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2nd International Conference on cyber Resilience, ICCR 2024

作者： Al-Hasnawi, Abduljaleel Niu, Yitong Tawfeq, Jamal F. Ranjan Pradhan, Manas Salahat, Mohammed Ghazal, Taher M. Ministry Of Higher Education And Scientific Research Baghdad Iraq College Of Information Technology Imam Ja'afar Al-Sadiq University Department Of Communication Technology Engineering Baghdad Iraq School Of Aeronautical Engineering AnYang University Anyang China Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

The privacy of sensitive data within the Internet of Things (IoT) is at risk due to potential privacy violations. These violations can occur through illegal access by external entities, as well as inadvertent disclosures or intentional attacks by those within the system who exceed their authorized access privileges. Furthermore, there needs to be more control experienced by data owners regarding their sensitive data as it transitions from their domain to other entities within the Internet of Things (IoT) ecosystem. Numerous methods have been presented in the academic literature over the past few years to effectively tackle the privacy concerns associated with the Internet of Things (IoT). This paper aims to classify existing literature by organizing it according to the specific technology or technique employed to safeguard privacy. The classification encompasses four primary areas of privacy solutions, specifically, solutions based on encryption, solutions based on identity management, solutions based on policy enforcement, and solutions focused on self-protection. The paper examines various state-of-the-art solutions within each category. It begins by providing a broad comparison between these solutions and our work. Subsequently, it focuses on a specific comparison of the privacy objectives of the offered PEFM solutions. © 2024 IEEE.

关键词： Internet of things

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A Scalable and Lightweight Authentication Architecture for the Internet of Things (IoT) in Smart Home Applications 2

A Scalable and Lightweight Authentication Architecture for t...

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2nd Zimbabwe Conference on Information Communication and technology, ZCICT 2023

作者： Gamundani, Attlee M. Phillips, Amelia Muyingi, Hippolyte N. Namibia University of Science and Technology Faculty of Computing & Informatics Cyber Security Department Windhoek Namibia University of the Cumberlands School of Computer and Information Sciences WilliamsburgKY United States Namibia University of Science and Technology Faculty of Computing & Informatics Windhoek Namibia

ISBN: (纸本)9798350318128

In the evolving landscape of smart home applications, ensuring robust and efficient authentication within the Internet of Things (IoT) environments is paramount. This paper presents a scalable, lightweight authentication architecture for IoT-driven smart homes. The proposed architecture, drawing inspiration from cloud computing mechanisms, introduces a trusted, smart home agent, integrating the Data-Information-Knowledge-Wisdom (DIKW) model to facilitate efficient and secure data exchange within smart homes. Our solution addresses the challenges of minimal computation and processing overheads, essential in resource-constrained IoT devices, and focuses on device-level authentication to reduce computational costs. A comparative analysis with existing models demonstrates the superiority of our architecture in terms of computational efficiency, making it an ideal candidate for scalable deployments. The architecture successfully navigates the complexities of ensuring secure communication among autonomous IoT devices, balancing the dual needs of computational efficiency and robust security, thereby promising resilience against common security threats in the smart home ecosystem. © 2023 IEEE.

关键词： Authentication

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Implementing Three Factors Authentication System (3FA) to Maintain Employee Attendance Record to Enhance security

Implementing Three Factors Authentication System (3FA) to Ma...

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Intelligent Technologies (CONIT), International Conference on

作者： Plezzant Columbus Johnson Shri Kant Sandeep Kumar Department of Computer Science Engineering Sharda University Center for Cyber Security and Cryptology Sharda University

