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2024 IEEE International Conference on Internet of Things and Intelligence systems, IoTaIS 2024

作者： Pecson, Jeff Nereo G. Tan, Wilson M. Festin, Cedric Angelo M. Dulva, Philip Angelo Y. Networks and Distributed Systems Group Department of Computer Science University of the Philippines - Diliman Metro Manila Philippines

ISBN: (纸本)9798331521219

Current methods in mapping the availability of WiFi networks, such as crowdsourcing platforms (e.g. Project BASS and CoverageMap) and dedicated wardriving, face limitations in terms of data recency, volume, and cost-effectiveness. Due to the downsides of both these methods, opportunistic wardriving is proposed which utilizes public utility vehicles (PUVs). This study investigates the feasibility of PUVs as potential wardriving vehicles for opportunistic coverage mapping. This method is cost-efficient and removes the problem of data recency because of its continuous daily trips. The findings suggest that opportunistic wardriving with PUVs is viable for wardriving with its capability to detect a high volume of access points (APs). Multiple runs within the University of the Philippines Diliman campus showed the PUVs’ effectiveness in detecting a significant number of APs, with higher success rates at closer distances. Comparatively, warwalking detected fewer APs, but there was a significant overlap between the methods. The study also highlighted significant differences in detected AP types, with wardriving finding more mobile hotspots and miscellaneous devices than warwalking. Overall, the results underline the effectiveness of PUVs in providing extensive WiFi coverage data and further enhancements to the detection system could optimize the approach. ©2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Hotspotter: An Incentivized Crowdsensing System for WiFi and Cellular Network Coverage Visualization 29

Hotspotter: An Incentivized Crowdsensing System for WiFi and...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Ortiz, Sebastian Luis S. Enriquez, Justin Gabriel R. Tan, Wilson M. Festin, Cedric Angelo M. University of the Philippines - Diliman Networks and Distributed Systems Laboratory Department of Computer Science Metro Manila Philippines

ISBN: (纸本)9798331517816

Reliable internet access is a key enabler for economic growth. Although the Philippine government launched initiatives to improve connectivity, connection speeds remained below the global average, especially for mobile networks. This paper presents Hotspotter, a system that aims to aid implementers and stakeholders in quantifying and addressing network coverage issues. Hotspotter is an incentivized crowdsensing system that collects, maps, and visualizes WiFi and cellular data to pinpoint hotspots and dead zones for the effective deployment and relocation of WiFi access points. A mobile application for Android was developed to facilitate data collection on geolocation, nearby WiFi access points, and connected cellular networks. The user interface visualizes the aggregated data in a hexagon-grid map. Fieldwork was conducted in two sitios of Barangay San Lorenzo, Norzagaray, Bulacan to stress-test the Hotspotter system in a Geographically Isolated and Disadvantaged Area (GIDA). It was found that 2G and 4G had the widest coverage and strongest signals overall, with modal signal strengths of 4.0 and 3.0, respectively. Being at the cutting edge, 5G was not yet supported. In the end, the mobile application's passive sensing, collecting, and caching of data successfully operated even in the most isolated areas without an internet connection. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Comparative Study of Static Analysis and Machine Learning Approaches for Detecting Android Banking Malware 3

Comparative Study of Static Analysis and Machine Learning Ap...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ahmed, Alaa M. Saeed, Mogeeb A. Hamood, Amr A. Alazab, Abduljabar A. Ahmed, Kamel A. Computer Networks & Distributed Systems Al-saeed It Taiz University Faculty of Engineering Taiz Yemen Taiz University Al-saeed Faculty of Engineering & It Computer Networks & Distributed Systems Department Taiz Yemen

ISBN: (纸本)9798350305333

Android currently dominates the smartphone market, accounting for an impressive market share of over 70%. However, because of its widespread acceptance, mobile operating systems have become a prime target for bad actors looking to profit from them. Particularly Android has been subjected to an increasing barrage of malware assaults, including the infamous Android Banking Trojans. This study investigates the effectiveness of static analysis in locating Android banking malware in order to counter this threat. It does so by utilizing a wide range of features, including permissions, application programming interface (API) calls, opcodes, API packages, system commands, intents, strings, services, receivers, and activities. The study suggests using machine learning techniques to assess the detection of Android malware by utilizing various sets of classifiers in order to achieve this goal. The study also uses a feature selection approach to determine which features are most useful for telling malicious code apart from good code. 500 samples of malicious code and 500 samples of benign code make up the dataset that was used. The XGboost algorithm outperforms others in terms of accuracy, achieving an impressive accuracy value of 99.5% in malware detection after conducting a thorough comparison of various classifier sets. These results demonstrate the potential of static analysis and machine learning as useful tools in fending off the growing threats posed by Android malware. © 2023 IEEE.

