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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Mulahuwaish, Aos Towne, Wyatt Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Saginaw Valley State University Dept. of Computer Science and Information Systems Michigan United States Salahaddin University-Erbil Dep. of Software Engineering and Informatics Iraq Koya University Faculty of Engineering Dept. of Software Engineering Kurdistan Region-F.R. Iraq

ISBN: (纸本)9781728176246

The positioning accuracy of various kinds of devices is often calculated by using Wi-Fi fingerprints as a data source of Received Signal Strength (RSS). Through different matching algorithms, these RSS values are related to corresponding positioning coordinates for positioning estimation. In this paper, we compare two well known Machine Learning (ML) structures for calculating positioning accuracy: Recurrent Neural network-Long Short-Term Memory (RNN-LSTM) and a Decision Tree (DT). Both matching algorithms run through a series of datasets in two parts. In the first part, the datasets contain raw, non-normalized RSS values. In the second part, the RSS values are all normalized to remove noise in the datasets. After extensive testing and simulations, the tests showed that as long as RSS values are first normalized, a Decision Tree offers better average positioning accuracy. © 2020 IEEE.

关键词： Decision trees

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Secure Data Exchange in the Industrial Internet of Things network of the Fuel and Energy Complex

Secure Data Exchange in the Industrial Internet of Things Ne...

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2020 International Conference on Electrotechnical Complexes and systems, ICOECS 2020

作者： Emil, Hajrullin Vulfin, Alexey Mironov, Konstantin Frid, Arkadij Guzairov, Murat Kirillova, Anastasia Ufa State Aviation Technical University Dept. of Computer Engineering and Information Security Ufa Russia Ufa State Aviation Technical University Inmt Ural Federal University Ufa Russia

ISBN: (纸本)9781728191157

The issues of data exchange security increasing in the Industrial Internet of Things network of the fuel and energy complex enterprises based on the use of the domestic cryptographic protocol CRISP 1.0 are considered. A model example of industrial network infrastructure is analyzed and a fuzzy gray cognitive map is constructed to analyze local relative cybersecurity risks in the event of an internal attacker without using and using CRISP 1.0 as a protective measure. Indicators of local relative risk decreased by 35%. The use of cognitive analysis in the task of assessing cybersecurity risks allows us to take into account the spread of opinions of experts, as well as the inaccuracy and incompleteness of the data collected during the audit on the state and properties of the information system. Cognitive models make it possible to formalize the mutual influence of system elements and the destabilizing effects of internal and external attackers exploiting vulnerabilities of software and hardware components, and are an important decision support tool in the process of qualitative and quantitative risk assessments. © 2020 IEEE.

关键词： Cybersecurity

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Automatic Seed Generation based Hybrid Fuzzing for Code Coverage Efficiency 20

Automatic Seed Generation based Hybrid Fuzzing for Code Cove...

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Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications

作者： Soonhong Kwon Yangseo Choi Jong-Hyouk Lee Dept. of Computer and Information Security & Covergence Engineering for Intelligent Drone Sejong University Seoul Republic of Korea Electronics and Telecommunication Research Institute Daejeon Republic of Korea

ISBN: (纸本)9781450383042

Based on the 4th Industrial Revolution, numerous ICT technologies are developing, and for this reason, IoT devices are formed around us. Accordingly, hackers are inflicting financial and physical damage to our lives by using software vulnerabilities in IoT devices around us based on intelligent hacking technology. Automated security vulnerability response systems are required to respond to attacks through continuously occurring software vulnerabilities. In this paper, we analyze the hybrid fuzzing system that complements the technical limitations of the existing fuzzing technology as the base technology for an automated security vulnerability response system. In addition, we propose a hybrid fuzzing system based on an automatic seed generation mechanism for coverage efficiency in order to find vulnerabilities inherent in software quickly and efficiently.

