Security Analysis in Smart Agriculture: Insights from a Cyber-Physical System Application

Smart agriculture modifies traditional farming practices,and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies.In today’s world where technology is everything,these technologies are utilized to streamline regular tasks and procedures in agriculture,one of the largest and most significant industries in every nation.This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems.Divided into three main sections-security analysis,system architecture and design and risk assessment of Cyber-Physical Systems(CPS)applications-the study delves into various elements crucial for smart farming,such as data sources,infrastructure components,communication protocols,and the roles of different stakeholders such as farmers,agricultural scientists and researchers,technology providers,government agencies,consumers and many others.In contrast to earlier research,this work analyzes the resilience of smart agriculture systems using approaches such as threat modeling,penetration testing,and vulnerability assessments.Important discoveries highlight the concerns connected to unsecured communication protocols,possible threats from malevolent actors,and vulnerabilities in IoT devices.Furthermore,the study suggests enhancements for CPS applications,such as strong access controls,intrusion detection systems,and encryption protocols.In addition,risk assessment techniques are applied to prioritize mitigation tactics and detect potential hazards,addressing issues like data breaches,system outages,and automated farming process sabotage.The research sets itself apart even more by presenting a prototype CPS application that makes use of a digital temperature sensor.This application was first created using a Tinkercad simulator and then using actual hardware with Arduino boards.The CPS application’s defenses against potential threats and vulnerabilities are strengthened by this integrated approach,which distinguishes this research for its depth and usefulness in the field of smart agriculture security.

A Review on the Security of the Ethereum-Based DeFi Ecosystem

Decentralized finance(DeFi)is a general term for a series of financial products and services.It is based on blockchain technology and has attracted people’s attention because of its open,transparent,and intermediary free.Among them,the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention.However,the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years.Herein,we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues.To that end,we investigate the Ethereum-based DeFi security issues:1)inherited from the real-world financial system,which can be solved by macro-control;2)induced by the problems of blockchain architecture,which require a better blockchain platform;3)caused by DeFi invented applications,which should be focused on during the project development.Based on that,we further discuss the current solutions and potential directions ofDeFi security.According to our research,we could provide a comprehensive vision to the research community for the improvement of Ethereum-basedDeFi ecosystem security.

Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world software.The existing analysis of software security vulnerabilities often focuses on specific features or modules.This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software.The key novelty lies in overcoming the constraints of partial approaches.The proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security guidelines.Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each product.These guidelines are not only practical but also applicable in real-world software,allowing for prioritized security responses.The proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related software.The analysis resulted in the identification of a total of 121 vulnerabilities.The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

Intelligent Solution System for Cloud Security Based on Equity Distribution:Model and Algorithms

In the cloud environment,ensuring a high level of data security is in high demand.Data planning storage optimization is part of the whole security process in the cloud environment.It enables data security by avoiding the risk of data loss and data overlapping.The development of data flow scheduling approaches in the cloud environment taking security parameters into account is insufficient.In our work,we propose a data scheduling model for the cloud environment.Themodel is made up of three parts that together help dispatch user data flow to the appropriate cloudVMs.The first component is the Collector Agent whichmust periodically collect information on the state of the network links.The second one is the monitoring agent which must then analyze,classify,and make a decision on the state of the link and finally transmit this information to the scheduler.The third one is the scheduler who must consider previous information to transfer user data,including fair distribution and reliable paths.It should be noted that each part of the proposedmodel requires the development of its algorithms.In this article,we are interested in the development of data transfer algorithms,including fairness distribution with the consideration of a stable link state.These algorithms are based on the grouping of transmitted files and the iterative method.The proposed algorithms showthe performances to obtain an approximate solution to the studied problem which is an NP-hard(Non-Polynomial solution)problem.The experimental results show that the best algorithm is the half-grouped minimum excluding(HME),with a percentage of 91.3%,an average deviation of 0.042,and an execution time of 0.001 s.

