Visible to the public Biblio

Found 7526 results

Filters: Keyword is pubcrawl  [Clear All Filters]
2020-07-09
Nisha, D, Sivaraman, E, Honnavalli, Prasad B.  2019.  Predicting and Preventing Malware in Machine Learning Model. 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—7.

Machine learning is a major area in artificial intelligence, which enables computer to learn itself explicitly without programming. As machine learning is widely used in making decision automatically, attackers have strong intention to manipulate the prediction generated my machine learning model. In this paper we study about the different types of attacks and its countermeasures on machine learning model. By research we found that there are many security threats in various algorithms such as K-nearest-neighbors (KNN) classifier, random forest, AdaBoost, support vector machine (SVM), decision tree, we revisit existing security threads and check what are the possible countermeasures during the training and prediction phase of machine learning model. In machine learning model there are 2 types of attacks that is causative attack which occurs during the training phase and exploratory attack which occurs during the prediction phase, we will also discuss about the countermeasures on machine learning model, the countermeasures are data sanitization, algorithm robustness enhancement, and privacy preserving techniques.

Ashouri, Mohammadreza.  2019.  Detecting Input Sanitization Errors in Scala. 2019 Seventh International Symposium on Computing and Networking Workshops (CANDARW). :313—319.

Scala programming language combines object-oriented and functional programming in one concise, high-level language, and the language supports static types that help to avoid bugs in complex programs. This paper proposes a dynamic taint analyzer called ScalaTaint for Scala applications. The analyzer traces the propagation of malicious inputs from untrusted sources to sensitive sink methods in programs that can be exploited by adversaries. In this work, we evaluated the accuracy of ScalaTaint with a security benchmark suite including 7 projects in Scala. As a result, our analyzer could report 49 vulnerabilities within 753,372 lines of code. Moreover, the result of our performance measurement on ScalaBench shows 67% runtime overhead that demonstrates the usefulness and efficiently of our technique in comparison with similar tools.

Kassem, Ali, Ács, Gergely, Castelluccia, Claude, Palamidessi, Catuscia.  2019.  Differential Inference Testing: A Practical Approach to Evaluate Sanitizations of Datasets. 2019 IEEE Security and Privacy Workshops (SPW). :72—79.

In order to protect individuals' privacy, data have to be "well-sanitized" before sharing them, i.e. one has to remove any personal information before sharing data. However, it is not always clear when data shall be deemed well-sanitized. In this paper, we argue that the evaluation of sanitized data should be based on whether the data allows the inference of sensitive information that is specific to an individual, instead of being centered around the concept of re-identification. We propose a framework to evaluate the effectiveness of different sanitization techniques on a given dataset by measuring how much an individual's record from the sanitized dataset influences the inference of his/her own sensitive attribute. Our intent is not to accurately predict any sensitive attribute but rather to measure the impact of a single record on the inference of sensitive information. We demonstrate our approach by sanitizing two real datasets in different privacy models and evaluate/compare each sanitized dataset in our framework.

Fahrenkrog-Petersen, Stephan A., van der Aa, Han, Weidlich, Matthias.  2019.  PRETSA: Event Log Sanitization for Privacy-aware Process Discovery. 2019 International Conference on Process Mining (ICPM). :1—8.

Event logs that originate from information systems enable comprehensive analysis of business processes, e.g., by process model discovery. However, logs potentially contain sensitive information about individual employees involved in process execution that are only partially hidden by an obfuscation of the event data. In this paper, we therefore address the risk of privacy-disclosure attacks on event logs with pseudonymized employee information. To this end, we introduce PRETSA, a novel algorithm for event log sanitization that provides privacy guarantees in terms of k-anonymity and t-closeness. It thereby avoids disclosure of employee identities, their membership in the event log, and their characterization based on sensitive attributes, such as performance information. Through step-wise transformations of a prefix-tree representation of an event log, we maintain its high utility for discovery of a performance-annotated process model. Experiments with real-world data demonstrate that sanitization with PRETSA yields event logs of higher utility compared to methods that exploit frequency-based filtering, while providing the same privacy guarantees.

