Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

A Blockchain-Based Access Control Scheme for Reputation Value Attributes of the Internet of Things

The Internet of Things(IoT)access controlmechanism may encounter security issues such as single point of failure and data tampering.To address these issues,a blockchain-based IoT reputation value attribute access control scheme is proposed.Firstly,writing the reputation value as an attribute into the access control policy,and then deploying the access control policy in the smart contract of the blockchain system can enable the system to provide more fine-grained access control;Secondly,storing a large amount of resources fromthe Internet of Things in Inter Planetary File System(IPFS)to improve system throughput;Finally,map resource access operations to qualification tokens to improve the performance of the access control system.Complete simulation experiments based on the Hyperledger Fabric platform.Fromthe simulation experimental results,it can be seen that the access control system can achieve more fine-grained and dynamic access control while maintaining high throughput and low time delay,providing sufficient reliability and security for access control of IoT devices.

Blockchain-Enabled Privacy Protection and Access Control Scheme Towards Sensitive Digital Assets Management

With the growth of requirements for data sharing,a novel business model of digital assets trading has emerged that allows data owners to sell their data for monetary gain.In the distributed ledger of blockchain,however,the privacy of stakeholder's identity and the confidentiality of data content are threatened.Therefore,we proposed a blockchainenabled privacy-preserving and access control scheme to address the above problems.First,the multi-channel mechanism is introduced to provide the privacy protection of distributed ledger inside the channel and achieve coarse-grained access control to digital assets.Then,we use multi-authority attribute-based encryption(MAABE)algorithm to build a fine-grained access control model for data trading in a single channel and describe its instantiation in detail.Security analysis shows that the scheme has IND-CPA secure and can provide privacy protection and collusion resistance.Compared with other schemes,our solution has better performance in privacy protection and access control.The evaluation results demonstrate its effectiveness and practicability.

Cross-Domain Bilateral Access Control on Blockchain-Cloud Based Data Trading System

Data trading enables data owners and data requesters to sell and purchase data.With the emergence of blockchain technology,research on blockchain-based data trading systems is receiving a lot of attention.Particularly,to reduce the on-chain storage cost,a novel paradigm of blockchain and cloud fusion has been widely considered as a promising data trading platform.Moreover,the fact that data can be used for commercial purposes will encourage users and organizations from various fields to participate in the data marketplace.In the data marketplace,it is a challenge how to trade the data securely outsourced to the external cloud in a way that restricts access to the data only to authorized users across multiple domains.In this paper,we propose a cross-domain bilateral access control protocol for blockchain-cloud based data trading systems.We consider a system model that consists of domain authorities,data senders,data receivers,a blockchain layer,and a cloud provider.The proposed protocol enables access control and source identification of the outsourced data by leveraging identity-based cryptographic techniques.In the proposed protocol,the outsourced data of the sender is encrypted under the target receiver’s identity,and the cloud provider performs policy-match verification on the authorization tags of the sender and receiver generated by the identity-based signature scheme.Therefore,data trading can be achieved only if the identities of the data sender and receiver simultaneously meet the policies specified by each other.To demonstrate efficiency,we evaluate the performance of the proposed protocol and compare it with existing studies.

Big Data Access Control Mechanism Based on Two-Layer Permission Decision Structure

Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business constraints.The BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

Automatic Generation of Attribute-Based Access Control Policies from Natural Language Documents

In response to the challenges of generating Attribute-Based Access Control(ABAC)policies,this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents.This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model.The manual retrieval and analysis involved in this transition are inefficient,prone to errors,and costly.Most organizations have high-level specifications defined for security policies that include a set of access control policies,which often exist in the form of natural language documents.Utilizing this rich source of information,our method effectively identifies and extracts the necessary attributes and rules for access control from natural language documents,thereby constructing and optimizing access control policies.This work transforms the problem of policy automation generation into two tasks:extraction of access control statements andmining of access control attributes.First,the Chat General Language Model(ChatGLM)isemployed to extract access control-related statements from a wide range of natural language documents by constructing unique prompts and leveraging the model’s In-Context Learning to contextualize the statements.Then,the Iterated Dilated-Convolutions-Conditional Random Field(ID-CNN-CRF)model is used to annotate access control attributes within these extracted statements,including subject attributes,object attributes,and action attributes,thus reassembling new access control policies.Experimental results show that our method,compared to baseline methods,achieved the highest F1 score of 0.961,confirming the model’s effectiveness and accuracy.

