Muhammad Shehzad Aslam (19065891), Ayesha Altaf (19065894), Faiza Iqbal (19065897) et al. · Figshare · 2024
<div><p>The IoT (Internet of Things) has played a promising role in e-healthcare applications during the last decade. Medical sensors record a variety of data and transmit them over the IoT network to facilitate remote patient monitoring. When a patient visits a hospital he may need to connect or disconnect medical devices from the medical healthcare system frequently. Also, multiple entities (e.g., doctors, medical staff, etc.) need access to patient data and require distinct sets of patient data. As a result of the dynamic nature of medical devices, medical users require frequent access to data, which raises complex security concerns. Granting access to a whole set of data creates privacy issues. Also, each of these medical user need to grant access rights to a specific set of medical data, which is quite a tedious task. In order to provide role-based access to medical users, this study proposes a blockchain-based framework for authenticating multiple entities based on the trust domain to reduce the administrative burden. This study is further validated by simulation on the infura blockchain using solidity and Python. The results demonstrate that role-based authorization and multi-entities authentication have been implemented and the owner of medical data can control access rights at any time and grant medical users easy access to a set of data in a healthcare system. The system has minimal latency compared to existing blockchain systems that lack multi-entity authentication and role-based authorization.</p></div>
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Kazuaki Nakamura · The Journal of The Institute of Image Information and Television Engineers · 2021
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Joseph F. Follettie · 1971
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Georges Ouffoué, Fatiha Zaïdi, Ana R. Cavalli et al. · 2017
Software-based systems are nowadays complex and highly distributed. In contrast, existing intrusion detection mechanisms are not always suitable for protecting these systems against new and sophisticated attacks that increasingly appear. In this paper, we present a new generic approach that combines monitoring and formal methods in order to ensure attack-tolerance at a high level of abstraction. Our experiments on an authentication Web application show that this method is effective and realistic to tolerate a variety of attacks.
Cited by 5
Alycia Brooke Honas · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2024
The Bayesian Attack Model (BAM) is an analytical tool designed to enhance the comprehension of adversarial activity in OT environments. BAM leverages both expert cybersecurity insights and historical data to characterize the likelihood of adversarial behavior given anomalous observable events.
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Jun Wu (4002), Tiecheng Song (501598), Yue Yu (260119) et al. · Figshare · 2018
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WORLD SCIENTIFIC eBooks · 2025
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Hong He, Li Luo, Yang Wang et al. · Advanced materials research · 2011
With the development of computer technology and network application, the safety of application has become one of the most important research problems in the field of information security. Firstly, the paper analyzes malicious attack approaches for applications. Secondly, this article proposes an application anti-attack model (AAM), and further presents the formal description of the model. Finally, this paper represents its good security properties and proves them.
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Francois Mouton · UpSpace Institutional Repository (University of Pretoria) · 2018
Information security is a fast-growing discipline, and relies on continued improvement of security measures to protect sensitive information. Human operators are one of the weakest links in the security chain as they are highly susceptible to manipulation. A social engineering attack targets this weakness by using various manipulation techniques to elicit individuals to perform sensitive requests. Social engineering is deeply entrenched in the fields of both computer science and social psychology. Knowledge is required in both these disciplines to perform social engineering based research. \n \nThe field of social engineering is still lacking with regards to standardised definitions, ethical concerns, attack frameworks, examples of attacks and detection models. The main focus of this thesis is the proposal of a social engineering attack detection model, however, this thesis also addresses gaps within the field with regards to standardised definitions, ethical concerns, attack frameworks and examples of attacks. \n \nThe first step of this journey was to review the existing definitions within the field of social engineering. After the review, this thesis proposed standardised definitions for social engineer, social engineering, social engineered and social engineering attack. It was also established that social engineering can only be performed over bidirectional, unidirectional and indirect communication. \n \nThis thesis also identifies a number of concerns regarding social engineering in public communication, penetration testing and social engineering research. It also discusses the identified concerns with regard to three different normative ethics approaches (virtue ethics, utilitarianism and deontology) and provides their corresponding ethical perspectives. \n \nFurthermore, this thesis proposes a social engineering attack framework based on Kevin Mitnick's social engineering attack cycle. The attack framework addresses shortcomings of