Liu, Chuanyi, Han, Peiyi, Dong, Yingfei, Pan, Hezhong, Duan, Shaoming, Fang, Binxing.  2019.  CloudDLP: Transparent and Automatic Data Sanitization for Browser-Based Cloud Storage. 2019 28th International Conference on Computer Communication and Networks (ICCCN). :1—8.

Because cloud storage services have been broadly used in enterprises for online sharing and collaboration, sensitive information in images or documents may be easily leaked outside the trust enterprise on-premises due to such cloud services. Existing solutions to this problem have not fully explored the tradeoffs among application performance, service scalability, and user data privacy. Therefore, we propose CloudDLP, a generic approach for enterprises to automatically sanitize sensitive data in images and documents in browser-based cloud storage. To the best of our knowledge, CloudDLP is the first system that automatically and transparently detects and sanitizes both sensitive images and textual documents without compromising user experience or application functionality on browser-based cloud storage. To prevent sensitive information escaping from on-premises, CloudDLP utilizes deep learning methods to detect sensitive information in both images and textual documents. We have evaluated the proposed method on a number of typical cloud applications. Our experimental results show that it can achieve transparent and automatic data sanitization on the cloud storage services with relatively low overheads, while preserving most application functionalities.

Wang, Wei-Chen, Lin, Ping-Hsien, Li, Yung-Chun, Ho, Chien-Chung, Chang, Yu-Ming, Chang, Yuan-Hao.  2019.  Toward Instantaneous Sanitization through Disturbance-induced Errors and Recycling Programming over 3D Flash Memory. 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). :1—8.

As data security has become one of the most crucial issues in modern storage system/application designs, the data sanitization techniques are regarded as the promising solution on 3D NAND flash-memory-based devices. Many excellent works had been proposed to exploit the in-place reprogramming, erasure and encryption techniques to achieve and implement the sanitization functionalities. However, existing sanitization approaches could lead to performance, disturbance overheads or even deciphered issues. Different from existing works, this work aims at exploring an instantaneous data sanitization scheme by taking advantage of programming disturbance properties. Our proposed design can not only achieve the instantaneous data sanitization by exploiting programming disturbance and error correction code properly, but also enhance the performance with the recycling programming design. The feasibility and capability of our proposed design are evaluated by a series of experiments on 3D NAND flash memory chips, for which we have very encouraging results. The experiment results show that the proposed design could achieve the instantaneous data sanitization with low overhead; besides, it improves the average response time and reduces the number of block erase count by up to 86.8% and 88.8%, respectively.

