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检索条件"任意字段=2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017"

共 2746 条记录，以下是81-90 订阅

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Multi-Agent Reinforcement learning Based on Cross Task Information Sharing 2

Multi-Agent Reinforcement Learning Based on Cross Task Infor...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Gao, Yu Zhu, Lizhong Liu, Yunting Yang, Jiaming Harbin First Machinery Group Corporation Harbin China Shenyang Ligong University Shenyang China

ISBN: (纸本)9798350368888

This article selects StarCraft as the experimental environment, which has a complex and vast action and state space, making it difficult for intelligent agents to make optimal decisions. In this article, the sharing of parameter experience values among multiple agents in a single task is used to enhance the collaboration ability between agents and avoid repetitive learning of basic strategies. In addition, after distilling and refining the core strategies of individual tasks, policy interaction is carried out in multiple tasks of the same type. By establishing a joint objective function for multiple tasks and alternating optimization for mutual constraints and updating iterations, information exchange between cross task multi-agent systems is completed, solving the problem of difficulty in obtaining effective data in complex scenarios and improving the learning efficiency of micro unit joint decision-making. This paper also adds qualification tracking to solve the problem of reward delay at the initial stage of model training, and uses course transfer learning to complete incremental joint training. © 2024 IEEE.

关键词： Adversarial machine learning

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Evaluation of Network Intrusion Detection with machine learning and Deep learning Using Ensemble Methods on CICIDS-2017 Dataset 5

Evaluation of Network Intrusion Detection with Machine Learn...

引用

5th IEEE international conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Rakesh, Lanka Upadhyay, Lav Reddy, Pochamreddy Mukesh Manipal University Jaipur Department Of Cse Jaipur India Manipal University Jaipur Department Of Cce Jaipur India

ISBN: (纸本)9798350330861

An intrusion detection system (IDS), also referred to as an IDS, is a type of network security equipment that monitors network communications in real-time and, should any potentially hostile transmissions be identified, either sends out alarms or implements active reaction measures. In this context, numerous researchers have attempted to improve intrusion detection performance by combining traditional machine learning models with alternative optimization techniques. Although the present intrusion detection model has the potential to considerably improve performance, there are still certain persistent problems, such as faulty detection and data preparation operations, which both have the potential to reduce accuracy. Using the CICIDS2017 dataset, we provided an analysis model of various classifiers in our paper, including random forest, stacking set-1 (Naive Bayes, K-Nearest Neighbours, Random Forest), bagging, boosting (XGB), LSTM, CNN, and stacking set-2 (LSTM and CNN). Boosting (XGB) performed better than all models across all feature sets overall. © 2023 IEEE.

关键词： Network security

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Harmonizing Deep learning and Ensemble Techniquesfor Network Traffic Anomaly Detection

Harmonizing Deep Learning and Ensemble Techniquesfor Network...

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2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kashyap, Ayush Kumar Mahapatra, Madhumita Yadav, Piyush Verma, Seema Department of Computer Science Delhi Technical Campus Greater Noida India Delhi technical Campus Greater Noida India

ISBN: (纸本)9798350355338

Keeping not only the security of the network infrastructure, but the innovativeness of the cyber threats in mind, is vital for the safety of the digital landscape today. This study aims to inspect which strategies are more applicable by combining deep learning with ensemble methods for elaboration of network traffic anomaly detection, especially dwelling on the most productive methodologies. Through employing the Train-Test-Evaluation scheme on the CICIDS 2017 dataset, we deployed advanced techniques including Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), Recurrent Neural Networks with LSTM as well as ensemble methods like AdaBoost, Gradient Boosting, Random Forest, Isolation Forest, and Voting. Evaluation metrics which measure performance of algorithms such as Accuracy, Precision, Recall, and F1 Score were used to compare the performance of these techniques. Analysis of our voting method gave us confidence in the described term. It generated the highest in detection accuracy, precision, and recall among others considered models. This work enhances the fields of network security with the presentation of the right detection techniques of the anomalies, the importance of the ensemble methods and the voting classifier in fighting cybercrimes on the network. © 2024 IEEE.

