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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Bashir, Umar Mohammed Nyangwarimam, Ali Obadiah Thomas, Sadiq Odimba, Chinomso Roselyn Lawal, Shariff Aboaba, Adulfatah A. Elijah, Olakunle Nile University of Nigeria Dept. of Computer Engineering Abuja Nigeria Nile University of Nigeria Dept. of Industrial Chemistry Abuja Nigeria Airforce Institute of Technology Dept. of Mechanical Engineering Kaduna Nigeria University of Maiduguri Dept. of Computer Engineering Borno Nigeria

ISBN: (纸本)9798350358834

This Paper Presents a computer-based census management system which is a software application designed to collect, store, manage, and analyze census data electronically. It replaces traditional paper-based methods of data collection and processing, making the census process more efficient, accurate, and secure. The system typically consists of several components that work together to facilitate the census data management process. © 2023 IEEE.

关键词： Information management

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Dynamic Threat Intelligence Coalitions for Improved Cyber Defense Capabilities

Dynamic Threat Intelligence Coalitions for Improved Cyber De...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Omar Abdel Wahab Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada

ISBN: (数字)9798350376562

ISBN: (纸本)9798350376579

In the dynamic landscape of cybersecu-rity and cyber warfares, Cyber Threat Intelligence (CTI) is increasingly relied on for gathering and sharing the latest information about threats and their trends. Current CTI sharing methods (e.g., ISACs, automated STIX/TAXII platforms), face challenges in terms of scalability, trust, and data quality issues. This is because they often lack systematic metrics for evaluating the quality and relevance of the threat data that are being shared. Moreover, they do not offer any mechanism to enable participating organizations to autonomously make decisions as to what Threat Intelligence providers to request and share data from/to. To address these limitations, we propose a novel Threat Intelligence sharing approach based on coalitional game theory. We first propose a set of metrics that enable organizations to assess the effectiveness of the shared Threat Intelligence data. Based on these metrics, we propose a preference function and a coalition formation algorithm that enable organizations to autonomously join and leave Threat Intelligence coalitions until reaching a Nash-Stable situation wherein no organization has incentive to leave its current coalition and join another one. Experiments suggest that our solution significantly improves the Mean Time to Detect (MTTD), Mean Time to Respond (MTTR) and Containment Rate.

关键词： Measurement Systematics Machine learning algorithms Scalability Data integrity Organizations Reliability theory Market research Real-time systems Cyber threat intelligence

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4×4 Array Multiplier Using Transmission Gate Full Adder 9

4×4 Array Multiplier Using Transmission Gate Full Adder

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Rathore, Nitesh Kumar Beohar, Ankur Sarkar, Pallabi Khare, Kavita Singh, Pooran Mahindra University Dept. of Electrical And Computer Engineering Hyderabad India Madhya Pradesh India Manit Bhopal Dept. of Electronic And Communication Engineering Madhya Pradesh India

ISBN: (纸本)9798350394474

Multiplication is the most essential function carried out by the arithmetic and logic unit. The multiplier specifically has important applications in signal processing analysis, image processing analysis, and so on. The design of multipliers with high-performance properties such as fast speed, low power, small size, and minimal latency is of great interest in the research field. In this research, a 4×4 array multiplier using a transmission gate full adder has been designed. The full adder used in the design is a conventional CMOS full adder implemented using XOR, AND, OR gates with a load capacitance of 32F. In the architecture, the same 4×4 multiplier has been designed using semi-dynamic logic and has carried out different design parameters calculations and observations respectively along with process, voltage, and temperature variation (PVT) of 10%. When compared to Full Adder implementation using a semi-dynamic logic gate, Full Adder implementation utilizing a transmission gate imparts less latency. Each of the mentioned logic blocks is designed using LT-Spice software for 180nm technology. © 2024 IEEE.

关键词： CMOS integrated circuits

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Deep Neural Networks in Social Media Forensics: Unveiling Suspicious Patterns and Advancing Investigations on Twitter 3

Deep Neural Networks in Social Media Forensics: Unveiling Su...