关键词： Android malware

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EloMetrics: Advanced Outcome Prediction for Chess Matches with Elo Ratings and Logistic Regression

EloMetrics: Advanced Outcome Prediction for Chess Matches wi...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Reyes, Ma Julianna Re-An D.G. Dicreto, Eirnan Santos, Emmanuel Gabriel D. Limbag, Daniella Franxene P. Sampedro, Gabriel Avelino College of Computer Studies De La Salle University Manila Philippines Networks and Distributed Systems Laboratory University of the Philippines Diliman Quezon City Philippines

ISBN: (纸本)9798331510756

Chessis a complex game characterized by diverse strategies and time constraints, making quick decision-making essential for success. While Elo ratings are widely recognized as indicators of player skill, the predictability of match outcomes based solely on these ratings remains a challenge. The study aims to develop a model for accurately predicting the outcome of chess games using logistic regression, focusing on Elo rating differences and the number of moves in each game. The dataset includes over 20,000 games from ***, and only games with decisive outcomes (excluding draws) were used. The research categorizes Elo ratings into various classes and evaluates model performance across these ranges. The model achieves a predictive accuracy of 68.18%, demonstrating the significance of Elo ratings and move counts in determining game results. Performance metrics, including precision, recall, and Fl-score, further validate the model's effectiveness. The study concludes that while Elo ratings and move count are strong predictors of chess outcomes, further refinement is needed to improve performance at high-ranking skill levels. The model performs well for experts but loses accuracy in master-level games, reflecting higher skill-level complexities. The insights gained from this research contribute to a deeper understanding of predictive modeling in chess, sug-gesting potential avenues for further investigation into additional Influencing factors. © 2025 IEEE.

关键词： Logistic regression

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Business Intelligence Model for Customer Targeting Using Fuzzy-C-Means and FP-Growth 3

Business Intelligence Model for Customer Targeting Using Fuz...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ahmed Mohammed, Sam Abdulilah Saeed, Mogeeb A. Software Engineering Taiz University Taiz Yemen Computer Networks and Distributed Systems Taiz University Taiz Yemen

ISBN: (纸本)9798350305333

In the era of business, business intelligence (BI) technology can inform the decision-maker of the right data and insight. In this study, a business intelligence model is built to help the marketing department build an efficient promotion strategy. This paper aims to understand the impact of segmenting customers using the soft clustering technique (fuzz-c-means) based on their spending behavior or Recency, Frequency, and Monetary Model (RFM model) on product affiliation or market basket analysis to develop more efficient promotions to target customers. This aim is approached first by, downloading a dataset called online retail from Kaggle, exploring data to remove the missing value and detect and remove outliers, and constructing an RFM model (Recency, frequency, and monetary). Second, the clustering algorithm is performed fuzzy-c-means, to segment customers into clusters with similar characteristics. Third, product affiliation or association rules are obtained as per cluster using the FP-growth algorithm. Eventually, spending behavior is concluded to show a significant impact on product affiliation which means that clustering based on spending behavior increases the efficiency of market basket analysis (MBA). © 2023 IEEE.

关键词： Decision making

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Int-BEB: Enhanced Binary Exponential Backoff using Q-learning 5

Int-BEB: Enhanced Binary Exponential Backoff using Q-learnin...

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

作者： Zerguine, N. Ferhat Abbas Setif 1 University Networks and Distributed Systems Laboratory Department of Computer Science Setif Algeria

ISBN: (纸本)9798350381450

Because of their development and success, MANETs are now preferred alternatives in many fields of study and application. The IEEE 802.11 standard is the basis of most wireless technologies. To treat collisions in wireless networks, IEEE 802.11 distributed Coordination Function (DCF) MAC layer employs Binary Exponential Backoff (BEB) algorithm, that is helpful in decreasing the potential of collision, but comes at the expense of several network performance metrics, such as medium access delay, bandwidth management, power use, and fairness. A Deep Learning technique called Deep Reinforcement Learning (DRL) allows an agent to communicate with the environment in order to accomplish a goal. In the present paper, we suggest Q-learning (QL), one of the DRL approaches to improve the Binary Exponential Backoff (BEB) in MANETs. The intelligently designed Int-BEB aims for better CW selection;this selection strikes to reduce collisions as much as possible and to give more chances to nodes that make collisions to access the channel by taking into consideration their residual energy. In-depth simulations are performed to evaluate the performance of the proposed mechanism. The performance of Int-BEB is evaluated in diverse networks with several criteria such as packet successful transmission, packet loss and energy. © 2023 IEEE.

