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GAN-Based Anomaly Intrusion Detection for Industrial Controller System 1

World Conference on Information systems for Business Management, ISBM 2023

作者： Riya V. Raj Sangeetha, V. Amritha, P.P. TIFAC-CORE in Cyber Security Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Information Systems BITS Pilani Pilani India

ISBN: (数字)9789819983469

ISBN: (纸本)9789819983452

Industrial controller system (ICS) is becoming more and more important in daily lives. In recent years, ICS has become more frequent targets of cyberattacks. In addition to the system, the environment is also significantly impacted by the ICS cyberattack. The main aim of ICS intrusion detection is a process of anomaly detection because cyberthreats cause anomalies to occur in the ICS and components under its control. With a machine learning or deep learning aid, the IDS can produce precise detection outcomes. Though machine learning models can be used to detect cyberattacks, there is a challenge in handling imbalanced real-time data. In this paper, we have implemented generative adversarial networks, to resolve the issue of imbalance in datasets by creating class-specific adversarial samples and further detecting anomalies with greater efficiency. This proposed method is tested on Secure Water Treatment Dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Towards a User-Friendly Web Application Firewall 11

Towards a User-Friendly Web Application Firewall

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11th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2023

作者： Omar, Fatima Ahmed, Dalia Elnakib, Omar Ahmed, Mohamed Farhan, Nada Hindy, Hanan Abdel-Hamid, Mahmoud Badawi, Yehia Ain Shams University Faculty of Computer and Information Science Cyber Security Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Computer Systems Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Cyber Security Department Giza Egypt Ain Shams University Faculty of Computer and Information Science Computer Science Department Cairo Egypt

ISBN: (纸本)9798350322101

Web Application Firewall (WAF) systems play a vital role in protecting web applications from various security vulnerabilities. However, existing WAF solutions are often costly and complex to configure, limiting their accessibility to smaller organizations. This paper aims to develop an affordable and user-friendly WAF framework that mitigates the most common security vulnerabilities identified in the Open Web Application security Project (OWASP) Top 10. The proposed WAF framework provides protection against attacks such as Cross-Site Scripting (XSS), Structured Query Language Injection (SQLi), Operating System (OS) Command Injection, File Upload, Denial of Service (DoS), Cross Site Request Forgery (CSRF), Path Traversal, and XML External Entity (XXE) attacks. The framework offers an easy-to-use interface with features for easy configuration of mitigation settings. Users can monitor web traffic and receive real-time alerts to quickly identify and respond to potential security threats. Proxy techniques are used to intercept and handle user requests, sanitizing them using the mitigation techniques to prevent the inclusion of malicious code or unauthorized data. Additionally, two machine learning models were implemented. The first model detects malicious requests, and the second model classifies attacks based on their type. Mitigation techniques are employed to filter requests and prevent specific attack types. Regular expressions (Regex) are used to remove malicious code related to XSS, SQL injection, OS Command Injection, File Upload, CSRF, XXE, and Path Traversal attacks. Rate limiting is implemented to mitigate DoS attacks. © 2023 IEEE.

关键词： Denial-of-service attack

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A Review of Key Elements for Underwater Wireless Sensor networks

A Review of Key Elements for Underwater Wireless Sensor Netw...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Al-Dulaimi, Aymen Mohammed Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Aliesawi, Salah A. Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq

ISBN: (纸本)9798350369106

Underwater Wireless Sensor networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications. © 2024 IEEE.

关键词： Wireless sensor networks

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Novel Feature Extractor Framework in Conjunction with Supervised Three Class-XGBoost Algorithm for Osteosarcoma Detection from Whole Slide Medical Histopathology Images

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computers, Materials & Continua 2025年第2期82卷 3337-3353页

作者： Tanzila Saba Muhammad Mujahid Shaha Al-Otaibi Noor Ayesha Amjad Rehman Khan Artificial Intelligence&Data Analytics Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.

关键词： Medical image processing deep learning healthcare image classification histopathology

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A Novel Deep Learning Framework for Contraband Items Recognition in Smart City Applications 2nd

A Novel Deep Learning Framework for Contraband Items Recogni...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khalid, Shehzad Ahmed, Zubair Noor, Ayesha Sedik, Ahmed Asif, Waqar Department of Computer Engineering Bahria University Islamabad Pakistan Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Computer Science and Cyber Security University of West London London United Kingdom