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3rd Intelligent Cybersecurity Conference, ICSC 2023

作者： Sharrab, Yousef Al-Fraihat, Dimah Alsmirat, Mohammad Isra University Dept. of Data Science and Artificial Intelligence Amman Jordan Isra University Dept.of Software Engineering Amman Jordan University of Sharjah Dept. of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798350382174

Text data forensics, a rapidly developing field that focuses on analyzing textual content to identify criminal or suspicious activities, is becoming increasingly important due to the popularity and the huge number of text posts on social media platforms. This study aims to improve the detection of suspicious text on social media using deep neural networks. Suspicious text is defined as any text that is likely to be associated with criminal activity or is unusual or out of the ordinary. This study could make a significant contribution to the field of text data forensics by helping to improve the detection of such text. We leveraged the 'CIC Truth Seeker Dataset 2023' [1], which is widely recognized as a comprehensive and representative dataset for text data forensics research. The dataset contains over 180,000 tweets related to 700 real and 700 fake pieces of news, labeled by experts. In this study, we enhance text data forensics in social media by leveraging the powerful analytical capabilities of deep neural networks. More specifically, we investigate the effectiveness of Long Short-Term Memory (LSTM) in the detection of suspicious text. The results are very promising as we achieved an accuracy of 96% during preliminary evaluations. We plan to explore future work on the model's potential applications, including criminal activity identification, misinformation detection, and online harassment prevention. © 2023 IEEE.

关键词： Sentiment analysis

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Federated Learning with Clustering-Based Participant Selection for IoT Applications

Federated Learning with Clustering-Based Participant Selecti...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Wang, Kevin I-Kai Ye, Xiaozhou Sakurai, Kouichi The University of Auckland Dept. of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Dept. of Informatics Fukuoka Japan

ISBN: (纸本)9781665480451

Modern Internet of Things (IoT) systems are highly complex due to its mobile, ad-hoc and geographically distributed nature. Very often, an edge-cloud infrastructure is established to offer intelligent services in modern IoT systems. However, IoT edge devices are typically resource-constrained and can not perform sophisticated machine learning algorithm on board. Data sharing with a central server is a common approach of crowdsourcing, but also brings privacy and security concerns. The emerging federated learning offers a promising pathway to achieve an accurate model through distributed machine learning while ensuring data privacy. The existing federated learning process is not tailored to the mobile and adhoc nature of IoT systems where devices are of varying data and system qualities and may not be able to participate the entire training process. Therefore, in this paper, a new federated learning framework is proposed to support asynchronous model fusion with clustering-based participant selection. The proposed framework aims to accommodate the ad-hoc nature of IoT devices, and at the same time avoiding low quality or even malicious data from its participants to ensure model convergence and performance. © 2022 IEEE.

关键词： Internet of things

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A Sensitivity Analysis of the DCP/Vardis Protocol for Coordinating Drone Swarms

A Sensitivity Analysis of the DCP/Vardis Protocol for Coordi...

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IEEE Vehicular Networking Conference (VNC)

作者： Andreas Willig Dept. of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

ISBN: (数字)9798331524371

ISBN: (纸本)9798331524388

The DCP/Vardis protocol stack is designed to support coordination and sensor data exchange tasks in ad-hoc type large-scale and multi-hop networks of drones (or drone swarms), by piggybacking coordination and sensor data onto frequently transmitted beacon packets that are present anyway to help with collision avoidance. The Vardis protocol introduces the concept of a variable, which is available to all nodes, and for which information about any modifying operation (create, update, delete) is disseminated globally using beacons. In this paper we study the sensitivity of Vardis application performance metrics related to reliability and delay to some of its key parameters. This sensitivity analysis allows to assess the relative impact of these parameters and to derive recommendations for setting their values.

关键词： Wireless communication Wireless sensor networks Protocols Sensitivity analysis Spread spectrum communication Ad hoc networks Delays Reliability Wireless fidelity Drones

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Heuristic approaches for hiding sensitive frequent itemsets in uncertain databases 5

Heuristic approaches for hiding sensitive frequent itemsets ...

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5th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Lefkir, Mira Nouioua, Farid Fournier-Viger, Phillipe University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj BBA Dept. of Computer Science Algeria Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350381450

Pattern mining is a core objective within data mining, involving the detection of frequent itemsets (collections of values) within databases. This process serves to extract valuable insights from the data, facilitating informed decision-making. In recent times, a range of algorithms has emerged to adapt this task to the realm of uncertain *** has a wide range of applications, such as analyzing medical data to identify correlated symptoms based on their existential probabilities. However, pattern mining algorithms also pose the risk of revealing sensitive information. As far as we know, there is no existing solution that addresses the inadvertent disclosure of sensitive itemsets when pattern mining algorithms are employed on uncertain databases. This paper addresses this problem by proposing three heuristic approaches, which ensure that sensitive expected frequent itemsets are not discovered while minimizing undesirable side effects for other itemsets. The first heuristic approach (called aggregate) deletes some transactions from an uncertain database. The second approach (named disaggregate) removes some selected items from each transaction. And the third approach (called the hybrid approach) combines the two previous approaches, and deletes some selected items from some selected transactions. An experimental evaluation is presented to assess and compare the effectiveness of the three approaches. © 2023 IEEE.

