2) The results of educational pilot projects implemented in 6 Lithuanian municipalities in 2020. These projects confirmed the scientific hypothesis of this article: that more active civil participation in decision-making processes can be expected when the objective differences of local civil actors (stakeholders) are recognized. According to this, different forms of civil participation should be provided as well as different strategies for improving skills and increasing civil engagement in decision-making processes. [ABSTRACT FROM AUTHOR]

Location-based services (LBS) are necessary for obtaining the important details since the user needs vary based on the location. Location-based advertising (LBA) are utilized for abandoning the user location and to offer assistance by using the obtained information. Therefore, an efficient machine learning and metaheuristic-based model referred as GANM is designed for LBS. Initially, the potential location information is evaluated utilizing the geographic information system (GIS). Thereafter, significant features are computed using the location data. Obtained features are then segmented to improve the process of LBS. Adaptive network-based fuzzy inference system (ANFIS) is then utilized to efficiently classify the user data. Finally, the optimization of classified documents is achieved using the nondominated sorting genetic algorithm-III (NSGA-III). Extensive experiments are performed to validate GANM for LBS. Comparative analyses reveal that GANM outperforms the competitive LBS models in terms of F-score, accuracy, sensitivity, specificity, and area under curve by 2.2734%, 2.3981%, 2.3947%, 2.4271%, and 2.1638%, respectively. [ABSTRACT FROM AUTHOR]

This paper asks: to what extent can a dedicated or special committee with access to classified information empower parliaments to oversee major defence procurement decisions? These decisions often involve a mixture of political, military, economic and societal interests. Particularly after episodes of contestation or controversy, questions tend to arise about how to empower parliaments. The central argument in this paper is that being institutionally empowered and incentivised does not guarantee rigorous oversight. The availability of expertise is an oft-ignored factor in studies of parliamentary oversight. An analysis of oversight behaviour by the Belgian Federal Parliament during the acquisition of new fighter jets (2015–2018) shows that members of parliament also need to be capable of mobilising the necessary expertise in order to translate technical information in such a way that it allows them to influence decision-making, which is often executive-dominated. In this way, this paper contributes to managing expectations about the capacity of parliaments to cope with complex military problems. [ABSTRACT FROM AUTHOR]

Susceptible data recognition has become a fundamental requirement in any network administration system. Though, in suitable sharing and usage, the susceptible data could wipe out the user's privacy. So, susceptible data detection and its security re-assurance in a cloud-IoT (Internet of Things) integrated distributive communication network are mandatory. In this paper, the authors have anticipated novel susceptible data detection and re-assurance algorithms. The algorithms are capable to make out the identical attributes from diverse data sources which are précised by the domain expert. In the proposed method, the sensitivity scores of distinct attributes are measured as significant features for susceptible data identification and assurance. However, the distinctions of sensitivity scores will be able to distinguish the susceptible data from the non-susceptible data in a cloud-IoT integrated distributed computing environment. The authors have explicated various ways through which susceptible data may be exposed in the distributed system environment. Moreover, the authors have proposed novel algorithms for the security re-assurance of static/dynamic susceptible data. The decision tables are considered for each of the definite cases of security re-assurance in a cloud-IoT enabled distributive computing platform. These decision tables will facilitate the network managers to validate the legitimacy of the requests which are arriving from various extents of distributive internetworked systems. In this research work, the results of security re-assurance processes of static and dynamic susceptible data are authenticated through the two dimensional (2D) and three dimensional (3D) graphic representations. The two- and three-dimensional graphical representations designate that the requests initiated from inter/intra networks are being traced and the illegitimate requests are being leftover by the automated model in a cloud-IoT environment. This process will avert the attacks generated from identical internet protocol (IP) addresses. As a summing up it can be said that the research paper primarily emphasizes an innovative approach to recognizing the susceptible data in a cloud-IoT integrated distributive environment and the anticipated technique defends the susceptible data from unlawful admittance by the intruders. [ABSTRACT FROM AUTHOR]

Networks play a central role in cyber-security: networks deliver security attacks, suffer from them, defend against them, and sometimes even cause them. This article is a concise tutorial on the large subject of networks and security, written for all those interested in networking, whether their specialty is security or not. To achieve this goal, we derive our focus and organization from two perspectives. The first perspective is that, although mechanisms for network security are extremely diverse, they are all instances of a few patterns. Consequently, after a pragmatic classification of security attacks, the main sections of the tutorial cover the four patterns for providing network security, of which the familiar three are cryptographic protocols, packet filtering, and dynamic resource allocation. Although cryptographic protocols hide the data contents of packets, they cannot hide packet headers. When users need to hide packet headers from adversaries, which may include the network from which they are receiving service, they must resort to the pattern of compound sessions and overlays. The second perspective comes from the observation that security mechanisms interact in important ways, with each other and with other aspects of networking, so each pattern includes a discussion of its interactions. [ABSTRACT FROM AUTHOR]

Security bugs can catastrophically impact our increasingly digital lives. Designing effective tools for detecting and fixing software security bugs requires a deep understanding of security bug characteristics. In this paper, we conducted a comprehensive study on security bugs and proposed the classification criteria for security bug category, that is, root cause, consequence, and location. In addition, we selected 1076 bug reports from five projects (i.e., Apache Tomcat, Apache HTTP Server, Mozilla Firefox, Linux Kernel, and Eclipse) in the NVD for investigation. Finally, we investigated the correlation between the classification results and obtained some findings: (1) memory operation is the most common security bug; (2) the primary root causes of security bugs are CON (Configuration Error), INP (Input Validation Error), and MEM (Memory Error); (3) the severity of more than 40% of security bugs is high; (4) security bugs caused by INP mainly occur on web; and (5) security bugs caused by LOG (Logic Resource Error) usually lead to DoS (Denial of Service). We discussed these findings through data analysis, which can also help developers better understand the characteristics of security bugs. [ABSTRACT FROM AUTHOR]