2020-07-06
Hasan, Kamrul, Shetty, Sachin, Hassanzadeh, Amin, Ullah, Sharif.  2019.  Towards Optimal Cyber Defense Remediation in Cyber Physical Systems by Balancing Operational Resilience and Strategic Risk. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :1–8.
A prioritized cyber defense remediation plan is critical for effective risk management in cyber-physical systems (CPS). The increased integration of Information Technology (IT)/Operational Technology (OT) in CPS has to lead to the need to identify the critical assets which, when affected, will impact resilience and safety. In this work, we propose a methodology for prioritized cyber risk remediation plan that balances operational resilience and economic loss (safety impacts) in CPS. We present a platform for modeling and analysis of the effect of cyber threats and random system faults on the safety of CPS that could lead to catastrophic damages. We propose to develop a data-driven attack graph and fault graph-based model to characterize the exploitability and impact of threats in CPS. We develop an operational impact assessment to quantify the damages. Finally, we propose the development of a strategic response decision capability that proposes optimal mitigation actions and policies that balances the trade-off between operational resilience (Tactical Risk) and Strategic Risk.
Mason, Andrew, Zhao, Yifan, He, Hongmei, Gompelman, Raymon, Mandava, Srikanth.  2019.  Online Anomaly Detection of Time Series at Scale. 2019 International Conference on Cyber Situational Awareness, Data Analytics And Assessment (Cyber SA). :1–8.
Cyber breaches can result in disruption to business operations, reputation damage as well as directly affecting the financial stability of the targeted corporations, with potential impacts on future profits and stock values. Automatic network-stream monitoring becomes necessary for cyber situation awareness, and time-series anomaly detection plays an important role in network stream monitoring. This study surveyed recent research on time-series analysis methods in respect of parametric and non-parametric techniques, and popular machine learning platforms for data analysis on streaming data on both single server and cloud computing environments. We believe it provides a good reference for researchers in both academia and industry to select suitable (time series) data analysis techniques, and computing platforms, dependent on the data scale and real-time requirements.
Brezhniev, Yevhen.  2019.  Multilevel Fuzzy Logic-Based Approach for Critical Energy Infrastructure’s Cyber Resilience Assessment. 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT). :213–217.
This paper presents approach for critical energy infrastructure's (CEI) cyber resilience assessment. The CEI is the vital physical system of systems, whose accidents and failures lead to damage of economy, environment, impact on health and lives of people. The analysis of cyber incidents with Ukrainian CEI confirms the importance of the task of increasing its cyber resilience to external hostile influences and keeping of the appropriate level of functionality, safety and reliability. This paper is devoted to development of approach for CEI's cyber resilience assessment considering the important capacities of its systems (adaptivity, restoration, absorbability, preventive) and interdependencies between them. This approach is based on application of multilevel fuzzy logic models (called as logic-linguistic models, LLM) taking into consideration the data available from expert's knowledge. The comparison between risk management and resilience assurance is performed. The new risk-oriented definition of resiliency is suggested.
Sheela, A., Revathi, S., Iqbal, Atif.  2019.  Cyber Risks Assessment For Intelligent And Non-Intelligent Attacks In Power System. 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC). :40–45.
Smart power grid is a perfect model of Cyber Physical System (CPS) which is an important component for a comfortable life. The major concern of the electrical network is safety and reliable operation. A cyber attacker in the operation of power system would create a major damage to the entire power system structure and affect the continuity of the power supply by adversely changing its parameters. A risk assessment method is presented for evaluating the cyber security assessment of power systems taking into consideration the need for protection systems. The paper considers the impact of bus and transmission line protection systems located in substations on the cyber physical performance of power systems. The proposed method is to simulate the response of power systems to sudden attacks on various power system preset value and parameters. This paper focuses on the cyber attacks which occur in a co-ordinated way so that many power system components will be in risk. The risk can be modelled as the combined probability of power system impact due to attacks and of successful interruption into the system. Stochastic Petri Nets is employed for assessing the risks. The effectiveness of the proposed cyber security risk assessment method is simulated for a IEEE39 bus system.
Xiong, Leilei, Grijalva, Santiago.  2019.  N-1 RTU Cyber-Physical Security Assessment Using State Estimation. 2019 IEEE Power Energy Society General Meeting (PESGM). :1–5.
