Informatica最新文献

{"title":"A Model for Android Platform Malware Detection Utilizing Multiple Machine Learning Algorithms","authors":"Hussein Albazar, Hussein Abdel-Jaber, Muawya Naser, Arwa Hamid","doi":"10.31449/inf.v48i17.6543","DOIUrl":"https://doi.org/10.31449/inf.v48i17.6543","url":null,"abstract":"In today's technological landscape, the ubiquitous use of mobile devices underscores their critical importance in facilitating daily tasks and enabling a wide array of functionalities, from communication to commerce and entertainment. However, this widespread adoption also brings significant concerns regarding security and privacy, especially with the proliferation of mobile applications capable of accessing sensitive data without explicit user consent. The Android operating system, renowned for its openness and extensive app ecosystem, faces substantial security challenges due to its susceptibility to malware attacks. Malicious software, covertly embedded within seemingly legitimate apps, poses serious threats such as data theft, unauthorized access, and device compromise. This study focuses on using Random Forest, Extra Trees, Logistic Regression, Gradient Boosting, and Support Vector Machine algorithms to develop an Android-based platform for robust malware detection in applications. The research aims to evaluate and compare the performance of these algorithms in terms of accuracy, precision, recall, and F1-score. The results show that the Logistic Regression algorithm achieved the highest accuracy with 97.31%. Additionally, it seeks to benchmark these results against a prior model utilizing different machine learning algorithms, aiming to identify the most effective approach for mitigating Android malware threats. By advancing detection capabilities through sophisticated machine learning methodologies, this study contributes to ongoing efforts to safeguard mobile device users from evolving cybersecurity threats.","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"48 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.informatica.si/index.php/informatica/article/download/6543/3331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147920220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Animation Character Mouth Matching Model Considering Reinforcement Learning and Feature Extraction","authors":"Hang Zhao","doi":"10.31449/inf.v48i3.6187","DOIUrl":"https://doi.org/10.31449/inf.v48i3.6187","url":null,"abstract":"With the development of the times, animation production has become increasingly sophisticated, and mouth matching is one of the key points to ensure the vividness and realism of animated characters. Therefore, a study proposes an animated character mouth matching model that takes into account reinforcement learning and feature extraction. Using the Actor-Critic method in reinforcement learning, the extracted audio and facial features are used as input states to predict the next facial feature in the next time step. The experimental results show that the proposed model has an average accuracy of 95.61% and an F1 value of 97.13% on three databases. Meanwhile, the peak signal-to-noise ratio and structural similarity index are 41.77 and 0.93, respectively, which are better than their comparison methods. In addition, the study tested the error of mouth shape under different emotions, and the results showed an average mean square error of only 6.639. Finally, the user survey results showed that the animated characters generated by the proposed model received more recognition in mouth shape matching and realism, with a highest selection rate of 98.64%. The successful application of the proposed model provides new ideas and methods for research in related fields, laying the foundation for further promotion and innovation of animation production technology.","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"48 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.informatica.si/index.php/informatica/article/download/6187/3259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147903728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Quasi-Adjoint Graphs: On Polynomial-Time Solvable Cases of the Hamiltonian Cycle and Path Problems","authors":"Marta Kasprzak","doi":"10.15388/24-infor568","DOIUrl":"https://doi.org/10.15388/24-infor568","url":null,"abstract":"The Hamiltonian cycle and path problems are fundamental in graph theory and useful in modelling real-life problems. Research in this area is directed toward designing better and better algorithms for general problems, but also toward defining new special cases for which exact polynomial-time algorithms exist. In the paper, such new classes of digraphs are proposed. The classes include, among others, quasi-adjoint graphs, which are a superclass of adjoints, directed line graphs, and graphs modelling a DNA sequencing problem.