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Multi-dimensional Hybrid Bayesian Belief Network Based Approach for APT Malware Detection in Various Systems 1

International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Sharma, Amit Gupta, Brij B. Singh, Awadhesh Kumar Saraswat, V.K. Department of Computer Engineering National Institute of Technology Kurukshetra Haryana Kurukshetra136119 India International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Lebanese American University Beirut1102 Lebanon NITI Aayog New Delhi110001 India

ISBN: (数字)9783031220180

ISBN: (纸本)9783031220173

We are living in a digital world where information flows in the form of bits. The world is witnessing rapid transformation in easy of living by utilizing smart devices ranging from Inter of Things (IoT), personal computing to advanced super computers for solving complex problems in domains like medical, transportation, banking and day-to-day living etc. So protecting the digital assets is an important aspect of modern world, which is susceptible to highly advanced Cyber attacks which have wide range of ramifications like financial loss, intellectual property loss and privacy violations etc. The modern Cyber adversaries are evolving with new tactics, techniques and procedures (TTP) for longtime strategic information gathering by neutralizing the victim’s security defenses at the perimeter and system level. The sophistication and complexity of the Advanced Persistent Threats (APT) necessitate developing a new range of security solutions with unconventional defense technologies. The APT payloads are equipped with highly evasive manoeuvers to evade modern security solutions which rely on signature and rule-based methodologies. In these scenarios the malware behavioral analysis assists in detecting APT payloads. However it generates huge amount of behavioral logs which require domain experts review to decide the samples nature which is time consuming, resource intensive and less scalable. We need an automation process to mimic human intelligence by analyzing the internal dependencies of the behavioral patterns to solve the classification problem. So we are proposing an unconventional Bayesian Belief Network-based approach to address the APT malware detection problem by extracting unique features over the malware sample’s static, dynamic, and event analysis. The proposed Threat Detection Bayesian Belief Network Model (TDBBNM) is a combination of three Bayesian models named Static Analysis Bayesian Belief Network (SABBN), Dynamic Analysis Bayesian Belief Network (DABBN) an

关键词： Bayesian networks

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Retweeting behavior prediction based on dynamic Bayesian network classifier in microblogging networks

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Applied Soft Computing 2024年 164卷

作者： Safari, Rahebeh Mojtahedi Rahmani, Amir Masoud Alizadeh, Sasan H. Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan Tehran Iran

Nowadays, predicting user retweeting behavior in microblogging networks has gained considerable attention. Solving this problem allows for understanding the underlying mechanism of information diffusion and analyzing diffusion's evolution concerning a particular original microblog over time. Time-series data from microblogging networks are ubiquitous, and user retweeting behavior prediction is primarily considered a classification task. Consequently, there is a significant demand for accurate classification of time-series data. Several influential factors affect user retweeting behavior prediction in microblogging networks. However, much of the existing research focuses on studies that neglect the impact of users' indirect social influence strength. This neglect leads to inaccurate forecasts of indirect retweeting behavior for a particular original microblog. To address this, this paper presents a new time-sensitive measure of social influence strength based on a combination of users' temporal behavior patterns and the shortest cascade path length. Second, it proposes a prediction model based on Dynamic Bayesian Network derivative classifiers called V-DBNC to accurately predict user retweeting behavior over time. The proposed time-sensitive social influence strength factor and other factors, such as individual behavior and similarity-based driving factors, are applied to the V-DBNC model to achieve accurate user retweeting behavior prediction in the microblogging network. The joint density of factors in this classifier is estimated using a multivariate Gaussian kernel function with smoothing parameters. The V-DBNC model is empirically optimized by splitting the smoothing parameters into different-scale intervals and using the model averaging method to select the optimal classifier. Additionally, the proposed model's relatively simple structure helps overcome the overfitting problem and allows it to accumulate classification information through iterative evolution, p

关键词： Classification (of information)

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Multi-Attribute Decision-Making for Intrusion Detection Systems: A Systematic Review