关键词： Reinforcement learning

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Utilization of Database Spatial Queries for Region of Interest Extraction in Stored Images

Utilization of Database Spatial Queries for Region of Intere...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Valdez, Julian Troy C. Tan, Wilson M. University of the Philippines Diliman Department of Computer Science Networks and Distributed Systems Group Quezon City Philippines

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Image processing, specifically extracting regions of interest, being a costly operation would benefit from a method of storing its output region and input image data in a database for future querying so as to prevent re-execution of the processing operation. This study investigates the performance of using built-in spatial queries found in a couple of modern Database systems to store and retrieve specific regions of image data. After testing two widely used Database software: MongoDB and Postgresql, results have shown that MongoDB performs better in terms of average and tail latency as well as efficiency in disk space consumption. © 2022 IEEE.

关键词： Query processing

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Hybrid-MELAu: A Hybrid Mixing Engineered Linguistic Features Framework Based on Autoencoder for Social Bot Detection

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Informatica (Slovenia) 2022年第6期46卷 143-158页

作者： Hamida, Zineb Ferhat Refoufi, Allaoua Drif, Ahlem Giordano, Silvia Networks and Distributed Systems Laboratory Department of Computer Science University of Sétif 1 Sétif Algeria Networking Lab SUPSI University of Applied Sciences of Southern Switzerland Lugano Switzerland

Social bots are defined as computer algorithms that generate massive amounts of obnoxious or meaningful information. Most bot detection methods leverage multitudinous characteristics, from network features, temporal dynamics features, activities features, and sentiment features. However, there has been fairly lower work exploring lexicon measurement and linguistic indicators to detect bots. The main purpose of this research is to recognize the social bots through their writing style. Thus, we carried out an exploratory study on the effectiveness of only a set of linguistic features (17 features) exploitable for bot detection, without the need to resort to other types of features. And we develop a novel framework in a hybrid fashion of Mixing Engineered Linguistic features based on Autoencoders (Hybrid-MELAu). The semi-supervised Hybrid-MELAu framework is composed of two essential constituents: the features learner and the predictors. We establish the features learner innovated on two powerful structures: a) the first is a Deep dense Autoencoder fed by the Lexical and the Syntactic content (DALS) that represents the high order lexical and syntactic features in latent space, b) the second one is a Glove-BiLSTM autoencoder, which sculpts the semantic features;subsequently, we generate elite elements from the pre-trained encoder part from each latent space with transfer learning. We consider a sample of 1 Million from Cresci datasets to conduct our linguistic analysis comparison between the writing style of humans and bots. With this dataset, we observe that the bot's textual lexical diversity median is greater than the human one and the syntactic analysis based on speech-tagging shows a creative behavior in human writing style. Finally, we test the model's robustness on several public dataset (celebrity, pronbots-2019, and political bots). The proposed framework achieves a good accuracy of 92.22%. Overall, the results shown in this paper, and the related discussion, ar

关键词： Classification (of information)

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WifiGO: Assessment of PUV Viability for Opportunistic Coverage Mapping of WiFi networks

WifiGO: Assessment of PUV Viability for Opportunistic Covera...

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IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)

作者： Jeff Nereo G. Pecson Wilson M. Tan Cedric Angelo M. Festin Philip Angelo Y. Dulva Department of Computer Science Networks and Distributed Systems Group University of the Philippines - Diliman Metro Manila Philippines

ISBN: (数字)9798331521219

ISBN: (纸本)9798331521226

关键词： Accuracy Limiting Refining Interference WiMAX Hardware Object recognition Printers Transient analysis Wireless fidelity

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Enhancing MANETs Security Against Black Hole Attacks through Time-Based Node Analysis in AOMDV Protocol

Enhancing MANETs Security Against Black Hole Attacks through...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdullah A. Naji Mogeeb A. Saeed Ammar H. Almagashi Alaa M. Ahmed Hashem S. Al-Atefi Mohammed N. Khaled Olayan M. Rashed Computer Network & Distributed Systems Department Al-Saeed Faculty Of Engineering &IT Taiz University Computer Networks & Distributed Systems Al-Saeed Faculty Of Engineering &IT Taiz University Computer Center & Information Technology Taiz University

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Mobile ad-hoc networks (MANETs) rely solely on direct node communication and function without a fixed infrastructure. Due to their decentralized design, they are susceptible to numerous security risks, with black hole attacks posing significant threats to routing integrity and confidentiality. This paper proposes a technique to enhance the Ad hoc On-Demand Multi-path Distance Vector (AOMDV) routing protocol's security against black hole attacks. The method involves a time-based reply message analysis to identify and mitigate malicious nodes. This algorithm improves the protocol's ability to detect and prevent black hole attacks by closely analyzing response time differences between malicious and normal nodes. The results of the simulation show that the suggested solution greatly enhances network performance by lowering the Routing Overhead Ratio from 1388.04% to 0.73% and the Packet Loss Rate from 74% to 11% while keeping a high Packet Delivery Ratio—up to 93% in different scenarios. These enhancements sustain strong data transmission in dynamic MANET environments, supporting network integrity and reliability.

关键词： Protocols Packet loss Routing Ad hoc networks Vectors Security Time factors

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