基于Focal-EIOU函数的被动式太赫兹图像违禁物品识别

针对被动式太赫兹安检系统因环境影响导致图像质量波动,从而影响识别算法,导致准确率大幅降低的问题,提出了基于Focal-EIOU损失函数的改进YOLOv4算法,并用被动式太赫兹人体安检图像对刀、枪违禁物品进行模型训练获得模型。建立不同环境、不同位置角度携带刀枪嫌疑物人员的太赫兹图像数据库,采用图像增广的方法构建丰富数据集;将YOLOv4的CIOU loss改进为Focal-EIOU loss,提高算法对太赫兹图像识别的鲁棒性,进而经过训练获得较优的模型。在本文的测试集中,使用改进后的算法训练的模型平均检测精确度(mAP)达到96.4%,检测速度在28 ms左右,交并比(IOU)平均值为0.95,在同等条件下高于常规算法,改善了检测识别的效果。实验结果表明,本文方法能够有效提高被动式太赫兹人体安检系统的嫌疑物识别准确率,有利于该项技术在人体安检领域的推广应用。     

Extracting embedded messages using adaptive steganography based on optimal syndrome-trellis decoding paths

Privacy protection is the key to maintaining the Internet of Things (IoT) communication strategy. Steganography is an important way to achieve covert communication that protects user data privacy. Steganalysis technology is the key to checking steganography security, and its ultimate goal is to extract embedded messages. Existing methods cannot extract under known cover images. To this end, this paper proposes a method of extracting embedded messages under known cover images. First, the syndrome-trellis encoding process is analyzed. Second, a decoding path in the syndrome trellis is obtained by using the stego sequence and a certain parity-check matrix, while the embedding process is simulated using the cover sequence and parity-check matrix. Since the decoding path obtained by the stego sequence and the correct parity-check matrix is optimal and has the least distortion, comparing the path consistency can quickly filter the coding parameters to determine the correct matrices, and embedded messages can be extracted correctly. The proposed method does not need to embed all possible messages for the second time, improving coding parameter recognition significantly. The experimental results show that the proposed method can identify syndrome-trellis coding parameters in stego images embedded by adaptive steganography quickly to realize embedded message extraction.     

Firm diversity and data breach risk: A longitudinal study

Research has extensively investigated the rationale of firm diversity from the economic perspective, but little is known about how such a strategy may affect information security. The present study is the first to examine how firm diversity is relevant to firms’ likelihood to experience data breaches (i.e., data breach risk). Drawing from the strands of literature on information security, diversification, and resource-based view, we propose hypotheses on the relationship between firm diversity and data breach risk, as well as the boundary conditions of this relationship. On the basis of a twelve-year sample of publicly-listed firms, our analysis provides evidence to support the negative association between firm diversity and data breach risk. Our analysis also delineates conditions under which the effects of firm diversity can intervene to reduce the data breach risk invoked, such as under related diversity and when managers are managerially capable. For academics, our research accentuates an intriguing but unexamined benefit of firm diversity because it relates to information security. For practicing professionals, this research highlights the significant impact of firms’ operational structure on information security.     

A Proposed Biometric Authentication Model to Improve Cloud Systems Security

Most user authentication mechanisms of cloud systems depend on the credentials approach in which a user submits his/her identity through a username and password. Unfortunately, this approach has many security problems because personal data can be stolen or recognized by hackers. This paper aims to present a cloud-based biometric authentication model (CBioAM) for improving and securing cloud services. The research study presents the verification and identification processes of the proposed cloud-based biometric authentication system (CBioAS), where the biometric samples of users are saved in database servers and the authentication process is implemented without loss of the users’ information. The paper presents the performance evaluation of the proposed model in terms of three main characteristics including accuracy, sensitivity, and specificity. The research study introduces a novel algorithm called “Bio_Authen_as_a_Service” for implementing and evaluating the proposed model. The proposed system performs the biometric authentication process securely and preserves the privacy of user information. The experimental result was highly promising for securing cloud services using the proposed model. The experiments showed encouraging results with a performance average of 93.94%, an accuracy average of 96.15%, a sensitivity average of 87.69%, and a specificity average of 97.99%.     

Adaptive Particle Swarm Optimization Data Hiding for High Security Secret Image Sharing

The main aim of this work is to improve the security of data hiding for secret image sharing. The privacy and security of digital information have become a primary concern nowadays due to the enormous usage of digital technology. The security and the privacy of users’ images are ensured through reversible data hiding techniques. The efficiency of the existing data hiding techniques did not provide optimum performance with multiple end nodes. These issues are solved by using Separable Data Hiding and Adaptive Particle Swarm Optimization (SDHAPSO) algorithm to attain optimal performance. Image encryption, data embedding, data extraction/image recovery are the main phases of the proposed approach. DFT is generally used to extract the transform coefficient matrix from the original image. DFT coefficients are in float format, which assists in transforming the image to integral format using the round function. After obtaining the encrypted image by data-hider, additional data embedding is formulated into high-frequency coefficients. The proposed SDHAPSO is mainly utilized for performance improvement through optimal pixel location selection within the image for secret bits concealment. In addition, the secret data embedding capacity enhancement is focused on image visual quality maintenance. Hence, it is observed from the simulation results that the proposed SDHAPSO technique offers high-level security outcomes with respect to higher PSNR, security level, lesser MSE and higher correlation than existing techniques. Hence, enhanced sensitive information protection is attained, which improves the overall system performance.     

