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DeepWare: Imaging Performance Counters With Deep Learning to Detect Ransomware

IEEE Transactions on computers 2023年第3期72卷 600-613页

作者： Ganfure, Gaddisa Olani Wu, Chun-Feng Chang, Yuan-Hao Shih, Wei-Kuan Dire Dawa University Institute of Technology School of Computing Department of Computer Science Dire Dawa1487 Ethiopia Harvard University Department of Computer Science Cambridge United States Academia Sinica Institute of Information Science Taipei115 Taiwan National Tsing Hua University Department of Computer Science Hsinchu City300 Taiwan

In the year passed, rarely a month passes without a ransomware incident being published in a newspaper or social media. In addition to the rise in the frequency of ransomware attacks, emerging attacks are very effective as they utilize sophisticated techniques to bypass existing organizational security perimeter. To tackle this issue, this paper presents 'DeepWare,' which is a ransomware detection model inspired by deep learning and hardware performance counter (HPC). Different from previous works aiming to check all HPC results returned from a single timing for every running process, DeepWare carries out a simple yet effective concept of 'imaging hardware performance counters with deep learning to detect ransomware,' so as to identify ransomware efficiently and effectively. To be more specific, DeepWare monitors the system-wide change in the distribution of HPC data. By imaging the HPC values and restructuring the conventional CNN model, DeepWare can address HPC's nondeterminism issue by extracting the event-specific and event-wise behavioral features, which allows it to distinguish the ransomware activity from the benign one effectively. The experiment results across ransomware families show that the proposed DeepWare is effective at detecting different classes of ransomware with the 98.6% recall score, which is 84.41%, 60.93%, and 21% improvement over RATAFIA, OC-SVM, and EGB models respectively. DeepWare achieves an average MCC score of 96.8% and nearly zero false-positive rates by using just a 100 ms snapshot of HPC data. This timeliness of DeepWare is critical on the ground that organizations and individuals have the opportunity to take countermeasures in the first stage of the attack. Besides, the experiment conducted on unseen ransomware families such as CoronaVirus, Ryuk, and Dharma demonstrates that DeepWare has excellent potential to be a useful tool for zero-day attack detection. © 1968-2012 IEEE.

关键词： Feature extraction

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A Novel Pressure Sensor with Composite Sensitive Film for High Precision Measurement at Broad Range 38

A Novel Pressure Sensor with Composite Sensitive Film for Hi...

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38th IEEE International Conference on Micro Electro Mechanical systems, MEMS 2025

作者： Chen, Xiaopeng Ling, Sijia Tong, Hanyang Xiao, Yujing Zhang, Jin Yu, Zhengyin Jin, Qinghui Ningbo University The Faculty of Electrical Engineering and Computer Science China Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology China

ISBN: (纸本)9798331508890

Microelectromechanical system (MEMS) based pressure sensors have been utilized for decades;however, new trends in pressure sensors have recently emerged, such as increased sensitivity, a broader range and reduced chip size. This paper presents a tiny 0.6mm×0.8mm piezoresistive pressure sensor that offers exceptional sensitivity (0.27mV/kPa/3.3V) at a wide range of pressures (0-1MPa). The innovative composite pressure-sensitive film of the sensor is composed of a 10μm "*"-shaped single-crystalline silicon structure and a remarkably thin thickness of only 2.5μm poly-silicon film. It is developed utilizing the thin-film under bulk-silicon technique. And simultaneous improvement of detection sensitivity and linearity is achieved by etching out the nonlinear deformation region of the film. © 2025 IEEE.

关键词： Pressure sensors

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An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

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computers, Materials & Continua 2022年第1期70卷 1721-1747页

作者： Sultan Noman Qasem Mohammed Al-Sarem Faisal Saeed Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaiz6803Yemen College of Computer Science and Engineering Taibah UniversityMedinaSaudi Arabia Information System Department Saba’a Region UniversityMareebYemen

Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health *** huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been *** COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social *** methods have been applied for detecting rumors and tracking their sources for COVID 19-related ***,very few studies have been conducted for this purpose for the Arabic language,which has unique ***,this paper proposes a comprehensive approach which includes two phases:detection and *** the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the *** experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.

关键词： Rumor detection rumor tracking similarity techniques COVID-19 social media analytics

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An efficient lung disease classification from X-RAY image using graph convolutional network and transformer

An efficient lung disease classification from X-RAY image us...