Security and Privacy Challenges in SDN-Enabled IoT Systems: Causes, Proposed Solutions,and Future Directions

Software-Defined Networking(SDN)represents a significant paradigm shift in network architecture,separating network logic from the underlying forwarding devices to enhance flexibility and centralize deployment.Concur-rently,the Internet of Things(IoT)connects numerous devices to the Internet,enabling autonomous interactions with minimal human intervention.However,implementing and managing an SDN-IoT system is inherently complex,particularly for those with limited resources,as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN integration.The findings of this study underscore the primary security and privacy challenges across application,control,and data planes.A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy protection.Recent investigations have explored cutting-edge methods,such as leveraging blockchain for transaction recording to enhance security and privacy,along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service(DoS)and Distributed DoS(DDoS)attacks.Moreover,the analysis indicates that encryption and hashing techniques are prevalent in the data plane,whereas access control and certificate authorization are prominently considered in the control plane,and authentication is commonly employed within the application plane.Additionally,this paper outlines future directions,offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem.

A Security Trade-Off Scheme of Anomaly Detection System in IoT to Defend against Data-Tampering Attacks

Internet of Things(IoT)is vulnerable to data-tampering(DT)attacks.Due to resource limitations,many anomaly detection systems(ADSs)for IoT have high false positive rates when detecting DT attacks.This leads to the misreporting of normal data,which will impact the normal operation of IoT.To mitigate the impact caused by the high false positive rate of ADS,this paper proposes an ADS management scheme for clustered IoT.First,we model the data transmission and anomaly detection in clustered IoT.Then,the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device.In the presence of a high false positive rate in ADSs,to deal with the trade-off between the security and availability of data,we develop a linear programming model referred to as a security trade-off(ST)model.Next,we develop an analysis framework for the ST model,and solve the ST model on an IoT simulation platform.Last,we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis.Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS.

High-Level Security Dimension Evaluation of Blockchain BaaS System and Key Technology

In recent years,blockchain technology integration and application has gradually become an important driving force for new technological innovation and industrial transformation.While blockchain technology and applications are developing rapidly,the emerging security risks and obstacles have gradually become prominent.Attackers can still find security issues in blockchain systems and conduct attacks,causing increasing losses from network attacks every year.In response to the current demand for blockchain application security detection and assessment in all industries,and the insufficient coverage of existing detection technologies such as smart contract detectiontechnology,this paper proposes a blockchain core technology security assessment system model,and studies the relevant detection and assessment key technologies and systems.A security assessment scheme based on a smart contract and consensus mechanism detection scheme is designed.And the underlying blockchain architecture supports the traceability of detection results using super blockchains.Finally,the functionality and performance of the system were tested,and the test results show that the model and solutions proposed in this paper have good feasibility.

NFT Security Matrix:Towards Modeling NFT Ecosystem Threat

Digital assets have boomed over the past few years with the emergence of Non-fungible Tokens(NFTs).To be specific,the total trading volume of digital assets reached an astounding$55.5 billion in 2022.Nevertheless,numerous security concerns have been raised by the rapid expansion of the NFT ecosystem.NFT holders are exposed to a plethora of scams and traps,putting their digital assets at risk of being lost.However,academic research on NFT security is scarce,and the security issues have aroused rare attention.In this study,the NFT ecological process is comprehensively explored.This process falls into five different stages encompassing the entire lifecycle of NFTs.Subsequently,the security issues regarding the respective stage are elaborated and analyzed in depth.A matrix model is proposed as a novel contribution to the categorization of NFT security issues.Diverse data are collected from social networks,the Ethereum blockchain,and NFT markets to substantiate our claims regarding the severity of security concerns in the NFT ecosystem.From this comprehensive dataset,nine key NFT security issues are identified from the matrix model and then subjected to qualitative and quantitative analysis.This study aims to shed light on the severity of NFT ecosystem security issues.The findings stress the need for increased attention and proactive measures to safeguard the NFT ecosystem.