关键词： Data mining

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Enhancing SMS Spam Detection Using Deep Learning Models: A Comparative Study 1

Enhancing SMS Spam Detection Using Deep Learning Models: A C...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Singh Sikarwar, Shailendra Arivukkodi, R. Krishnane, Dhanya Sharma, Himanshu Namdeo, Avinash Jadon, Kaushiki Jiwaji University Dept. of Computer Science Gwalior India Meenakshi A.H. Educ. & Research Dept. of Computer Applications Chennai India Dhanalakshmi Srinivasan Dept. of CSE Coimbatore India Mangalayatan University Dept. of Computer Eng. & Applications Aligarh India Lingayas Vidyapeeth University Dept. of Mechanical Engineering Faridabad India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

The rise in Machine Learning algorithms for identifying SMS spam has become popular due to a significant surge in unwanted text messages. Detecting SMS spam holds crucial importance for several reasons. Firstly, it can inundate mobile phone users' message inboxes with irrelevant and unwanted messages, causing frustration and irritation. Secondly, SMS spam serves as a conduit for phishing scams, where scammers exploit fraudulent SMS messages to deceive users into divulging personal information or downloading malicious software. These scams can result in financial losses, identity theft, or other forms of fraud. Lastly, SMS spam can propagate malware or viruses, potentially compromising the security or functionality of the user's device. © 2024 IEEE.

关键词： Phishing

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Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware 40

Exploring Large Language Models for Semantic Analysis and Ca...

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40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Walton, Brandon J Khatun, Mst Eshita Ghawaly, James M Ali-Gombe, Aisha Louisiana State University Dept. of Computer Science and Engineering Baton Rouge United States

ISBN: (纸本)9798331532819

Malware analysis is a complex process of examining and evaluating malicious software's functionality, origin, and potential impact. This arduous process typically involves dissecting the software to understand its components, infection vector, propagation mechanism, and payload. Over the years, deep reverse engineering of malware has become increasingly tedious, mainly due to modern malicious codebases' fast evolution and sophistication. Essentially, analysts are tasked with identifying the elusive needle in the haystack within the complexities of zero-day malware, all while under tight time constraints. Thus, in this paper, we explore leveraging Large Language Models (LLMs) for semantic malware analysis to expedite the analysis of known and novel samples. Built on GPT-4o-mini model, MalParse is designed to augment malware analysis for Android through a hierarchical-Tiered summarization chain and strategic prompt engineering. Additionally, MalParse performs malware categorization, distinguishing potential malware from benign applications, thereby saving time during the malware reverse engineering process. Despite not being fine-Tuned for Android malware analysis, we demonstrate that through optimized and advanced prompt engineering MalParse can achieve up to 77% classification accuracy while providing highly robust summaries at functional, class, and package levels. In addition, leveraging the backward tracing of the summaries from package to function levels allowed us to pinpoint the precise code snippets responsible for malicious behavior. © 2024 IEEE.

关键词： Android malware

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Towards a Catalog of Prompt Patterns to Enhance the Discipline of Prompt engineering

Ada User Journal

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Ada User Journal 2023年第3期44卷 220-227页

作者： Schmidt, Douglas C. Spencer-Smith, Jesse Fu, Quchen White, Jules Dept. of Computer Science Vanderbilt University United States

The rapid advent of Large Language Models (LLMs), such as ChatGPT and Claude, is revolutionizing various fields, from education and healthcare to the engineering of reliable software systems. These LLMs operate through "prompts," which are natural language inputs that users employ to query and leverage the models’ capabilities. Given the novelty of LLMs, the understanding of how to effectively use prompts remains largely anecdotal, based on isolated use cases. This fragmented approach limits the reliability and utility of LLMs, especially when they are applied in mission-critical software environments. To harness the full potential of LLMs in such crucial contexts, therefore, we need a systematic, disciplined approach to "prompt engineering" that guides interactions with and evaluations of these LLMs. This paper provides several contributions to research on LLMs for reliable software systems. First, it provides a holistic perspective on the emerging discipline of prompt engineering. Second, it discusses the importance of codifying "prompt patterns" to provide a sound foundation for prompt engineering. Third, it provides examples of prompt patterns that improve human interaction with LLMs in the context of software engineering, as well as other domains. We conclude by summarizing ways in which prompt patterns play an essential role in providing the foundation for prompt engineering. © 2023, Ada-Europe. All rights reserved.

关键词： Query languages

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