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2024 International Conference on Artificial Intelligence and Machine Learning Research, CAIMLR 2024

作者： Jiang, Zhihao Manshor, Noridayu Bt Chen, Limi Hainan Vocational University of Science and Technology Hainan China Faculty of Computer Science and Information Technology Universiti Putra Malaysia Malaysia

ISBN: (纸本)9781510691797

Lung diseases pose a significant global health challenge, requiring timely and accurate diagnosis for effective treatment. This research addresses the limitations of conventional diagnostic methods by proposing a novel approach for lung disease classification from X-ray images, utilizing the combined strengths of Graph Convolutional Network (GCN) and Transformers. Traditional machine learning models often struggle with the complexity and variability of medical imaging data, resulting in limited diagnostic accuracy. Our hybrid model leverages GCN to capture complex spatial relationships within lung tissues, while Transformers manage sequential data and long-range dependencies. This combined approach ensures superior feature extraction and analysis, robust handling of imaging variability, and seamless integration with clinical data. Our system aims to significantly improve diagnostic accuracy by reducing false positives and negatives, providing reliable support for radiologists. The model also incorporates a continuous learning mechanism, allowing it to evolve with new data and advancements in medical imaging technology. By integrating patient-specific factors such as demographics and clinical history, the model enhances the personalization and precision of diagnoses. Extensive validation on the Chest X-ray14 dataset demonstrates our method’s superiority over traditional models, showcasing its potential to transform lung disease diagnosis in clinical settings. © 2025 SPIE.

关键词： Lung cancer

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eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers 5

eRock at Qur’an QA 2022: Contemporary Deep Neural Networks ...

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5th Workshop Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection, OSACT 2022

作者： Aftab, Esha Malik, Muhammad Kamran Department of Computer Science Department of Information Technology Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (纸本)9791095546757

Question Answering (QA) has enticed the interest of NLP community in recent years. NLP enthusiasts are engineering new Models and fine-tuning the existing ones that can give out answers for the posed questions. The deep neural network models are found to perform exceptionally on QA tasks, but these models are also data intensive. For instance, BERT has outperformed many of its contemporary contenders on SQuAD dataset. In this work, we attempt at solving the closed domain reading comprehension Question Answering task on QRCD (Qur’anic Reading Comprehension Dataset) to extract an answer span from the provided passage, using BERT as a baseline model. We improved the model’s output by applying regularization techniques like weight-decay and data augmentation. Using different strategies we had 59% and 31% partial Reciprocal Ranking (pRR) on development and testing data splits respectively. © European Language Resources Association (ELRA).

关键词： Natural language processing systems

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Aspect Level Songs Rating Based Upon Reviews in English

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computers, Materials & Continua 2023年第2期74卷 2589-2605页

作者： Muhammad Aasim Qureshi Muhammad Asif Saira Anwar Umar Shaukat Atta-ur-Rahman Muhammad Adnan Khan Amir Mosavi Department of Computer Science Bahria University Lahore54000Pakistan Lahore Institute of Science and Technology Lahore 54792Pakistan Department of Multidisciplinary Engineering Texas A&M UniversityCollege Station77843USA Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam 31441Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

With the advancements in internet facilities,people are more inclined towards the use of online *** service providers shelve their items for *** users post their feedbacks,reviews,ratings,*** the use of the *** enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these *** Analysis(SA)is a technique that performs such decision *** research targets the ranking and rating through sentiment analysis of these reviews,on different *** a case study,Songs are opted to design and test the decision *** aspects of songs namely music,lyrics,song,voice and video are *** the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a *** machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are *** performed the best with 74.99%*** are validated using K-Fold.

关键词： Machine learning natural language processing songs reviews:sentiment analysis songs rating aspect level sentiment analysis reviews analysis text classification music

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A Machine Learning Based Automated Model for Managing Student Dropout 22

A Machine Learning Based Automated Model for Managing Studen...

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22nd IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2024

作者： Ghosh, Partha Charit, Arnab Banerjee, Hindol Bandhu, Debanwesa Ghosh, Agniv Pal, Ankita Goto, Takaaki Sen, Soumya Academy of Technology Department of Computer Science and Engineering Adisaptagram India Toyo University Faculty of Information Sciences and Arts Saitama Japan A.K.Choudhury School of Information Technology University of Calcutta Kolkata India

ISBN: (纸本)9798350391343

Addressing the persistent challenge of student dropout, particularly prevalent in developing countries like India, Bangladesh, etc. are of paramount importance. Factors such as poverty, natural calamities, and early marriages exacerbate this issue. High student dropout rates can negatively impact a country by diminishing its economic productivity, increasing social inequalities, and perpetuating a cycle of poverty. Addressing dropout issues requires comprehensive strategies to ensure a skilled and educated workforce, fostering societal well-being and global competitiveness. This research focuses on analysing comprehensive data on students who have dropped out. Thereafter, a machine learning based methodology is used to discern the underlying causes of student attrition in various schools. Furthermore, it allows for efficient monitoring of the state's educational landscape, with the ability to drill down to granular levels when necessary to identify specific regional challenges. The effectiveness of this approach is validated through the utilization of real-world datasets. © 2024 IEEE.

关键词： Students

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Secure Private Data Storage and File Sharing Using Homomorphic and Hybrid Hash-Based Cryptographic Technique in Cloud

Secure Private Data Storage and File Sharing Using Homomorph...