Advanced Authentication Mechanisms for Identity and Access Management in Cloud Computing

Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes. The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources. The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users. Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome these issues. Another major concern in IAM services is the excessive or the lack of access level to different users with previously granted authorizations. This paper proposes a comprehensive review of security services and threats. Based on the presented services and threats, advanced frameworks for IAM that provide authentication mechanisms in public and private cloud platforms. A threat model has been applied to validate the proposed authentication frameworks with different security threats. The proposed models proved high efficiency in protecting cloud platforms from insider attacks, single sign-on failure, brute force attacks, denial of service, user privacy threats, and data privacy threats.     

An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago

The sharp rise in urban crime rates is becoming one of the most important issues of public security, affecting many aspects of social sustainability, such as employment, livelihood, health care, and education. Therefore, it is critical to develop a predictive model capable of identifying areas with high crime intensity and detecting trends of crime occurrence in such areas for the allocation of scarce resources and investment in the prevention and reduction of criminal strategies. This study develops a predictive model based on K-means clustering, signal decomposition technique, and neural networks to identify crime distribution in urban areas and accurately forecast the variation tendency of the number of crimes in each area. We find that the time series of the number of crimes in different areas show a correlation in the long term, but this long-term effect cannot be reflected in the short period. Therefore, we argue that short-term joint law enforcement has no theoretical basis because data show that spatial heterogeneity and time lag cannot be timely reflected in short-term prediction. By combining the temporal and spatial effects, a high-precision anticrime information support system is designed, which can help the police to implement more targeted crime prevention strategies at the micro level.     

Intrusion Detection Systems Using Blockchain Technology: A Review,Issues and Challenges

Intrusion detection systems that have emerged in recent decades can identify a variety of malicious attacks that target networks by employing several detection approaches. However, the current approaches have challenges in detecting intrusions, which may affect the performance of the overall detection system as well as network performance. For the time being, one of the most important creative technological advancements that plays a significant role in the professional world today is blockchain technology. Blockchain technology moves in the direction of persistent revolution and change. It is a chain of blocks that covers information and maintains trust between individuals no matter how far apart they are. Recently, blockchain was integrated into intrusion detection systems to enhance their overall performance. Blockchain has also been adopted in healthcare, supply chain management, and the Internet of Things. Blockchain uses robust cryptography with private and public keys, and it has numerous properties that have leveraged security’s performance over peer-to-peer networks without the need for a third party. To explore and highlight the importance of integrating blockchain with intrusion detection systems, this paper provides a comprehensive background of intrusion detection systems and blockchain technology. Furthermore, a comprehensive review of emerging intrusion detection systems based on blockchain technology is presented. Finally, this paper suggests important future research directions and trending topics in intrusion detection systems based on blockchain technology.     

关于建立健全交通基础设施长寿命安全保障体系的战略思考

本文主要针对我国交通基础设施存在的结构病害严重、"短寿命"突出、"老龄化"日益凸显、长寿命安全服役面临严峻挑战等问题,借鉴发达国家的先进经验,提出了我国交通基础设施长寿命安全保障体系建设的总体目标、战略任务及相关建议,并开展了交通基础设施长寿命安全保障体系的系统研究和建设工作,对确保其长寿命安全服役和交通大动脉的安全畅通、支撑国民经济可持续发展、保障人民生活安稳有序、助力国家安全和社会稳定具有十分重要的战略意义。     

Designing a cellular manufacturing system considering decision style,skill and job security by NSGA-II and response surface methodology

Cell formation is a traditional problem in cellular manufacturing systems that concerns the allocation of parts, operators and machines to the cells. This paper presents a new mathematical programming model for cell formation in which operators’ personality and decision-making styles, skill in working with machines, and also job security are incorporated simultaneously. The model involves the following five objectives: (1) minimising costs of adding new machines to and removing machines from the cells at the beginning of each period, (2) minimising total cost of material handling, (3) maximising job security, (4) minimising inconsistency of operators’ decision styles in cells and (5) minimising cost of suitable skill. On account of the NP-hard nature of the proposed model, NSGA-II as a powerful meta-heuristic approach is used for solving large-sized problems. Furthermore, response surface methodology (RSM) is used for tuning the parameters. Lastly, MOPSO and two scalarization methods are employed for validation of the results obtained. To the best of our knowledge, this is the first study that presents a multi-objective mathematical model for cell formation problem considering operators’ personality and skill, addition and removal of machines and job security.