Deep Learning Social Network Access Control Model Based on User Preferences

A deep learning access controlmodel based on user preferences is proposed to address the issue of personal privacy leakage in social networks.Firstly,socialusers andsocialdata entities are extractedfromthe social networkandused to construct homogeneous and heterogeneous graphs.Secondly,a graph neural networkmodel is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network.Then,high-order neighbor nodes,hidden neighbor nodes,displayed neighbor nodes,and social data nodes are used to update user nodes to expand the depth and breadth of user preferences.Finally,a multi-layer attention network is used to classify user nodes in the homogeneous graph into two classes:allow access and deny access.The fine-grained access control problem in social networks is transformed into a node classification problem in a graph neural network.The model is validated using a dataset and compared with other methods without losing generality.The model improved accuracy by 2.18%compared to the baseline method GraphSAGE,and improved F1 score by 1.45%compared to the baseline method,verifying the effectiveness of the model.

A Dual-Cluster-Head Based Medium Access Control for Large-Scale UAV Ad-Hoc Networks

Unmanned Aerial Vehicle(UAV)ad hoc network has achieved significant growth for its flexibility,extensibility,and high deployability in recent years.The application of clustering scheme for UAV ad hoc network is imperative to enhance the performance of throughput and energy efficiency.In conventional clustering scheme,a single cluster head(CH)is always assigned in each cluster.However,this method has some weaknesses such as overload and premature death of CH when the number of UAVs increased.In order to solve this problem,we propose a dual-cluster-head based medium access control(DCHMAC)scheme for large-scale UAV networks.In DCHMAC,two CHs are elected to manage resource allocation and data forwarding cooperatively.Specifically,two CHs work on different channels.One of CH is used for intra-cluster communication and the other one is for inter-cluster communication.A Markov chain model is developed to analyse the throughput of the network.Simulation result shows that compared with FM-MAC(flying ad hoc networks multi-channel MAC,FM-MAC),DCHMAC improves the throughput by approximately 20%~50%and prolongs the network lifetime by approximately 40%.

Guaranteed Cost Attitude Tracking Control for Uncertain Quadrotor Unmanned Aerial Vehicle Under Safety Constraints

In this paper,guaranteed cost attitude tracking con-trol for uncertain quadrotor unmanned aerial vehicle(QUAV)under safety constraints is studied.First,an augmented system is constructed by the tracking error system and reference system.This transformation aims to convert the tracking control prob-lem into a stabilization control problem.Then,control barrier function and disturbance attenuation function are designed to characterize the violations of safety constraints and tolerance of uncertain disturbances,and they are incorporated into the reward function as penalty items.Based on the modified reward function,the problem is simplified as the optimal regulation problem of the nominal augmented system,and a new Hamilton-Jacobi-Bellman equation is developed.Finally,critic-only rein-forcement learning algorithm with a concurrent learning tech-nique is employed to solve the Hamilton-Jacobi-Bellman equa-tion and obtain the optimal controller.The proposed algorithm can not only ensure the reward function within an upper bound in the presence of uncertain disturbances,but also enforce safety constraints.The performance of the algorithm is evaluated by the numerical simulation.

Blockchain-Empowered Token-Based Access Control System with User Reputation Evaluation

Currently,data security and privacy protection are becoming more and more important.Access control is a method of authorization for users through predefined policies.Token-based access control(TBAC)enhances the manageability of authorization through the token.However,traditional access control policies lack the ability to dynamically adjust based on user access behavior.Incorporating user reputation evaluation into access control can provide valuable feedback to enhance system security and flexibility.As a result,this paper proposes a blockchain-empowered TBAC system and introduces a user reputation evaluation module to provide feedback on access control.The TBAC system divides the access control process into three stages:policy upload,token request,and resource request.The user reputation evaluation module evaluates the user’s token reputation and resource reputation for the token request and resource request stages of the TBAC system.The proposed system is implemented using the Hyperledger Fabric blockchain.The TBAC system is evaluated to prove that it has high processing performance.The user reputation evaluation model is proved to be more conservative and sensitive by comparative study with other methods.In addition,the security analysis shows that the TBAC system has a certain anti-attack ability and can maintain stable operation under the Distributed Denial of Service(DDoS)attack environment.