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International Journal of Information technology and Decision Making 2023年第1期22卷 589-636页

作者： Alamleh, Amneh Albahri, O.S. Zaidan, A.A. Alamoodi, A.H. Albahri, A.S. Zaidan, B.B. Qahtan, Sarah Binti Ismail, Amelia Ritahani Malik, R.Q. Baqer, M.J. Jasim, Ali Najm Al-Samarraay, Mohammed S. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim35900 Malaysia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Faculty of Engineering and IT The British University in Dubai United Arab Emirates Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Center Middle Technical University's Baghdad Iraq International Islamic University Malaysia Kuala Lumpur Malaysia Department of Artificial Intelligence Faculty of Information Technology Zarqa University Zarqa Jordan Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University College Hillah Iraq Foundation of Alshuhda Baghdad Iraq

Intrusion detection systems (IDSs) employ sophisticated security techniques to detect malicious activities on hosts and/or networks. IDSs have been utilized to ensure the security of computer and network systems. However, numerous evaluation and selection issues related to several cybersecurity aspects of IDSs were solved using a decision support approach. The approach most often utilized for decision support in this regard is multi-Attribute decision-making (MADM). MADM can aid in selecting the most optimal solution from a huge pool of available alternatives when the appropriate evaluation attributes are provided. The openness of the MADM methods in solving numerous cybersecurity issues makes it largely efficient for IDS applications. We must first understand the available solutions and gaps in this area of research to provide an insightful analysis of the combination of MADM techniques with IDS and support researchers. Therefore, this study conducts a systematic review to organize the research landscape into a consistent taxonomy. A total of 28 articles were considered for this taxonomy and were classified into three main categories: data analysis and detection (n=4), response selection (n=7) and IDS evaluation (n=17). Each category was thoroughly analyzed in terms of a variety of aspects, including the issues and challenges confronted, as well as the contributions of each study. Furthermore, the datasets, evaluation attributes, MADM methods, evaluation and validation and bibliography analysis used by the selected articles are discussed. In this study, we highlighted the existing perspective and opportunities for MADM in the IDS literature through a systematic review, providing researchers with a valuable reference. © 2023 World Scientific Publishing Company.

关键词： Taxonomies

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An Investigation Through Stochastic Procedures for Solving the Fractional Order computer Virus Propagation Mathematical Model with Kill Signals

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Neural Processing Letters 2023年第2期55卷 1783-1797页

作者： Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Mumtaz, Nadia Fathurrochman, Irwan Sadat, R. Ali, Mohamed R. Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Computer Science Iqra University Islamabad Campus Islamabad Karachi Pakistan Department of Islamic Educational Management Institute Agama Islam Negeri Curup Rejang Lebong Indonesia Department of Mathematics Zagazig Faculty of Engineering Zagazig University Zagazig Egypt Faculty of Engineering and Technology Future University Cairo Egypt

In this study, the numerical investigations through the stochastic procedures for solving a class of fractional order (FO) computer virus propagation (CVP) mathematical model with kill signals (KS), i.e., CVP-KS is presented. The KS gets alert about those viruses, which can be infected through the computer system to decrease the virus propagation danger. The mathematical model of the CVP-KS is based on the SEIR-KS model. The focus of these investigations is to present the numerical solutions of the FO-SEIR-KS model using the sense of Levenberg–Marquardt backpropagation scheme (LMBS) together with the neural networks (NNs), i.e., LMBS-NNs. The use of the one dynamic of the other makes the model nonlinear. Three different FO values have been used to check the performances of the designed scheme for this FO-SEIR-KS nonlinear mathematical model. The statics used in this study is 80%, 10% and 10% for training, testing and certification for solving the FO-SEIR-KS nonlinear mathematical model. The numerical simulations are performed through the stochastic LMBS-NNs scheme for solving the FO-SEIR-KS nonlinear mathematical model. The obtained results will be compared with the design of database reference solutions based on the Adams–Bashforth–Moulton. In order to accomplish the validity, capability, consistency, competence and accuracy of the LMBS-NNs, the numerical results using the error histograms, regression, mean square error, state transitions and correlation have been provided. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Mean square error