Real-time supervisory control and data acquisition (SCADA) systems use remote terminal units (RTUs) to monitor and manage the flow of power at electrical substations. As their connectivity to different utility and private networks increases, RTUs are becoming more vulnerable to cyber-attacks. Some attacks seek to access RTUs to directly control power system devices with the intent to shed load or cause equipment damage. Other attacks (such as denial-of-service) target network availability and seek to block, delay, or corrupt communications between the RTU and the control center. In the most severe case, when communications are entirely blocked, the loss of an RTU can cause the power system to become unobservable. It is important to understand how losing an RTU impacts the system state (bus voltage magnitudes and angles). The system state is determined by the state estimator and serves as the input to other critical EMS applications. There is currently no systematic approach for assessing the cyber-physical impact of losing RTUs. This paper proposes a methodology for N-1 RTU cyber-physical security assessment that could benefit power system control and operation. We demonstrate our approach on the IEEE 14-bus system as well as on a synthetic 200-bus system.
Tripathi, Dipty, Maurya, Ashish Kumar, Chaturvedi, Amrita, Tripathi, Anil Kumar.  2019.  A Study of Security Modeling Techniques for Smart Systems. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). :87–92.
The term “smart” has been used in many ways for describing systems and infrastructure such as smart city, smart home, smart grid, smart meter, etc. These systems may lie in the domain of critical security systems where security can be estimated in terms of confidentiality, integrity and some cases may involve availability for protection against the theft or damage of system resources as well as disruption of the system services. Although, in spite of, being a hot topic to enhance the quality of life, there is no concrete definition of what smart system is and what should be the characteristics of it. Thus, there is a need to identify what these systems actually are and how they can be designed securely. This work firstly attempts to describe attributes related to the smartness to define smart systems. Furthermore, we propose a secure smart system development life cycle, where the security is weaved at all the development phase of smart systems according to principles, guidelines, attack patterns, risk, vulnerability, exploits, and defined rules. Finally, the comparative study is performed for evaluation of traditional security modeling techniques for early assessment of threats and risks in smart systems.
Mikhalevich, I. F., Trapeznikov, V. A..  2019.  Critical Infrastructure Security: Alignment of Views. 2019 Systems of Signals Generating and Processing in the Field of on Board Communications. :1–5.
Critical infrastructures of all countries unites common cyberspace. In this space, there are many threats that can disrupt the security of critical infrastructure in one country, but also cause damage in other countries. This is a reality that makes it necessary to agree on intergovernmental national views on the composition of critical infrastructures, an assessment of their security and protection. The article presents an overview of views on critical infrastructures of the United States, the European Union, the United Kingdom, and the Russian Federation, the purpose of which is to develop common positions.
Mao, Zhong, Yan, Yujie, Wu, Jiahao, Hajjar, Jerome F., Padir, Taskin.  2019.  Automated Damage Assessment of Critical Infrastructure Using Online Mapping Technique with Small Unmanned Aircraft Systems. 2019 IEEE International Symposium on Technologies for Homeland Security (HST). :1–5.
Rapid inspection and assessment of critical infrastructure after man-made and natural disasters is a matter of homeland security. The primary aim of this paper is to demonstrate the potential of leveraging small Unmanned Aircraft System (sUAS) in support of the rapid recovery of critical infrastructure in the aftermath of catastrophic events. We propose our data collection, detection and assessment system, using a sUAS equipped with a Lidar and a camera. This method provides a solution in fast post-disaster response and assists human responders in damage investigation.
Paliath, Vivin, Shakarian, Paulo.  2019.  Reasoning about Sequential Cyberattacks. 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). :855–862.
Cyber adversaries employ a variety of malware and exploits to attack computer systems, usually via sequential or “chained” attacks, that take advantage of vulnerability dependencies. In this paper, we introduce a formalism to model such attacks. We show that the determination of the set of capabilities gained by an attacker, which also translates to extent to which the system is compromised, corresponds with the convergence of a simple fixed-point operator. We then address the problem of determining the optimal/most-dangerous strategy for a cyber-adversary with respect to this model and find it to be an NP-Complete problem. To address this complexity we utilize an A*-based approach with an admissible heuristic, that incorporates the result of the fixed-point operator and uses memoization for greater efficiency. We provide an implementation and show through a suite of experiments, using both simulated and actual vulnerability data, that this method performs well in practice for identifying adversarial courses of action in this domain. On average, we found that our techniques decrease runtime by 82%.
Cerotti, D., Codetta-Raiteri, D., Egidi, L., Franceschinis, G., Portinale, L., Dondossola, G., Terruggia, R..  2019.  Analysis and Detection of Cyber Attack Processes targeting Smart Grids. 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). :1–5.
This paper proposes an approach based on Bayesian Networks to support cyber security analysts in improving the cyber-security posture of the smart grid. We build a system model that exploits real world context information from both Information and Operational Technology environments in the smart grid, and we use it to demonstrate sample predictive and diagnostic analyses. The innovative contribution of this work is in the methodology capability of capturing the many dependencies involved in the assessment of security threats, and of supporting the security analysts in planning defense and detection mechanisms for energy digital infrastructures.