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International Conference on Computing and Machine Learning, CML 2024

作者： Senthil, G.A. Prabha, R. Monica, K.M. Priya, R. Mohana Kanna, R. Rajesh Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri SaiRam Institute of Technology Chennai India Department of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Salem India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (纸本)9789819765874

Data is a crucial asset in any business, and for some businesses, it ranks among the most valuable. More and more businesses want to keep their data on the cloud and securely and efficiently distribute it among their authorized staff. Data duplication across numerous local devices is one factor in the enormous amount of data that is expanding daily. Both databases and data are made available on a large scale through the Cloud. Storage infrastructure is employed in many different locations. Security concerns have prevented a broad adoption of the cloud computing architecture. This is because of the extensive use of the internet throughout the world;advancements in cryptography have become vital in addressing some of these security issues. Two-phase algorithms have been introduced for a protocol for safe data storage. The hybrid technique that has been presented seeks to boost the power of cloud storage by decreasing computation time and ciphertext size. In this project, users may store all sorts of files, establish an effective directory structure, and navigate it in a way that is remarkably comparable to any conventional Operating system, all in a cloud area. The system also demonstrates a method for secure sharing of personal digital content. Users may transfer files via various methods, including links and QR codes, all inside an easy-to-use single-page Angular UI, with enhanced security provided by AWS services. Our solution can offer some extra features like making lightweight sticky notes and sharing folders utilising a Nodejs server and a MongoDB in addition to these basic functions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud storage

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CannabisO: The Ontology-based Knowledge Model for Safe Cannabis Consumption in Thailand 20

CannabisO: The Ontology-based Knowledge Model for Safe Canna...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Samasutthi, Pranpreya Anutariya, Chutiporn School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand

ISBN: (纸本)9798350300505

In June 2022, the Ministry of Public Health of Thailand announced that cannabis can be used in healthcare, medical, research, and commerce. This announcement led to a wide discussion in social media, TV broadcasts, and public media about how to use cannabis safely, the symptoms that cannabis can relieve, side effects, and precautions of cannabis. Prior to the Ministry's announcement, cannabis was illegal and forbidden to consume since it was considered as a kind of drugs and was dangerous to consume, even privately. Most people still lack of proper knowledge about safe cannabis consumption. Also, information provided on the Internet and social media about cannabis is now often redundant, inconsistent, and unreliable. Therefore, this study gathers official and reliable published knowledge about cannabis consumption specifically for general users. CannabisO is modeled as a formal, shareable, and reusable ontology-based knowledge model to be used for a Q&A system that can help answer questions about safe edible consumption of cannabis, precautions and side effects of cannabis consumption, and lastly, symptoms that can be alleviated by cannabis. Several interesting Q&As are demonstrated in this paper. © 2023 IEEE.

关键词： Ontology

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A Mutual Authentication and Cross Verification Protocol for Securing Internet-of-Drones (IoD)

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computers, Materials & Continua 2022年第9期72卷 5845-5869页

作者： Saeed Ullah Jan Irshad Ahmed Abbasi Fahad Algarni Department of Computer Science&IT University of MalakandChakadara18800Pakistan Faculty of Science&Arts Belqarn Department of Computer ScienceUniversity of BishaSabtul Alaya 61985Saudi Arabia Faculty of Computing and Information Technology University of BishaBisha 67714Saudi Arabia

With the rapid miniaturization in sensor technology,Internet-ofDrones(IoD)has delighted researchers towards information transmission security among drones with the control station server(CSS).In IoD,the drone is different in shapes,sizes,characteristics,and *** can be classified on the purpose of its deployment,either in the civilian or military ***’s manufacturing,equipment installation,power supply,multi-rotor system,and embedded sensors are not issues for *** main thing is to utilize a drone for a complex and sensitive task using an infrastructureless/self-organization/resource-less network type called Flying Ad Hoc Network(FANET).Monitoring data transmission traffic,emergency and rescue operations,border surveillance,search and physical phenomenon sensing,and so on can be achieved by developing a robust mutual authentication and cross-verification scheme for IoD deployment civilian *** several protocols are available in the literature,they are either design issues or suffering from other vulnerabilities;still,no one claims with conviction about foolproof security ***,in this paper,the researchers highlighted the major deficits in prior protocols of the domain,i.e.,these protocols are either vulnerable to forgery,side channel,stolen-verifier attacks,or raised the outdated data transmission *** order to overcome these loopholes and provide a solution to the existing vulnerabilities,this paper proposed an improved and robust public key infrastructure(PKI)based authentication scheme for the IoD *** proposed protocol’s security analysis section has been conducted formally using BAN(Burrows-Abadi-Needham)logic,ProVerif2.03 simulation,and informally using discussion/pragmatic *** the performance analysis section of the paper has been assessed by considering storage,computation,and communication *** comparing the proposed protocol with prior works,it has been demonstrate

关键词： Cryptography authentication confidentiality reachability ZSP

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