ISBN: (纸本)9789819783441

Ensuring safety and security is paramount in today’s complex environment, and the effective detection of contraband items plays a pivotal role in achieving this objective. Contraband items, ranging from illegal substances to unauthorized goods, pose a threat to public safety, security, and the overall well-being of smart city inhabitants. Such items are currently detected by human operator reviewing the images from X-ray baggage scanners. However, manual detection of contraband items is inherently challenging and time-consuming resulting in significant delays at crowded places such as airports, train-stations, shopping malls etc. Moreover, there is a significant risk of overlooking certain items that could pose potential harm. To address these challenges, there is a growing demand for intelligent systems for contraband items detection that can efficiently and accurately detect items whilst minimizing false negatives. Automated deep learning solutions offer a sophisticated and technologically advanced approach to enhance the accuracy and speed of the detection process. In our pursuit to address this challenge comprehensively, we have obtained an X-ray Imaging Dataset specifically curated for this purpose. The dataset includes five types of objects including guns, knives, pliers, scissors, and wrenches that are typically banned to carry along. In this paper, we have proposed a deep learning-based approach to efficiently and accurately detect contraband items from X-ray images. The proposed approach is based on YOLO architectures that has been shown to perform better for object detection in variety of domains both in terms of accuracy and real-time performance. We have evaluated different versions of YOLO to select the version that works best for contraband item detection from X-ray images. Yolo-v8 has shown superior performance followed by Yolo-v5 in terms of accuracy. Challenges regarding class imbalance have been addressed using data augmentation especially for clas

关键词： Deep learning

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Backslash map: An Automated Vulnerability Scanner 11

Backslash map: An Automated Vulnerability Scanner

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11th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2023

作者： Alian, Alshaimaa Abo Sobhy, Bishoy Nasser, Marina Hani, Lydia Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Cyber Security Department Cairo Egypt

ISBN: (纸本)9798350322101

Web application vulnerability detection is critical to cybersecurity. Web applications are vulnerable to attacks from a variety of sources, with varying degrees of scale and complexity. Therefore, it becomes essential to employ an automated tool that can effectively examine and identify vulnerabilities within the application. This research paper introduces Backslash Map, an automated system for penetration testing that enables efficient identification of web application vulnerabilities. The proposed system utilizes signature-based scanning techniques and predefined payloads. It offers key features such as subdomain enumeration (passive or active), port scanning, and detection of SQL injection, server-side template injection, and code execution vulnerabilities. During the vulnerability assessment, Backslash Map inspects and filters requests to identify vulnerable endpoints while performing subdomain enumeration on domain services. The experimental results prove the promising performance of the proposed system when compared with the state-of-the-art. © 2023 IEEE.

关键词： Automation

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Channel Aging-Aware LSTM-Based Channel Prediction for Satellite Communications

IEEE Networking Letters

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IEEE Networking Letters 2024年第3期6卷 183-187页

作者： Abbasi, Omid Kaddoum, Georges Carleton University Non-Terrestrial Networks Lab Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Université du Québec École de Technologie Supérieure Department of Electrical Engineering MontrealQCH3C 1K3 Canada Lebanese American University Cyber Security Systems and Applied Ai Research Center Beirut2801 Lebanon

Satellite communication systems encounter channel aging issues due to the substantial distance that separates users and satellites. In such systems, the estimated channel state at a given time slot reflects the channel state from several time slots in the past. This letter proposes a long short-term memory (LSTM)-based architecture for channel prediction to mitigate the channel aging problem. The proposed scheme predicts the next time slot's channel based on a block of estimated channel state information (CSI) from previous time slots. We consider the effect of channel aging in the training phase so that channel prediction in the testing phase is performed based on available data. We demonstrated through simulation experiments on new radio non-terrestrial network tapped delay line (NR NTN TDL) channel models, that our proposed scheme can effectively mitigate channel aging, and that it performs better than outdated channels. The proposed scheme improves the reliability and efficiency of satellite communication systems with long propagation delays. © 2019 IEEE.

关键词： Satellite communication systems

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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Empirical Evaluations of Machine Learning Effectiveness in Detecting Web Application Attacks 7th

Empirical Evaluations of Machine Learning Effectiveness in...

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7th EAI International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, EAI FABULOUS 2023

作者： Ismail, Muhusina Alrabaee, Saed Harous, Saad Choo, Kim-Kwang Raymond Department of Information Systems and Security CIT United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

ISBN: (纸本)9783031500503

Web applications remain a significant attack vector for cybercriminals seeking to exploit application vulnerabilities and gain unauthorized access to privileged data. In this research, we evaluate the efficacy of eight supervised machine learning algorithms - Naive Bayes, Decision Tree, AdaBoost, Random Forest, Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) - in detecting and countering web application attacks. Our results indicate that KNN and Random Forest classifiers achieve an accuracy rate of 89% and an area under the curve of 94% on the CSIC HTTP dataset, a commonly used benchmark in the field. Meanwhile, the Naive Bayes classifier proves the most efficient, taking the least computational time when differentiating between malicious and benign HTTP requests. These findings may help direct future efforts towards more efficient, machine learning-driven defenses against web application attacks. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Machine Learning Web Attacks Web Vulnerabilities

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