关键词： Automatic Seed Generation Mechanism

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Efficient Context-aware File System Approach 7

Efficient Context-aware File System Approach

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7th International Conference on Software Defined systems, SDS 2020

作者： Razaque, Abdul Amsaad, Fathi Almiani, Muder Xiaoya, Wan Shiyu, Li Ganda, Raouf Rawashdeh, Majdi Alrahayfeh, Amer Dept. of Computer Engineering and Telecommunication Eastern Michigan University School of Information Security United States Al-Hussein Bin Talal University College of Information Technology Jordan Dept. of Computer Science NYIT Nanjing China Yale School of Management Yale University United States Princess Sumaya University for Technology Department of Business Information Technology Amman Jordan

ISBN: (纸本)9781728172194

With the rapid development of science and technology, wireless networks are now widely used throughout the world. Given the requirement to improve the usability of computers, a context-aware file system has been rendered as an important research topic. Enhanced methods must be proposed to improve the accuracy of user authority in such systems. In this study, we introduce a combined approach involving the historical and physical user contexts to gain the contextual information for the file system. We proposed a role-based access control (RBAC) based on context awareness model, which is an extension of the traditional RBAC model, to implement context awareness in the users' permission for the file system. Using the context constraints, the file system can control the access of the user to protect the file system from damages caused by hackers. Results showed that the proposed method is more accurate than traditional ones because it can control the user access through context awareness. © 2020 IEEE.

关键词： Access control

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Post Quantum Cryptography: Techniques, Challenges, Standardization, and Directions for Future Research

arXiv

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arXiv 2022年

作者： Bavdekar, Ritik Chopde, Eashan Jayant Bhatia, Ashutosh Tiwari, Kamlesh Daniel, Sandeep Joshua Atul Dept. of Computer Science and Information Systems Birla Insitute of Technology and Science Pilani Pilani Campus Jhunjhunu Rajasthan 333031 India Department of Electrical and Electronics Engineering Birla Insitute of Technology and Science Pilani Pilani Campus Jhunjhunu Rajasthan 333031 India

The development of large quantum computers will have dire consequences for cryptography. Most of the symmetric and asymmetric cryptographic algorithms are vulnerable to quantum algorithms. Grover’s search algorithm gives a square root time boost for the searching of the key in symmetric schemes like AES and 3DES. The security of asymmetric algorithms like RSA, Diffie Hellman, and ECC is based on the mathematical hardness of prime factorization and discrete logarithm. The best classical algorithms available take exponential time. Shor’s factoring algorithm can solve the problems in polynomial time. Major breakthroughs in quantum computing will render all the present-day widely used asymmetric cryptosystems insecure. This paper analyzes the vulnerability of the classical cryptosystems in the context of quantum computers, discusses various post-quantum cryptosystem families, discusses the status of the NIST post-quantum cryptography standardization process, and finally provides a couple of future research directions in this field. © 2022, CC BY.

关键词： Quantum computers

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A Survey on Explainable Artificial Intelligence for Cybersecurity

arXiv

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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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Distributed Trust Management for Secured Localization systems in Mobile Cloud Services

Distributed Trust Management for Secured Localization System...

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IEEE International Conference on Utility and Cloud Computing (UCC)

作者： Farouq Aliyu Mustafa Ghaleb Saud Mohammad Mostafa Sani Umar AbdalRahman Aburakhia Center of Excellence in Dev of NPO King Fahd University of Petr. & Min. Dhahran Saudi Arabia IRC for Intelligent Secure Systems King Fahd University of Petr. & Min. Dhahran Saudi Arabia Computer Engineering Department King Fahd University of Petr. & Min. Dhahran Saudi Arabia Dept. of Electrical Engr. and Computer Sci. Khalifa University Abu Dhabi UAE Information & Computer Science King Fahd University of Petr. & Min. Dhahran Saudi Arabia

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

The market for the Internet of Things (IoT) is expanding at an exponential rate. As IoT devices increasingly rely on cloud computing for data processing, storage, and management, the security of cloud-based IoT services becomes paramount. Many of these services depend on the IoT device’s location. Therefore, secured localization systems are necessary. However, traditional security techniques are resource-consuming. An alternative method for detecting localization attacks in IoT is through trust models. This paper proposes a distributed trust management technique for localization in Mobile IoT, specifically designed to enhance the security and trustworthiness of cloud-based IoT services. The trust of a node is computed based on its availability and service quality. Nodes use this information to calculate their neighbors’ and anchor nodes’ trust values. We simulated the proposed system using the Cooja simulator. The results show that the proposed trust-based localization technique can help unknown nodes mobilize and reach a target location, reducing the control group’s error by 186.4% at the cost of 4.8 μJ only. The system finds application in indoor localization for location-based cloud services, such as locating shops of items in shopping malls.