Impacts of multi-scenario land use change on ecosystem services and ecological security pattern: A case study of the Yellow River Delta

The Yellow River Delta(YRD), a critical economic zone along China's eastern coast, also functions as a vital ecological reserve in the lower Yellow River. Amidst rapid industrialization and urbanization, the region has witnessed significant land use/cover changes(LUCC), impacting ecosystem services(ES) and ecological security patterns(ESP). Investigating LUCC's effects on ES and ESP in the YRD is crucial for ecological security and sustainable development. This study utilized the PLUS model to simulate 2030 land use scenarios, including natural development(NDS), economic development(EDS), and ecological protection scenarios(EPS). Subsequently, the InVEST model and circuit theory were applied to assess ES and ESP under varying LUCC scenarios from 2010 to 2030. Findings indicate:(1) Notable LUCC from 2010 to 2030, marked by decreasing cropland and increasing construction land and water bodies.(2) From 2010 to 2020, improvements were observed in carbon storage,water yield, soil retention, and habitat quality, whereas 2020–2030 saw increases in water yield and soil retention but declines in habitat quality and carbon storage. Among the scenarios, EPS showed superior performance in all four ES.(3) Between 2010 and 2030, ecological sources, corridors, and pinchpoints expanded, displaying significant spatial heterogeneity. The EPS scenario yielded the most substantial increases in ecological sources,corridors, and pinchpoints, totaling 582.89 km^(2), 645.03 km^(2),and 64.43 km^(2), respectively. This study highlights the importance of EPS, offering insightful scientific guidance for the YRD's sustainable development.

Surgical treatment of liver cancer and pancreatic cancer under the China Healthcare Security Diagnosis Related Groups payment system

BACKGROUND Data from the World Health Organization’s International Agency for Research on Cancer reported that China had the highest prevalence of cancer and cancer deaths in 2022.Liver and pancreatic cancers accounted for the highest number of new cases.Real-world data(RWD)is now widely preferred to traditional clinical trials in various fields of medicine and healthcare,as the traditional research approach often involves highly selected populations and interventions and controls that are strictly regulated.Additionally,research results from the RWD match global reality better than those from traditional clinical trials.AIM To analyze the cost disparity between surgical treatments for liver and pancreatic cancer under various factors.METHODS This study analyzed RWD 1137 cases within the HB1 group(patients who underwent pancreatectomy,hepatectomy,and/or shunt surgery)in 2023.It distinguished different expenditure categories,including medical,nursing,technical,management,drug,and consumable costs.Additionally,it assessed the contribution of each expenditure category to total hospital costs and performed cross-group comparisons using the non-parametric Kruskal–Wallis test.This study used the Steel–Dwass test for post-hoc multiple comparisons and the Spearman correlation coefficient to examine the relationships between variables.RESULTS The study found that in HB11 and HB13,the total hospitalization costs were significantly higher for pancreaticoduodenectomy than for pancreatectomy and hepatectomy.Although no significant difference was observed in the length of hospital stay between patients who underwent pancreaticoduodenectomy and pancreatectomy,both were significantly longer than those who underwent liver resection.In HB15,no significant difference was observed in the total cost of hospitalization between pancreaticoduodenectomy and pancreatectomy;however,both were significantly higher than those in hepatectomy.Additionally,the length of hospital stay was significantly longer for patients who underwent pancreaticoduodenectomy than for those who underwent pancreatectomy or liver resection.CONCLUSION China Healthcare Security Diagnosis Related Groups payment system positively impacts liver and pancreatic cancer surgeries by improving medical quality and controlling costs.Further research could refine this grouping system and ensure continuous effectiveness and sustainability.

Cost analysis of radical resection of malignant breast tumors under the China Healthcare Security Diagnosis Related Groups payment system