EduASAC:A Blockchain-Based Education Archive Sharing and Access Control System

In the education archive sharing system,when performing homomorphic ciphertext retrieval on the storage server,there are problems such as low security of shared data,confusing parameter management,and weak access control.This paper proposes an Education Archives Sharing and Access Control(EduASAC)system to solve these problems.The system research goal is to realize the sharing of security parameters,the execution of access control,and the recording of system behaviors based on the blockchain network,ensuring the legitimacy of shared membership and the security of education archives.At the same time,the system can be combined with most homomorphic ciphertext retrieval schemes running on the storage server,making the homomorphic ciphertext retrieval mechanism controllable.This paper focuses on the blockchain access control framework and specifically designs smart contracts that conform to the business logic of the EduASAC system.The former adopts a dual-mode access control mechanism combining Discretionary Access Control(DAC)and Mandatory Access Control(MAC)and improves the tagging mode after user permission verification based on the Authentication and Authorization for Constrained Environments(ACE)authorization framework of Open Authorization(OAuth)2.0;the latter is used in the system to vote on nodes to join requests,define access control policies,execute permission verification processes,store,and share system parameters,and standardize the behavior of member nodes.Finally,the EduASAC system realizes the encryption,storage,retrieval,sharing,and access control processes of education archives.To verify the performance of the system,simulation experiments were conducted.The results show that the EduASAC system can meet the high security needs of education archive sharing and ensure the system’s high throughput,low latency,fast decision-making,and fine-grained access control ability.

Ether-IoT:A Realtime Lightweight and Scalable Blockchain-Enabled Cache Algorithm for IoT Access Control

Several unique characteristics of Internet of Things(IoT)devices,such as distributed deployment and limited storage,make it challenging for standard centralized access control systems to enable access control in today’s large-scale IoT ecosystem.To solve these challenges,this study presents an IoT access control system called Ether-IoT based on the Ethereum Blockchain(BC)infrastructure with Attribute-Based Access Control(ABAC).Access Contract(AC),Cache Contract(CC),Device Contract(DC),and Policy Contract(PC)are the four central smart contracts(SCs)that are included in the proposed system.CC offers a way to save user characteristics in a local cache system to avoid delays during transactions between BC and IoT devices.AC is the fundamental program users typically need to run to build an access control technique.DC offers a means for storing the resource data created by devices and a method for querying that data.PC offers administrative settings to handle ABAC policies on users’behalf.Ether-IoT,combined with ABAC and the BC,enables IoT access control management that is decentralized,fine-grained and dynamically scalable.This research gives a real-world case study to illustrate the suggested framework’s implementation.In the end,a simulation experiment is performed to evaluate the system’s performance.To ensure data integrity in dispersed systems,the results show that Ether-IoT can sustain high throughput in contexts with a large number of requests.

Analysis of Building Construction Quality Control and Safety Management

Quality control and safety management are two very important contents in building construction project management.Once the construction quality is not up to standard or a safety accident occurs,the economic benefits of the project will be severely impacted.However,there are still some problems in the quality and safety management of the project.In this paper the problems in construction quality control and safety management are analyzed,and effective countermeasures are put forward,in hopes to help improve construction quality and safety.

Development, Delivery and Evaluation of a Safety Training Program on Demolition for Blight Reduction

The main objective of the work presented in this paper was to develop a customized safety training program that can be incorporated into the demolition projects undertaken as part of blight reduction efforts in urban centers. A subsidiary objective was to devise and implement a safety program evaluation methodology, and gain insights on the relationships between knowledge acquisition through training and trainee demographics. Salient aspects of blight elimination efforts, as well as the main facets of building demolition practices and requirements, were reviewed. Information on various related safety and health hazards was studied in depth with a focus on demolition operations dealing with blighted properties. A unique safety hazard awareness training program was created for demolition workers, contractors and inspectors based on this research. In addition to devising a curriculum of relevant training topics along with traditional and online delivery systems to be employed, effectiveness evaluation instruments were formulated. Based on the limited data collected from the trainees it was concluded that the program was well-received by them and provided effective learning. It was also found that no statistically significant associations existed between the knowledge gain of the trainees, and either their experience level or union status, after taking this training.