\u0000PDF  XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"4 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confidential Transaction Balance Verification by the Net Using Non-Interactive Zero-Knowledge Proofs","authors":"Aušrys Kilčiauskas, Antanas Bendoraitis, Eligijus Sakalauskas","doi":"10.15388/24-infor564","DOIUrl":"https://doi.org/10.15388/24-infor564","url":null,"abstract":"One of the main trends for the monitoring and control of business processes is to implement these processes via private blockchain systems. These systems must ensure data privacy and verifiability for the entire network here denoted by ‘Net’. In addition, every business activity should be declared to a trusted third party (TTP), such as an Audit Authority (AA), for tax declaration and collection purposes.We present a solution for a confidential and verifiable realization of transactions based on the Unspent Transaction Output (UTxO) paradigm. This means that the total sum of transaction inputs (incomes) <span><span>$In$</span></span> must be equal to the total sum of transaction outputs (expenses) <span><span>$Ex$</span></span>, satisfying the balance equation <span><span>$In=Ex$</span></span>. Privacy in a private blockchain must be achieved through the encryption of actual transaction values. However, it is crucial that all participants in the network be able to verify the validity of the transaction balance equation. This poses a challenge with probabilistically encrypted data. Moreover, the inputs and outputs are encrypted with different public keys. With the introduction of the AA, the number of different public keys for encryption can be reduced to two. Incomes are encrypted with the Receiver’s public key and expenses with the AA’s public key.The novelty of our realization lies in taking additively-multiplicative, homomorphic ElGamal encryption and integrating it with a proposed paradigm of modified Schnorr identification providing a non-interactive zero-knowledge proof (NIZKP) using a cryptographically secure h-function. Introducing the AA as a structural element in a blockchain system based on the UTxO enables effective verification of encrypted transaction data for the Net. This is possible because the proposed NIZKP is able to prove the equivalency of two ciphertexts encrypted with two different public keys and different actors.This integration allows all users on the Net to check the UTxO-based transaction balance equation on encrypted data. The security considerations of the proposed solution are presented.\u0000PDF&nbsp;&nbsp;XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"61 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Offloaded Data Processing Energy Efficiency Evaluation","authors":"Victor Prokhorenko, Muhammad Ali Babar","doi":"10.15388/24-infor567","DOIUrl":"https://doi.org/10.15388/24-infor567","url":null,"abstract":"The growing popularity of mobile and cloud computing raises new challenges related to energy efficiency. This work evaluates four various SQL and NoSQL database solutions in terms of energy efficiency. Namely, Cassandra, MongoDB, Redis, and MySQL are taken into consideration. This study measures energy efficiency of the chosen data storage solutions on a selected set of physical and virtual computing nodes by leveraging Intel RAPL (Running Average Power Limit) technology. Various database usage scenarios are considered in this evaluation including both local usage and remote offloading. Different workloads are benchmarked through the use of YCSB (Yahoo! Cloud Serving Benchmark) tool. Extensive experimental results show that (i) Redis and MongoDB are more efficient in energy consumption under most usage scenarios, (ii) remote offloading saves energy if the network latency is low and destination CPU is significantly more powerful, and (iii) computationally weaker CPUs may sometimes demonstrate higher energy efficiency in terms of J/ops. An energy efficiency measurement framework is proposed in order to evaluate and compare different database solutions based on the obtained experimental results.\u0000PDF&nbsp;&nbsp;XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"67 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141770626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Improved Algorithm for Extracting Frequent Gradual Patterns","authors":"Edith Belise Kenmogne, Idriss Tetakouchom, Clémentin Tayou Djamegni, Roger Nkambou, Laurent Cabrel Tabueu Fotso","doi":"10.15388/24-infor566","DOIUrl":"https://doi.org/10.15388/24-infor566","url":null,"abstract":"Frequent gradual pattern extraction is an important problem in computer science widely studied by the data mining community. Such a pattern reflects a co-variation between attributes of a database. The applications of the extraction of the gradual patterns concern several fields, in particular, biology, finances, health and metrology. The algorithms for extracting these patterns are greedy in terms of memory and computational resources. This clearly poses the problem of improving their performance. This paper proposes a new approach for the extraction of gradual and frequent patterns based on the reduction of candidate generation and processing costs by exploiting frequent itemsets whose size is a power of two to generate all candidates. The analysis of the complexity, in terms of CPU time and memory usage, and the experiments show that the obtained algorithm outperforms the previous ones and confirms the interest of the proposed approach. It is sometimes at least 5 times faster than previous algorithms and requires at most half the memory.