Mitnick's social engineering attack cycle and focuses on every step of the social engineering attack from determining the goal of an attack up to the successful conclusion of the attack. \n \nThe social engineering attack framework is then utilised to derive detailed social engineering attack examples from real-world social engineering attacks within literature. Mapping several similar real-world examples to the social engineering attack framework allows one to establish a detailed flow of the attack whilst abstracting subjects and objects. This mapping is then utilised to propose the generalised social engineering attack examples that are representative of real-world examples, whilst still being general enough to encompass several different real-world examples. \n \nAfter all of the gaps within the field of social engineering were addressed, attention is shifted back towards the main focus of this thesis which is the social engineering attack detection model. There were three iterations of the social engineering attack detection model proposed throughout this thesis, with each iteration improving upon the limitations on the one prior. The first iteration of the social engineering attack detection model was designed with a call centre environment in mind and is only able to cater for social engineering attacks that use bidirectional communication. The second iteration of the social engineering attack detection model addresses this problem by extending the model to cater for social engineering attacks that use either bidirectional communication, unidirectional communication or indirect communication. The third iteration focuses on the proposal of the underlying finite state machine of the social engineering attack detection model. The third iteration of the social engineering attack detection model provides a more abstract and extensible model that highlights the inter-connections between task categories associated with different scenarios. Furthermore, the third iteration is intended to help facilitate the incorporation of organisation specific extensions by grouping similar activities into distinct categories, subdivided into one or more states. In addition, it facilitates additional analysis on state transitions that are difficult to extract from the second iteration. \n \nUltimately, this thesis proposes a refined social engineering attack detection model that can be utilised by industry to either implement into their environment or to be used as a social engineering awareness training tool. The social engineering attack detection model is also developed to be extensible so that other researchers can expand upon the proposed model. Lastly, the social engineering attack detection model can also be used as a comparative measure for future social engineering attack detection models.
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Hongyu YANG, Zaiming WANG · SHILAP Revista de lepidopterología · 2018
In order to solve the problem of poor efficiency and low accuracy of Android collusion detection,an Android collusion attack model based on component communication was proposed.Firstly,the feature vector set was extracted from the known applications and the feature vector set was generated.Secondly,the security policy rule set was generated through training and classifying the privilege feature set.Then,the component communication finite state machine according to the component and communication mode feature vector set was generated,and security policy rule set was optimized.Finally,a new state machine was generated by extracting the unknown application’s feature vector set,and the optimized security policy rule set was matched to detect privilege collusion attacks.The experimental results show that the proposed model has better detective efficiency and higher accuracy.
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Luo Yun, Xiao Fu, Bin Luo et al. · 2020
Recent trends have shown that botnets have been active since the 1990s. Attackers use newer technologies to damage enterprises and individuals through identity theft, bank fraud, spam campaigns, malware distribution, and distributed denial of service (DDoS) attacks. To identify the hidden details from a DDoS attack, we introduce a forensic model in this paper. This model uses NS2 to simulate the connectivity of real nodes in the network and uses Botnet and DDoS attack electronic evidence analysis methods. The botnet uses IRC channels as the basic unit. The analytical algorithm for Botnet uses election vectors to detect the split and transfer behavior of hackers. The analysis method for DDoS attacks uses attack vectors to detect whether Botnet is participating in a DDoS attack. On this basis, the fragmented packet marking method is added to track the source and path reconstruction of the router, thereby improving the scale recognition rate to 93%
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Dong, Fangqi · Figshare · 2023
Complete set of Social Engineering Attack Model from Dissecting the Process of Manipulating People: A State Transition-based Risk Assessment Approach for Social Engineering Attacks.
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Shangping Wang (3368915), Xiaoxue Zhang (1755322), Yaling Zhang (1687540) · Figshare · 2016
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Dong, Fangqi · Figshare · 2024
Complete set of Social Engineering Attack Model from Dissecting the Process of Manipulating People: A State Transition-based Risk Assessment Approach for Social Engineering Attacks.
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Dong, Fangqi · Figshare · 2024
Complete set of Social Engineering Attack Model from Dissecting the Process of Manipulating People: A State Transition-based Risk Assessment Approach for Social Engineering Attacks.