BACKGROUND Breast cancer is one of the most common malignant tumors in women worldwide and poses a severe threat to their health.Therefore,this study examined patients who underwent breast cancer surgery,analyzed hospitalization costs and structure,and explored the impact of China Healthcare Security Diagnosis Related Groups(CHS-DRG)management on patient costs.It aimed to provide medical institutions with ways to reduce costs,optimize cost structures,reduce patient burden,and improve service efficiency.AIM To study the CHS-DRG payment system’s impact on breast cancer surgery costs.METHODS Using the CHS-DRG(version 1.1)grouping criteria,4073 patients,who underwent the radical resection of breast malignant tumors from January to December 2023,were included in the JA29 group;1028 patients were part of the CHS-DRG payment system,unlike the rest.Through an independent sample t-test,the length of hospital stay as well as total hospitalization,medicine and consumables,medical,nursing,medical technology,and management expenses were compared.Pearson’s correlation coefficient was used to test the cost correlation.RESULTS In terms of hospitalization expenses,patients in the CHS-DRG payment group had lower medical,nursing,and management expenses than those in the diagnosis-related group(DRG)non-payment group.For patients in the DRG payment group,the factors affecting the total hospitalization cost,in descending order of relevance,were medicine and consumable costs,consumable costs,medicine costs,medical costs,medical technology costs,management costs,nursing costs,and length of hospital stay.For patients in the DRG nonpayment group,the factors affecting the total hospitalization expenses in descending order of relevance were medicines and consumable expenses,consumable expenses,medical technology expenses,the cost of medicines,medical expenses,nursing expenses,length of hospital stay,and management expenses.CONCLUSION The CHS-DRG system can help control and reduce unnecessary medical expenses by controlling medicine costs,medical consumable costs,and the length of hospital stay while ensuring medical safety.

Novel Static Security and Stability Control of Power Systems Based on Artificial Emotional Lazy Q-Learning

The stability problem of power grids has become increasingly serious in recent years as the size of novel power systems increases.In order to improve and ensure the stable operation of the novel power system,this study proposes an artificial emotional lazy Q-learning method,which combines artificial emotion,lazy learning,and reinforcement learning for static security and stability analysis of power systems.Moreover,this study compares the analysis results of the proposed method with those of the small disturbance method for a stand-alone power system and verifies that the proposed lazy Q-learning method is able to effectively screen useful data for learning,and improve the static security stability of the new type of power system more effectively than the traditional proportional-integral-differential control and Q-learning methods.

Using IoT to Smart Security Door System with Face Tracking Camera

Security is a serious concern, whether it may be the security of assets, data and human life. Providing humans with security and safety for their belongings and items is an important need. A smart lock door project/ with different types of methods for entry, like fingerprint and authentication PIN code is suggested with an unnoticeable face tracking camera capturing a photo in case of error data entry. It is to be controlled via the user’s smartphone using Blynk with the implementation of IoT. This technology is made with two microcontrollers. ESP32 is used to control the solenoid lock, fingerprint sensor and keypad. ESP32-CAM is used to capture a photo and send it to the owner’s smartphone to be viewed on Telegram application. Many conclusions are extracted from system results, as well as suggested ideas for future work.

Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

Cloud computing plays a significant role in modern information technology, providing organizations with numerous benefits, including flexibility, scalability, and cost-efficiency. However, it has become essential for organizations to ensure the security of their applications, data, and cloud-based networks to use cloud services effectively. This systematic literature review aims to determine the latest information regarding cloud computing security, with a specific emphasis on threats and mitigation strategies. Additionally, it highlights some common threats related to cloud computing security, such as distributed denial-of-service (DDoS) attacks, account hijacking, malware attacks, and data breaches. This research also explores some mitigation strategies, including security awareness training, vulnerability management, security information and event management (SIEM), identity and access management (IAM), and encryption techniques. It discusses emerging trends in cloud security, such as integrating artificial intelligence (AI) and machine learning (ML), serverless computing, and containerization, as well as the effectiveness of the shared responsibility model and its related challenges. The importance of user awareness and the impact of emerging technologies on cloud security have also been discussed in detail to mitigate security risks. A literature review of previous research and scholarly articles has also been conducted to provide insights regarding cloud computing security. It shows the need for continuous research and innovation to address emerging threats and maintain a security-conscious culture in the company.

The Research on Open Sharing of Higher Education Park Based on Intelligent Security System

The intelligent security system is a series of systems that use modern information technology means such as artificial intelligence, cloud computing, big data, face recognition to carry out comprehensive monitoring, early warning, prevention and control, disposal, etc, for security protection. It is the development trend of security system in the future, and it is also the basis for open sharing between higher education parks and universities. By using content analysis, unstructured interviews and other research methods, this paper deeply studies the feasibility and basic ideas of the construction of intelligent security system in Shahe Higher Education Park, and forms basic experience and typical practices through the project construction, which further promotes the more intelligent, standardized and scientific safety management in colleges and universities. It really provides an important theoretical basis and practical guidance for the opening and sharing between higher education parks and universities.