\u0000PDF&nbsp;&nbsp;XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"30 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141770625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demystifying the Stability and the Performance Aspects of CoCoSo Ranking Method under Uncertain Preferences","authors":"Sundararajan Dhruva, Raghunathan Krishankumar, Dragan Pamucar, Edmundas Kazimieras Zavadskas, Kattur Soundarapandian Ravichandran","doi":"10.15388/24-infor565","DOIUrl":"https://doi.org/10.15388/24-infor565","url":null,"abstract":"This paper attempts to demystify the stability of CoCoSo ranking method via a comprehensive simulation experiment. In the experiment, matrices of different dimensions are generated via Python with fuzzy data. Stability is investigated via adequacy and partial adequacy tests. The test passes if the ranking order does not change even after changes are made to entities, and the partial pass signifies that the top ranked alternative remains intact. Results infer that CoCoSo method has better stability with respect to change of alternatives compared to criteria; and CoCoSo method shows better stability with respect to partial adequacy test for criteria.\u0000PDF&nbsp;&nbsp;XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"15 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141770719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solution of Inverse Problem for Diffusion Equation with Fractional Derivatives Using Metaheuristic Optimization Algorithm","authors":"Rafał Brociek, Mateusz Goik, Jakub Miarka, Mariusz Pleszczyński, Christian Napoli","doi":"10.15388/24-infor563","DOIUrl":"https://doi.org/10.15388/24-infor563","url":null,"abstract":"The article focuses on the presentation and comparison of selected heuristic algorithms for solving the inverse problem for the anomalous diffusion model. Considered mathematical model consists of time-space fractional diffusion equation with initial boundary conditions. Those kind of models are used in modelling the phenomena of heat flow in porous materials. In the model, Caputo’s and Riemann-Liouville’s fractional derivatives were used. The inverse problem was based on identifying orders of the derivatives and recreating fractional boundary condition. Taking into consideration the fact that inverse problems of this kind are ill-conditioned, the problem should be considered as hard to solve. Therefore,to solve it, metaheuristic optimization algorithms popular in scientific literature were used and their performance were compared: Group Teaching Optimization Algorithm (GTOA), Equilibrium Optimizer (EO), Grey Wolf Optimizer (GWO), War Strategy Optimizer (WSO), Tuna Swarm Optimization (TSO), Ant Colony Optimization (ACO), Jellyfish Search (JS) and Artificial Bee Colony (ABC). This paper presents computational examples showing effectiveness of considered metaheuristic optimization algorithms in solving inverse problem for anomalous diffusion model.\u0000PDF&nbsp;&nbsp;XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"8 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141717762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of the Optimal Neural Network Architecture for Prediction of Bitcoin Return","authors":"Tea Šestanović, Tea Kalinić Milićević","doi":"10.15388/24-infor561","DOIUrl":"https://doi.org/10.15388/24-infor561","url":null,"abstract":"Neural networks (NNs) are well established and widely used in time series forecasting due to their frequent dominance over other linear and nonlinear models. Thus, this paper does not question their appropriateness in forecasting cryptocurrency prices; rather, it compares the most commonly used NNs, i.e. feedforward neural networks (FFNNs), long short-term memory (LSTM) and convolutional neural networks (CNNs). This paper contributes to the existing literature by defining the appropriate NN structure comparable across different NN architectures, which yields the optimal NN model for Bitcoin return forecasting. Moreover, by incorporating turbulent events such as COVID and war, this paper emerges as a stress test for NNs. Finally, inputs are carefully selected, mostly covering macroeconomic and market variables, as well as different attractiveness measures, the importance of which in cryptocurrency forecasting is tested. The main results indicate that all NNs perform the best in an environment of bullish market, where CNNs stand out as the optimal models for continuous dataset, and LSTMs emerge as optimal in direction forecasting. In the downturn periods, CNNs stand out as the best models. Additionally, Tweets, as an attractiveness measure, enabled the models to attain superior performance.\u0000PDF&nbsp;&nbsp;XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"32 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation","authors":"Francisco de Arriba-Pérez, Silvia García-Méndez, Fátima Leal, Benedita Malheiro, Juan C. Burguillo","doi":"10.15388/24-infor562","DOIUrl":"https://doi.org/10.15388/24-infor562","url":null,"abstract":"Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (<i>i</i>) incremental profiling, (<i>ii</i>) data drift detection &amp; adaptation, and (<i>iii</i>) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87% spam <i>F</i>-measure.\u0000PDF&nbsp;&nbsp;XML","PeriodicalId":56292,"journal":{"name":"Informatica","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141511309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}