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Reveal training performance mystery between Tensor Flow and PyTorch in the single GPU environment

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Science China(Information Sciences) 2022年第1期65卷 147-163页

作者： Hulin DAI Xuan PENG Xuanhua SHI Ligang HE Qian XIONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabSchool of Computer Science and Technology Huazhong University of Science and Technology Department of Computer Science University of Warwick

Deep learning has gained tremendous success in various fields while training deep neural networks(DNNs) is very compute-intensive, which results in numerous deep learning frameworks that aim to offer better usability and higher performance to deep learning practitioners. Tensor Flow and Py Torch are the two most popular frameworks. Tensor Flow is more promising within the industry context, while Py Torch is more appealing in academia. However, these two frameworks differ much owing to the opposite design philosophy:static vs dynamic computation graph. Tensor Flow is regarded as being more performance-friendly as it has more opportunities to perform optimizations with the full view of the computation graph. However, there are also claims that Py Torch is faster than Tensor Flow sometimes, which confuses the end-users on the choice between them. In this paper, we carry out the analytical and experimental analysis to unravel the mystery of comparison in training speed on single-GPU between Tensor Flow and Py Torch. To ensure that our investigation is as comprehensive as possible, we carefully select seven popular neural networks, which cover computer vision, speech recognition, and natural language processing(NLP). The contributions of this work are two-fold. First, we conduct the detailed benchmarking experiments on Tensor Flow and Py Torch and analyze the reasons for their performance difference. This work provides the guidance for the end-users to choose between these two frameworks. Second, we identify some key factors that affect the performance,which can direct the end-users to write their models more efficiently.

关键词： deep learning performance comparison TensorFlow PyTorch

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High Quality Factor AlScN Lamb Wave Resonators Using NbN/Al Top Electrodes at Cryogenic Temperature 22

High Quality Factor AlScN Lamb Wave Resonators Using NbN/Al ...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Luo, Zhifang Shao, Shuai Dong, Peng Guo, Haowen Zou, Xinbo Li, Jun Lee, Chengkuo Wu, Tao School Of Information Science And Technology ShanghaiTech University China Shanghai Institute Of Microsystem And Information Technology Chinese Academy Of Sciences China University Of Chinese Academy Of Sciences China School Of Physical Science And Technology ShanghaiTech University China Shanghai Engineering Research Center Of Energy Efficient And Custom Ai Ic China Department Of Electrical & Computer Engineering National University Of Singapore Singapore Center For Intelligent Sensors And Mems National University Of Singapore Singapore

ISBN: (纸本)9784886864352

Coupling between phonons and qubits provides a solid quantum platform for information transmission and processing. We report the high quality factor (Qa) aluminum scandium nitride (AlScN) Lamb wave resonators (LWRs) at cryogenic temperature for quantum applications. Superconducting 60 nm NbN/30nm Al serves as top electrodes for ultra-low energy dissipation at around 11 K. Al0.84Sc0.16N provides a high piezoelectric constant for high electro-mechanical coupling (kt2) of resonators. Additionally, LWRs allow lithography-defined resonant frequency for flexible multi-frequency phonon sources. The fabricated LWR shows Qa over 6000, and resulting in a record figure-of-merit (FoM = kt2∗Qa) of 211 for AlScN resonators. © 2023 IEEJ.