Today as we all know that maintaining of employee credential have been some major issues around the world. Finding it more difficult to keep track of employees. With this, the company, institution, or government entity does not know who has entered their work-spaces to steal important information from their systems or databases. This serves as a deep wound for any company that does not keep track of its employees. This has been a major issue within so many Africans country, most especially Liberia. where so many companies, institutions, and governmental entities losing huge amounts of data. The means by which they do not track employee records have been with the traditional sign-in method, which has to do with the pen and paper or book sign in and sign out method, which can easily be stolen or damaged. This is one of the methods that employees use to not sign in when they get to work in order to steal data without anyone knowing that they were at work that day. Coming up with a method that will enable strong security and secure maintenance of employees records with less consuming and a strong level of three-factor authentication (3FA) will enable all employees to sign in with their three-level credentials, which have to do with something you know (knowledge), something you have (possession), or something you are (Inherence).The trustworthiness of identification is increased because this data cannot be copied or hacked. Furthermore, Three (3) Factor Authentication attempts to demonstrate, from a broader perspective, why it is more important to review your security procedures in order to prevent unauthorized persons from gaining access to your systems and obtaining data and information that you have not provided them with. Our system is going to allow user signing in with their user credential on your system after entering your username and password which authenticate you into the system, you as an employee to be verified and attendance to be mark you will use your Fa

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Accounting for systemic risk in analysis of crisis scenarios 21

Accounting for systemic risk in analysis of crisis scenarios

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21st International ISCRAM Conference, ISCRAM 2024

作者： Gonzalez, Jose J. Abbas, Reem Paulsen, Sigurd University of Agder Kristiansand Norway Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Dept. of Organization Crisis Management and Information Security Kristiansand Norway

Systemic risk is ubiquitous in our increasingly globalised world owing to the interconnections within and across sectors. However, a review of National Risk Assessments performed in OECD member countries reveals that systemic risk is rarely mentioned, and it is never accounted for in those assessments. A powerful method accounts for systemic risk and identifies mitigating strategies using participatory modelling with stakeholders: strategy mapping with Strategyfinder. The risk model is represented as a directed graph consisting of risk nodes and directed edges (arrows) expressing causal influences. The method originated in the management of large, complex projects, and it has been applied to systemic risks in critical infrastructures and pandemics. We demonstrate the Strategyfinder method using a scenario describing a cyberattack on electronic communication infrastructure leading to serious consequences for health and lives. The scenario is borrowed from the report Analysis of Risk Scenarios by the Norwegian Directorate for Civil Protection. © 2024 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Cascading effects Disaster risk reduction National Risk Assessments Risk scenarios Vicious cycles

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Privacy Preservation Techniques in Smart Grids: Balancing security and Utility in IoT-Driven Environments

Privacy Preservation Techniques in Smart Grids: Balancing Se...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Hussien AbdelRaouf Michael Long Paul Tran Luna Baral Zuha Khan Mohamed Mahmoud Mostafa M. Fouda Mohamed I. Ibrahem School of Computer and Cyber Sciences Augusta University Augusta GA USA Cyber Security Engineering Department George Mason University Fairfax VA USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

The Internet of Things (IoT) has significantly impacted the energy industry, leading to increased reliance on smart grid (SG) technology. However, integrating electricity with the internet introduces security vulnerabilities, exposing customers’ consumption profiles. To protect this sensitive data, energy companies must employ novel algorithms that balance security compliance with utility. These models mask customer data using various methods, including differential privacy, which obfuscates data by adding noise. Another approach combines perturbation and encryption to generate noise from both central and local units. This study aims to identify factors that determine the most effective model, balancing utility and security. Comparing these solutions will provide insights for improving current deployments in smart meter (SM) privacy preservation.

关键词： Industries Privacy Perturbation methods Electricity Noise Smart meters Smart grids Encryption security Internet of Things

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Optimizing Distributed Denial of Service (DDoS) Attack Detection Techniques on Software Defined Network (SDN) Using Feature Selection 4

Optimizing Distributed Denial of Service (DDoS) Attack Detec...