On Dual-Mode Driving Control Method for a Novel Unmanned Tractor With High Safety and Reliability

Due to the non-standardization and complexity of the farmland environment,it is always a huge challenge for tractors to achieve fully autonomy(work at Self-driving mode)all the time in agricultural industry.Whereas,when tractors work in the Tele-driving(or Remote driving)mode,the operators are prone to fatigue because they need to concentrate for long periods of time.In response to these,a dual-mode control strategy was proposed to integrate the advantages of both approaches,i.e.,by combing Self-driving at most of the time with Tele-driving under special(complex and hazardous)conditions through switching control method.First,the state switcher was proposed,which is used for smooth switching the driving modes according to different working states of a tractor.Then,the state switching control law and the corresponding subsystem tracking controllers were designed.Finally,the effectiveness and superiority of the dualmode control method were evaluated via actual experimental testing of a tractor whose results show that the proposed control method can switch smoothly,stably,and efficiently between the two driving modes automatically.The average control accuracy has been improved by 20%and 15%respectively,compared to the conventional Tele-driving control and Self-driving control with low-precision navigation.In conclusion,the proposed dualmode control method can not only satisfy the operation in the complex and changeable farmland environment,but also free drivers from high-intensity and fatiguing work.This provides a perfect application solution and theoretical support for the intelligentization of unmanned farm agricultural machinery with high safety and reliability.

Attribute-based access control policy specification language

This paper first introduces attribute expression to describe attribute-based access control policy.Secondly,an access control policy enforcement language named A-XACML （attribute-XACML）is proposed,which is an extension of XACML.A-XACML is used as a simple,flexible way to express and enforce access control policies,especially attribute-based access control policy,in a variety of environments.The language and schema support include data types,functions,and combining logic which allow simple and complex policies to be defined.Finally,a system architecture and application case of user-role assignment is given to show how attribute expressions and A-XACML work in access control policy description and enforcement.The case shows that attribute expression and A-XACML can describe and enforce the complex access control policy in a simple and flexible way.

A New Role Hierarchy Model for Role Based Access Control

A new role hierarchy model for RBAC (role-based access control) is presented and its features are illustrated through examples. Some new concepts such as private permission, public permission and special permission are introduced, based on the RRA97 model. Some new role-role inheriting forms such as normal inheritance, private inheritance, public inheritance and special-without inheritance are defined. Based on the ideas mentioned, the new role hierarchy model is formulated. It is easier and more comprehensible to describe role-role relationships through the new model than through the traditional ones. The new model is closer to the real world and its mechanism is more powerful. Particularly it is more suitable when used in large-scale role hierarchies.

Role based access control design using Triadic concept analysis

Role based access control is one of the widely used access control models.There are investigations in the literature that use knowledge representation mechanisms such as formal concept analysis(FCA),description logics,and Ontology for representing access control mechanism.However,while using FCA,investigations reported in the literature so far work on the logic that transforms the three dimensional access control matrix into dyadic formal contexts.This transformation is mainly to derive the formal concepts,lattice structure and implications to represent role hierarchy and constraints of RBAC.In this work,we propose a methodology that models RBAC using triadic FCA without transforming the triadic access control matrix into dyadic formal contexts.Our discussion is on two lines of inquiry.We present how triadic FCA can provide a suitable representation of RBAC policy and we demonstrate how this representation follows role hierarchy and constraints of RBAC on sample healthcare network available in the literature.

Perti Net-Based Workflow Access Control Model

Access control is an important protection mechanism for information systems. This paper shows how to make access control in workflow system. We give a workflow access control model (WACM) based on several current access control models. The model supports roles assignment and dynamic authorization. The paper defines the workflow using Petri net. It firstly gives the definition and description of the workflow, and then analyzes the architecture of the workflow access control model (WACM). Finally, an example of an e-commerce workflow access control model is discussed in detail.

Task-Attribute-Based Access Control Scheme for IoT via Blockchain

As a new form of network,the Internet of things(IoT)is becoming more widely used in people’s lives.In this paper,related theoretical research and practical applications of the IoT are explored.The security of the IoT has become a hot research topic.Access controls are methods that control reasonable allocations of data and resources and ensure the security of the IoT.However,most access control systems do not dynamically assign users’rights.Additionally,with some access control systems,there is a risk of overstepping other user’s authority,and there may exist a central authority that is a single point of failure.Therefore,to solve these problems,this paper proposes a Task-Attribute-Based Access Control scheme for the IoT via blockchain that combines the access control technologies of both the IoT and blockchain.This model,which merges the advantages of task-based access controls and attribute-based access controls,is perfectly integrated with blockchain technology.This model uses hash functions and digital signature algorithms to ensure the authenticity and integrity of the data,and it can dynamically allocate users’minimum privileges and thus perfectly solves the single point of failure problem.The model is implemented using a Geth client and solidity code,and the simulation results demonstrate the effectiveness of the model.