关键词： AlScN Lamb wave resonator NbN quantum superconducting

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A Hybrid Model Combined with SVM and CNN for Community Content Classification 23

A Hybrid Model Combined with SVM and CNN for Community Conte...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ye, Yukui Xie, Xia Jin, Hai Wang, Duoqiang National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665494571

Community websites bring many conveniences to people, and the classification of community content is playing an important role in website management and information searching. As the carrier of community content, posts are difficult to classify manually. According to the characteristics of community content, a hybrid classification model for machine learning is proposed. This model consists of three steps. Firstly, aiming at the problem of fewer features of posts, a weighted word vector is proposed to enrich the features of posts. Secondly, since the single kernel function of SVM can not completely match all data distributions, a mixed kernel function is employed to improve the model. Finally, in order to fully utilize the powerful feature extraction ability of Convolutional Neural Network as well as the classification ability of SVM, a hybrid model is designed and implemented by replacing softmax layer with SVM classifier. The corresponding experiment results indicate that compared with traditional Convolutional Neural Network, the proposed hybrid model has better performance and stability with classification accuracy improved from 0.9% to 1.4% in general. © 2021 IEEE.

关键词： Support vector machines Smart cities Support vector machine classification Machine learning Feature extraction Stability analysis Data models

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A new 2-class unilateral upper limb motor imagery tasks for stroke rehabilitation training

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Medicine in Novel technology and Devices 2022年第1期13卷 65-70页

作者： Banghua Yang Jun Ma Wenzheng Qiu Yan Zhu Xia Meng School of Mechatronic Engineering and Automation School of MedicineResearch Center of Brain Computer EngineeringShanghai UniversityShanghai200444China Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of EducationShanghai201203China Shanghai Intelligent Engineering Technology Research Center for Addiction and rehabilitation Shanghai201901China The Second Rehabilitation Hospital of Shanghai Shanghai200441China China National Clinical Research Center for Neurological Diseases Beijing100070China

The rehabilitation training based on motor imagery brain-computer interface(MI-BCI)is considered to be an effective *** designed a new 2-class unilateral upper limb motor imagery *** from 15 healthy subjects and 10 stoke patients are collected in the *** results of event-related desynchronization/synchronization(ERD/ERS)and power spectral density(PSD)analysis showed the significant different features on health subjects and stroke *** improved 2-Conv-FBCNET is used to classify Electroencephalogram(EEG)signals and the accuracy is(health:61.0%,stroke:59.4%).The new two types of tasks provide a new training method for MI-BCI rehabilitation training system.

关键词： Stroke rehabilitation training Unilateral upper limb Motor imagery-brain computer interface(MIBCI)

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A Synchronous parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Forecasting

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Lasso Regression Model of HSV Color Space Masking Vision Based Whole Coffee Bean Processing Agtron Measurement

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Journal of Institute of Control, Robotics and Systems 2024年第11期30卷 1245-1253页

作者： Lee, Ye Chan Noh, Yi Hyun Noh, Yun Seo Kim, Ju Song Ryu, Kwangyeol Kim, Byeong Hak Department of Industrial Engineering Pusan National University Korea Republic of Department of Computer Science and Engineering Pusan National University Korea Republic of AI Research Center for Manufacturing Systems Korea Institute of Industrial Technology Korea Republic of

The rapid increase in coffee consumption has led to a significant expansion in production scale and variety within the agricultural regions of the global coffee belt. Recent coffee harvested in varies specious and processed in various ways, and the functionality of processing equipment is advanced to optimize its quality. In particular, coffee roasting equipment has evolved to incorporate complex structures and advanced electronic, mechanical, and heat/air control technologies for processing harvested green coffee beans. One of the most critical quality factors in the roasting process is the degree of roast, which is measured by the bean color (Agtron) value. The conventional method for Agtron measurement involves grinding the beans and using vision sensors. However, the conventional method shows difficult and time-consuming. The method for measuring the color of whole beans without grinding is simpler, but it has limitations in accuracy due to variations in shading and background images. This study proposes a method to accurately measure the Agtron color of whole coffee beans without grinding. The proposed method employs a preprocessing technique that separates the measurement area from the background area through color histogram analysis. To enhance measurement accuracy, we suggest using a Lasso regression model, which mitigates overfitting problems. Experimental results demonstrate that the proposed method achieves an improved accuracy compared with traditional regression methods and neural network-based approaches. © ICROS 2024.

关键词： Grinding (machining)

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