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4th International Conference on Innovations in computer Science, ICONICS 2024

作者： Haq, Inamul Khan, Salman A. Mohammad, Nazeeruddin Zaman, Ali College of Computing and Info. Sciences Karachi Institute of Economics and Technology Karachi Pakistan Cyber Security Center Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Shaheed Zulfiqar Ali Bhutto University Computer Science Department Karachi Pakistan

ISBN: (纸本)9798331504342

Distributed denial of service (DDoS) attacks refer to a category of cyber attacks in which an attacker attempts to overwhelm a resource, such as a server, destination, or bandwidth through various means. This is usually done by either flooding the traffic or sending a heavy crunching job to exhaust the CPU or heavy amounts of data to pileup the storage. By the rise of virtualization and cloud technologies, networks have also evolved from hardware to a piece of software which virtualizes the operation of a network equipment. This virtualization is termed as a Software Defined Network (SDN). Due to the sheer impact of cyberattacks on the performance of networks, the study of DDoS attacks on SDN is vital in the present era. Contemporary approaches such as machine learning and deep learning have proven to be effective in attack detection of DDoS in SDNs. In this paper, a preliminary analysis is carried out to study the impact of feature selection to increase the throughput and response time of Machine learning techniques in detection of DDoS attacks on SDN. The results indicate that feature selection, when applied to machine learning algorithms, is an effective approach for DDoS attack detection in SDNs. © 2024 IEEE.

关键词： cyber attacks

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Secure Channel Creation for Task Delivery in Vehicular Cloud Computing to Avoid Denial of Service (DoS)

Secure Channel Creation for Task Delivery in Vehicular Cloud...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： A. Meenambika S. Pavalarajan N. Anu Lavanya B. Ajith Jerom S. Divya Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India Department of CSE (Cyber Security) PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Vehicular Cloud Computing (VCC) offers a promising platform for supporting various automotive applications and services. However, its distributed and dynamic nature makes it susceptible to Denial-of-Service (DoS) attacks, particularly during channel establishment. This research proposes a comprehensive approach to mitigate these risks by integrating a suite of security mechanisms. These include rate-limiting, resource management, traffic analysis, access control, authentication, encryption, intrusion detection, behavioral analysis, and blacklisting/whitelisting techniques. By incorporating these measures, the proposed approach enhances the security and reliability of VCC, ensuring the availability of critical automotive services and protecting vehicles from the impact of DoS attacks.

关键词： Cloud computing Renewable energy sources Intrusion detection Transportation computer architecture Organizations Denial-of-service attack Safety Resource management Vehicle dynamics

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Extraction of Actionable Threat Intelligence from Dark Web Data

Extraction of Actionable Threat Intelligence from Dark Web D...

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International Conference on Control Communication & Computing India (ICCC)

作者： Varsha Varghese Mahalakshmi S Senthilkumar Kb Cyber Forensics and Information Security Department of Computer Science & Engineering ER&DCI Institute of Technology Thiruvananthapuram Cyber Security Group Centre for Development of Advanced Computing Thiruvananthapuram Scientict_E Cyber Security Group Centre for Development of Advanced Computing Thiruvananthapuram

Darknet has emerged as an excellent platform for cybercriminals to conduct various illicit activities such as operating a fully functional crypto-currency-based marketplace or maintaining a highly anonymous communication channel. However, it also acts as a source of cyber Threat Intelligence which is the information about the techniques, tactics, or motives of an emerging threat. The life cycle of a Darknet-based cyber Threat Intelligence solution mainly consists of the Collection, Processing, Analysis, and Production of Data from the Darknet. The Collection and Processing stages deal with accumulating information from various dark web pages and transforming the data into some format using a big data system. The Analysis part is integral to the Threat Intelligence schema since it constitutes the extraction of named entities like Organization, Offensive Activity, Tools, etc., and the determination of relationships between these entities. The production stage is like data mining where some knowledge is extracted that can be relevant to an organization under consideration. Such knowledge can mitigate risks and disrupt any targeted cyber-attack campaigns, making cyber Threat Intelligence actionable. In this research, an open-source-intelligence toolset is used to scan and collect data from the dark web forums through crawling and scrapping. The collected data is then ingested into a state-of-the-art NLP model that extracts actionable threat intelligence using Named Entity Recognition. The experimental results indicate that the model could identify HackerIds, tools, software, organizations, and other entities in the discussions of dark web forums with better efficiency and accuracy. This could be used to identify the source of a data leak, the release of new malware, evidence of a new exploit, and other offensive activities.