Frias, Alex Davila, Yodo, Nita, Yadav, Om Prakash.  2019.  Mixed-Degradation Profiles Assessment of Critical Components in Cyber-Physical Systems. 2019 Annual Reliability and Maintainability Symposium (RAMS). :1–6.
This paper presents a general model to assess the mixed-degradation profiles of critical components in a Cyber-Physical System (CPS) based on the reliability of its critical physical and software components. In the proposed assessment, the cyber aspect of a CPS was approached from a software reliability perspective. Although extensive research has been done on physical components degradation and software reliability separately, research for the combined physical-software systems is still scarce. The non-homogeneous Poisson Processes (NHPP) software reliability models are deemed to fit well with the real data and have descriptive and predictive abilities, which could make them appropriate to estimate software components reliability. To show the feasibility of the proposed approach, a case study for mixed-degradation profiles assessment is presented with n physical components and one major software component forming a critical subsystem in CPS. Two physical components were assumed to have different degradation paths with the dependency between them. Series and parallel structures were investigated for physical components. The software component failure data was taken from a wireless network switching center and fitted into a Weibull software reliability model. The case study results revealed that mix-degradation profiles of physical components, combined with software component profile, produced a different CPS reliability profile.
Xu, Zhiheng, Ng, Daniel Jun Xian, Easwaran, Arvind.  2019.  Automatic Generation of Hierarchical Contracts for Resilience in Cyber-Physical Systems. 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). :1–11.
With the growing scale of Cyber-Physical Systems (CPSs), it is challenging to maintain their stability under all operating conditions. How to reduce the downtime and locate the failures becomes a core issue in system design. In this paper, we employ a hierarchical contract-based resilience framework to guarantee the stability of CPS. In this framework, we use Assume Guarantee (A-G) contracts to monitor the non-functional properties of individual components (e.g., power and latency), and hierarchically compose such contracts to deduce information about faults at the system level. The hierarchical contracts enable rapid fault detection in large-scale CPS. However, due to the vast number of components in CPS, manually designing numerous contracts and the hierarchy becomes challenging. To address this issue, we propose a technique to automatically decompose a root contract into multiple lower-level contracts depending on I/O dependencies between components. We then formulate a multi-objective optimization problem to search the optimal parameters of each lower-level contract. This enables automatic contract refinement taking into consideration the communication overhead between components. Finally, we use a case study from the manufacturing domain to experimentally demonstrate the benefits of the proposed framework.
Lakhno, Valeriy, Kasatkin, Dmytro, Blozva, Andriy.  2019.  Modeling Cyber Security of Information Systems Smart City Based on the Theory of Games and Markov Processes. 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S T). :497–501.
The article considers some aspects of modeling information security circuits for information and communication systems used in Smart City. As a basic research paradigm, the postulates of game theory and mathematical dependencies based on Markov processes were used. Thus, it is possible to sufficiently substantively describe the procedure for selecting rational variants of cyber security systems used to protect information technologies in Smart City. At the same time, using the model proposed by us, we can calculate the probability of cyber threats for the Smart City systems, as well as the cybernetic risks of diverse threats. Further, on the basis of the described indicators, rational contour options are chosen to protect the information systems used in Smart City.
Chai, Yadeng, Liu, Yong.  2019.  Natural Spoken Instructions Understanding for Robot with Dependency Parsing. 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). :866–871.
This paper presents a method based on syntactic information, which can be used for intent determination and slot filling tasks in a spoken language understanding system including the spoken instructions understanding module for robot. Some studies in recent years attempt to solve the problem of spoken language understanding via syntactic information. This research is a further extension of these approaches which is based on dependency parsing. In this model, the input for neural network are vectors generated by a dependency parsing tree, which we called window vector. This vector contains dependency features that improves performance of the syntactic-based model. The model has been evaluated on the benchmark ATIS task, and the results show that it outperforms many other syntactic-based approaches, especially in terms of slot filling, it has a performance level on par with some state of the art deep learning algorithms in recent years. Also, the model has been evaluated on FBM3, a dataset of the RoCKIn@Home competition. The overall rate of correctly understanding the instructions for robot is quite good but still not acceptable in practical use, which is caused by the small scale of FBM3.