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Pierson Yieh · 2018
With recent advances in technology, Vehicular Ad-hoc Networks (VANETs) have grown in application. One of these areas of application is Vehicle Safety Communication (VSC) technology. VSC technology allows for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that enhance vehicle safety and driving experience. However, these newly developing technologies bring with them a concern for the vehicular privacy of drivers. Vehicles already employ the use of pseudonyms, unique identifiers used with signal messages for a limited period of time, to prevent long term tracking. But can attackers still attack vehicular privacy even when vehicles employ a pseudonym change strategy? The major contribution of this paper is a new attack model that uses long-distance pseudonym changing and short-distance non-changing protocols to associate vehicles with their respective pseudonyms.
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Et al. Jian Hu · Journal of Electrical Systems · 2024
All facets of society's governance, economics, and culture have been impacted by networking. Network attacks have become more common as a result of the digital revolution, which has also facilitated major changes in worldwide communication and accelerated the development of human society. An unauthorized attempt to access a network with the goal of committing theft or other forms of damage is what we call a network attack. This article focuses on building a chain security model utilising the block chain concept in order to address the issue of inaccurate assessments of forged malicious behavioural methods for identification. It uses the NIK-256 hashing algorithm to identify valid users utilising time-dependent verification. Passwords and recorded times are maintained in a combined chain database, which combines blockchain technology and the Trusted Platform Module (TPM), improving data security and privacy. After that, we schedule users using the Whale optimisation algorithm (WOA), which decreases difficulty, and then a smart contract is developed that grants authorised users access control based on their level of trust and permission. The proposed approach, known as Hyb_chain_TPM, implements countermeasures in accordance with the attack risk level and stores the attack graph in a combined chain database for future attack forecasting. Utilising diverse attack datasets, extensive tests are run to validate this system. Additionally, the outcomes of privacy protection and AI processes are assessed independently and contrasted using a variety of current techniques.
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Yu Liu, Kailiang Chen, Yue Chang et al. · 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · 2021
With the development of Internet of Things (IoT) technology, IoT attacks are more and more frequent. It's difficult to express the lateral IoT attack actions by the common models. Aiming at the problem of IoT attack modeling, this paper proposes a novel channel attack tree (Channel-AT) model. Firstly, we investigate the method of constructing an attack tree model automatically. The edges of attack trees are mined based on sequential patterns and association rules, and the attack tree is constructed under the same target. Then, we study the method of constructing Channel-AT model from the attack tree set. Attackers can use the channels to attack laterally among different attack trees. IoT attack channels are extracted among attack trees by two methods. One is that we can extract information channels as attack channels. Also, we calculate the similarity of nodes in different attack trees. There can be one attack channel if two nodes are very similar. Our work proposes a new correlation model which concludes the attack process, attack goal, and lateral attack action. Besides, we study the automatic modeling method based on sequential patterns and association rules.
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Yaling Zhang, Wang Jianian, Yichuan Wang · 2023
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USDOE Office of Cybersecurity, Energy Security, and Emergency Response (CESER), Lee Maccarone, USDOE National Nuclear Security Administration (NNSA) et al. · 2025
This document presents a user story for the Bayesian Attack Model (BAM) tool designed to aggregate and analyze cyber-attack observables for operational technology (OT) systems.BAM aims to empower cybersecurity analysts by providing a streamlined interface for collecting observable data from various sources, enabling real-time analysis of potential adversary activity.By enhancing the response capabilities of security teams, BAM facilitates risk-informed decision-making and improves organizational security posture.This user story outlines the key functionalities, user interactions, and requirements necessary to successfully integrate BAM with other security information and event management (SIEM) technology and cybersecurity operations centers (CSOCs). VALUE PROPOSITIONBAM aggregates observer experiences of anomalous events across OT environments to provide actionable decision intelligence.BAM can comprehensively analyze anomalous host-based, network-based, and physical environment observables.The primary benefit of aggregating a wide range of observables is to enhance the cybersecurity incident response process via earlier detection of adversarial activity and more effective response.Successful interruption of an OT cyber-attack may result in averting significant consequences including loss of revenue, damage to property, and loss of safety to facility staff and the public.
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