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Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 34-48页

作者： Hammadi, Ahmed Al Bariah, Lina Muhaidat, Sami Al-Qutayri, Mahmoud Sofotasios, Paschalis C. Debbah, Merouane Technology Innovation Institute Abu Dhabi United Arab Emirates Khalifa University Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaK1S 5B6 Canada Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Tampere University Department of Electrical Engineering Tampere33014 Finland Khalifa University KU 6G Center Abu Dhabi United Arab Emirates

Visible Light Communication (VLC) is a promising enabling technology for the next-generation wireless networks, as it complements radio-frequency (RF)-based communications by providing wider bandwidth, higher data rates, and immunity to interference from electromagnetic sources. However, due to its unique characteristics, VLC is highly sensitive to the line-of-sight (LoS) blockage. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative solution that dynamically reconfigures the wireless environment. The present contribution proposes a two-stage resource management framework in an indoor VLC system: In the first stage, a maximum possible fairness (MPF) algorithm is presented in order to maximize the fairness amongst the users. In the second stage, deep Q-learning is exploited in order to maximize the overall spectral efficiency (SE). The corresponding numerical results have shown that the proposed DQL-MPF framework exhibits superior performance in terms of both the overall SE and Jain's Fair Index, achieved at a fast convergence rate. More specifically, when the noise power is high and the number of users is relatively large, the DQL-MPF algorithm achieves a more than tenfold overall SE compared to the Baseline scheme. Moreover, the synergy between the MPF and the DQL algorithms is investigated. To this end, we demonstrate that the MPF algorithm maximizes the fairness amongst the users while the DQL algorithm maximizes the overall SE and improves the robustness against the noise. Our results also highlight the effectiveness of the proposed algorithm in leveraging the increasing number of IRS elements for optimized performance. © 2023 The Authors.

关键词： Spectrum efficiency

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Hybrid Grey Wolf and Particle Swarm Optimized (HGW-PSO) Scheduling in Containerized Cloud Computing Environment

Hybrid Grey Wolf and Particle Swarm Optimized (HGW-PSO) Sche...

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Emerging technology (INCET), International Conference for

作者： S.M. Udhaya sankar Martin Joel Rathnam Aswanth Babu Harikumar G J Keerthi Nadh Department of Computer Science and Engineering(Cyber Security) R.M.K College of Engineering and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Sri Sairam college of Engineering Bengaluru Karnataka India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The concept of containerization consists of encapsulating applications with their libraries with the help of operating system level virtualization, ensuring they are separated from other processes on the same machine. Containers gained popularity in several data centers due to their lightweight nature and simplified deployment. Application containerization technology is a fundamental technology that enables the deployment of numerous containers on a single physical node. A single physical node has the capability to offer several services to users. The adoption of microservice architecture is on the rise as it offers several benefits over monolithic designs, such as simplified deployment, enhanced versatility, and improved accessibility. The rise of containerization has significantly increased the significance of microservices, enhancing their scalability and portability. Many researchers have developed theories and solutions for the microservice container scheduling problem, yet limits remain. The algorithm's tendency to become stuck in local optimization and its slow optimal solution search are examples. In this work we present a proposal for grey wolf and particle swarm optimized (GW-PSO) scheduling in a Containerized Cloud Computing Environment. During the first phase of the search, the GW's global search can direct the PSO away from local optima, preventing early convergence. In the last phases of the search, the PSO can improve GW search along the Pareto optimum frontier. The research findings demonstrate, in comparison to the other 3 methods, the algorithm achieves a 19.06% reduction in network transmission cost, a 15.57% improvement in load balancing performance, and a 6.67% enhancement in service stability.

关键词： Cloud computing Scheduling algorithms Scalability Microservice architectures Containers Resource management Proposals

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