关键词： Location awareness Cloud computing Costs Computational modeling Data processing Internet of Things security Trust management

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A new smart-cropping pipeline for prostate segmentation using deep learning networks

arXiv

引用

arXiv 2021年

作者： Zaridis, Dimitrios G. Mylona, Eugenia Tachos, Nikolaos S. Marias, Kostas Papanikolaou, Nikolaos Tsiknakis, Manolis Fotiadis, Dimitrios I. The Dept. of Biomedical Research FORTH-IMBB Ioannina Greece The Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering University of Ioannina IoanninaGR 45110 Greece The Institute of Computer Science FORTH Heraklion Greece The Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Greece The Centre for the Unknown Champalimaud Foundation Clinical Computational Imaging Group Lisbon Portugal Crete Heraklion Greece The Dept. of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH Ioannina Greece The Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR 45110 Greece

Prostate segmentation from magnetic resonance imaging (MRI) is a challenging task. In recent years, several network architectures have been proposed to automate this process and alleviate the burden of manual annotation. Although the performance of these models has achieved promising results, there is still room for improvement before these models can be used safely and effectively in clinical practice. One of the major challenges in prostate MR image segmentation is the presence of class imbalance in the image labels where the background pixels dominate over the prostate. In the present work we propose a DL-based pipeline for cropping the region around the prostate from MRI images to produce a more balanced distribution of the foreground pixels (prostate) and the background pixels and improve segmentation accuracy. The effect of DL-cropping for improving the segmentation performance compared to standard center-cropping is assessed using five popular DL networks for prostate segmentation, namely U-net, U-net+, Res Unet++, Bridge U-net and Dense U-net. The proposed smart-cropping outperformed the standard center cropping in terms of segmentation accuracy for all the evaluated prostate segmentation networks. In terms of Dice score, the highest improvement was achieved for the Unet+ and ResU-net++ architectures corresponding to 8.9% and 8%, respectively. © 2021, CC BY.

关键词： Image segmentation

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Unsupervised Machine Learning for Malicious network Activities

Unsupervised Machine Learning for Malicious Network Activiti...

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2019 International Conference on Applied and engineering Mathematics, ICAEM 2019

作者： Hassan, Aamina Tahir, Shahzaib Baig, Ahmed Iftikhar Dept. of Information Security College of Signals National University of Sciences and Technology Islamabad Pakistan Dept. of Information Security Institue of Systems Engineering Riphah International University Islamabad Pakistan

ISBN: (纸本)9781728123530

Increase in cybercrimes in the last few years has dramatically increased the need for the network intrusion detection and subsequently its mitigation. Several classified intrusion detection systems (IDS) are currently in use but increase in dynamics of the cyber invasion hunts for more adaptive and intelligent model. This research covers the critical analysis and comparison of the Machine Learning (ML) network intrusion detection techniques, their use cases and proposes an unsupervised and fast ML implementation model for intrusion detection. The proposed model works on anomaly-based detection. ELK stack (Elasticsearch, Logstash and Kibana) has been used for unsupervised implementation model for exodus DNS requests in a wired network. © 2019 IEEE.

关键词： Machine learning

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The concept of integrity of telemetric information about the state of an aircraft power plant monitoring

The concept of integrity of telemetric information about the...

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2019 International Conference on Electrotechnical Complexes and systems, ICOECS 2019

作者： Guzairov, Murat Frid, Arkadij Vulfin, Alexey Berkholts, Victoria Ufa State Aviation Technical University Dept. of Computer Engineering and Information Security Ufa Russia

ISBN: (纸本)9781728117287

The issues of creating a system for monitoring the integrity of the accumulated data in a modular system for collecting, storing and processing telemetric information about the state of an aircraft's power plant based on the use of its model and modern technologies for the intelligent analysis of telemetric information are considered. A block diagram of a data integrity monitoring system based on the power plant model and an intellectual analysis of technological time is proposed, which allows to identify the fact of unauthorized access to the system and to increase the security of telemetry information from unauthorized modifications. A model for analyzing the accumulated telemetry data based on the technologies of neural network processing of technological time series has been developed. Numerical modeling using field data has confirmed the efficiency of the proposed analysis model. © 2019 IEEE.

关键词： Telemetering equipment

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