Ben, Yongming, Han, Yanni, Cai, Ning, An, Wei, Xu, Zhen.  2019.  An Online System Dependency Graph Anomaly Detection based on Extended Weisfeiler-Lehman Kernel. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :1–6.
Modern operating systems are typical multitasking systems: Running multiple tasks at the same time. Therefore, a large number of system calls belonging to different processes are invoked at the same time. By associating these invocations, one can construct the system dependency graph. In rapidly evolving system dependency graphs, how to quickly find outliers is an urgent issue for intrusion detection. Clustering analysis based on graph similarity will help solve this problem. In this paper, an extended Weisfeiler-Lehman(WL) kernel is proposed. Firstly, an embedded vector with indefinite dimensions is constructed based on the original dependency graph. Then, the vector is compressed with Simhash to generate a fingerprint. Finally, anomaly detection based on clustering is carried out according to these fingerprints. Our scheme can achieve prominent detection with high efficiency. For validation, we choose StreamSpot, a relevant prior work, to act as benchmark, and use the same data set as it to carry out evaluations. Experiments show that our scheme can achieve the highest detection precision of 98% while maintaining a perfect recall performance. Moreover, both quantitative and visual comparisons demonstrate the outperforming clustering effect of our scheme than StreamSpot.
Evgeny, Pavlenko, Dmitry, Zegzhda, Anna, Shtyrkina.  2019.  Estimating the sustainability of cyber-physical systems based on spectral graph theory. 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). :1–5.
Paper proposed an approach to estimating the sustainability of cyber-physical systems based on system state analysis. Authors suggested that sustainability is the system ability to reconfigure for recovering from attacking influences. Proposed a new criterion for cyber-physical systems sustainability assessment based on spectral graph theory. Numerical calculation of the criterion is based on distribution properties of the graph spectrum - the set of eigenvalues of the adjacency matrix corresponding to the graph. Experimental results have shown dependency of change in Δσ, difference between initial value of σstart and final σstop, on working route length, and on graph connectivity was revealed. This parameter is proposed to use as a criterion for CPS sustainability.
Castillo, Anya, Arguello, Bryan, Cruz, Gerardo, Swiler, Laura.  2019.  Cyber-Physical Emulation and Optimization of Worst-Case Cyber Attacks on the Power Grid. 2019 Resilience Week (RWS). 1:14–18.
In this paper we report preliminary results from the novel coupling of cyber-physical emulation and interdiction optimization to better understand the impact of a CrashOverride malware attack on a notional electric system. We conduct cyber experiments where CrashOverride issues commands to remote terminal units (RTUs) that are controlling substations within a power control area. We identify worst-case loss of load outcomes with cyber interdiction optimization; the proposed approach is a bilevel formulation that incorporates RTU mappings to controllable loads, transmission lines, and generators in the upper-level (attacker model), and a DC optimal power flow (DCOPF) in the lower-level (defender model). Overall, our preliminary results indicate that the interdiction optimization can guide the design of experiments instead of performing a “full factorial” approach. Likewise, for systems where there are important dependencies between SCADA/ICS controls and power grid operations, the cyber-physical emulations should drive improved parameterization and surrogate models that are applied in scalable optimization techniques.
Gries, Stefan, Ollesch, Julius, Gruhn, Volker.  2019.  Modeling Semantic Dependencies to Allow Flow Monitoring in Networks with Black-Box Nodes. 2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS). :14–17.
Cyber-Physical Systems are distributed, heterogeneous systems that communicate and exchange data over networks. This creates semantic dependencies between the individual components. In the event of an error, it is difficult to identify the source of an occurring error that is spread due to those underlying dependencies. Tools such as the Information Flow Monitor solve this problem, but require compliance with a protocol. Nodes that do not adhere to this protocol prevent errors from being tracked. In this paper, we present a way to bridge these black-box nodes with a dependency model and to still be able to use them in monitoring tools.
Chegenizadeh, Mostafa, Ali, Mohammad, Mohajeri, Javad, Aref, Mohammad Reza.  2019.  An Anonymous Attribute-based Access Control System Supporting Access Structure Update. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :85–91.
It is quite common nowadays for clients to outsource their personal data to a cloud service provider. However, it causes some new challenges in the area of data confidentiality and access control. Attribute-based encryption is a promising solution for providing confidentiality and fine-grained access control in a cloud-based cryptographic system. Moreover, in some cases, to preserve the privacy of clients and data, applying hidden access structures is required. Also, a data owner should be able to update his defined access structure at any time when he is online or not. As in several real-world application scenarios like e-health systems, the anonymity of recipients, and the possibility of updating access structures are two necessary requirements. In this paper, for the first time, we propose an attribute-based access control scheme with hidden access structures enabling the cloud to update access structures on expiry dates defined by a data owner.