A Data Driven Security Correction Method for Power Systems with UPFC

The access of unified power flow controllers(UPFC)has changed the structure and operation mode of power grids all across the world,and it has brought severe challenges to the traditional real-time calculation of security correction based on traditionalmodels.Considering the limitation of computational efficiency regarding complex,physical models,a data-driven power system security correction method with UPFC is,in this paper,proposed.Based on the complex mapping relationship between the operation state data and the security correction strategy,a two-stage deep neural network(DNN)learning framework is proposed,which divides the offline training task of security correction into two stages:in the first stage,the stacked auto-encoder(SAE)classification model is established,and the node correction state(0/1)output based on the fault information;in the second stage,the DNN learningmodel is established,and the correction amount of each action node is obtained based on the action nodes output in the previous stage.In this paper,the UPFC demonstration project of NanjingWest Ring Network is taken as a case study to validate the proposed method.The results show that the proposed method can fully meet the real-time security correction time requirements of power grids,and avoid the inherent defects of the traditional model method without an iterative solution and can also provide reasonable security correction strategies for N-1 and N-2 faults.

Repositioning fertilizer manufacturing subsidies for improving food security and reducing greenhouse gas emissions in China

China removed fertilizer manufacturing subsidies from 2015 to 2018 to bolster market-oriented reforms and foster environmentally sustainable practices.However,the impact of this policy reform on food security and the environment remains inadequately evaluated.Moreover,although green and low-carbon technologies offer environmental advantages,their widespread adoption is hindered by prohibitively high costs.This study analyzes the impact of removing fertilizer manufacturing subsidies and explores the potential feasibility of redirecting fertilizer manufacturing subsidies to invest in the diffusion of these technologies.Utilizing the China Agricultural University Agri-food Systems model,we analyzed the potential for achieving mutually beneficial outcomes regarding food security and environmental sustainability.The findings indicate that removing fertilizer manufacturing subsidies has reduced greenhouse gas(GHG)emissions from agricultural activities by 3.88 million metric tons,with minimal impact on food production.Redirecting fertilizer manufacturing subsidies to invest in green and low-carbon technologies,including slow and controlled-release fertilizer,organic-inorganic compound fertilizers,and machine deep placement of fertilizer,emerges as a strategy to concurrently curtail GHG emissions,ensure food security,and secure robust economic returns.Finally,we propose a comprehensive set of government interventions,including subsidies,field guidance,and improved extension systems,to promote the widespread adoption of these technologies.

Research on the Construction of Computer Network Security System in Middle School Campus Network

In order to improve the security of high school campus networks,this paper introduces the goal,system composition,and function of the network security of high school campus networks,and puts forward a series of strategies,including the establishment of network security protection system,data backup and recovery mechanism,and strengthening network security management and training.Through these strategies,the safety and stable operation of the campus network can be ensured,the quality of education can be improved,and school’s development can be promoted.

TRUSED:A Trust-Based Security Evaluation Scheme for A Distributed Control System

Distributed control systems(DCS)have revolutionized the communication process and attracted more interest due to their pervasive computing nature(cyber/physical),their monitoring capabilities and the benefits they offer.However,due to distributed communication,flexible network topologies and lack of central control,the traditional security strategies are inadequate formeeting the unique characteristics ofDCS.Moreover,malicious and untrustworthy nodes pose a significant threat during the formation of a DCS network.Trust-based secure systems not only monitor and track the behavior of the nodes but also enhance the security by identifying and isolating the malicious node,which reduces the risk and increases network lifetime.In this research,we offer TRUSED,a trust-based security evaluation scheme that both,directly and indirectly,estimates each node’s level of trustworthiness,incorporating the cumulative trust concept.In addition,simulation results show that the proposed technique can effectively identify malicious nodes,determine their node’s trustworthiness rating,and improve the packet delivery ratio.