关键词： Adversarial machine learning

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Viability of machine learning in Android Scareware Detection 38th

Viability of Machine Learning in Android Scareware Detection

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38th international conference on Computers and Their Applications, CATA 2023

作者： Gautam, Aayush Rahimi, Nick School of Computing Sciences & Computer Engineering The University of Southern Mississippi USM United States

With the rapid increase in the use of mobile phones and other technologies, there has been a proportional growth in malware that tries to collect sensitive user data. Android is the most popular operating system for smartphones and has a great potential of becoming a target for malware threats. Scareware is a type of malware that tries to get users to provide valuable information or download malicious software through social engineering. This research aims to find out if machine learning is a viable option to prevent the consequences of scareware by accurately detecting them. In this investigation, four supervised machine learning (ML) algorithms were used in a dataset called CICAndMal2017 with 85 attributes for each of 11 Android scareware families and benign samples. We were able to achieve an accuracy of 96% which helped us to conclude that machine learning can and should be used to detect scareware. The machine learning models were then tested to calculate the accuracy for classifying each scareware family. © 2023, EasyChair. All rights reserved.

关键词： Android (operating system)

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machine learning Approaches for Anomaly Detection in Cybersecurity 12th

Machine Learning Approaches for Anomaly Detection in Cyberse...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Dhabliya, Ritika Sivakumar, S.A. Khubalkar, Deepti Sen, Pawan Banchhor, Chitrakant O. Jadhav, Jyoti Hindurao Researcher Connect Nagpur India Department of Electronics and Communication Engineering Dr. N. G. P. Institute of Technology Tamil Nadu Coimbatore641048 India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India VIIT Pune India Pune Institute of Computer Technology Pune India

ISBN: (纸本)9789819601424

Given the increasing complexity of cyber threats, it is crucial to have strong anomaly detection systems in place for cybersecurity. This study investigates the utilization of machine learning techniques, particularly an ensemble method, to improve the ability to detect anomalies. The ensemble method synergistically incorporates the capabilities of three separate algorithms: Isolation Forest, Support Vector machine (SVM), and Naive Bayes, resulting in a cybersecurity defense mechanism that is both resilient and precise. The study employs the CICIDS2017 dataset, which is a comprehensive and widely recognized benchmark in the field of cybersecurity. The dataset encompasses a wide range of network traffic scenarios, including both regular and malicious activities, making it an excellent resource for assessing the efficiency of anomaly detection algorithms. A rigorous preprocessing phase is utilized to guarantee the quality and pertinence of the data. The Isolation Forest algorithm, known for its efficient anomaly isolation capabilities, is incorporated into the ensemble to detect and capture the unique patterns displayed by malicious activities. The SVM classifier is utilized for its ability to define intricate decision boundaries, thereby improving the model’s ability to distinguish between normal and anomalous behavior. Naive Bayes, renowned for its simplicity and efficiency, enhances the ensemble by offering probabilistic insights into the probability of an instance being anomalous. The ensemble method is carefully designed to leverage the advantages of each individual algorithm, creating a collaborative and synergistic framework for detecting anomalies. The training process entails optimizing hyperparameters and fine-tuning model parameters to attain an optimal equilibrium between precision and recall. The experimental results showcase the effectiveness of the suggested ensemble technique, resulting in a remarkable accuracy of 99.21% on the CICIDS2017 dataset. The

关键词： Anomaly detection CICIDS2017 dataset Cybersecurity Ensemble method machine learning

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The State of Algorithmic Fairness in Mobile Human-Computer Interaction 25

The State of Algorithmic Fairness in Mobile Human-Computer I...

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25th ACM international conference on Mobile Human-Computer Interaction (MobileHCI)

作者： Yfantidou, Sofia Constantinides, Marios Spathis, Dimitris Vakali, Athena Quercia, Daniele Kawsar, Fahim Aristotle Univ Thessaloniki Thessaloniki Greece Nokia Bell Labs Cambridge England Univ Cambridge Cambridge England

ISBN: (纸本)9781450399241

This paper explores the intersection of Artificial Intelligence and machine learning (AI/ML) fairness and mobile human-computer interaction (MobileHCI). Through a comprehensive analysis of MobileHCI proceedings published between 2017 and 2022, we first aim to understand the current state of algorithmic fairness in the community. By manually analyzing 90 papers, we found that only a small portion (5%) thereof adheres to modern fairness reporting, such as analyses conditioned on demographic breakdowns. At the same time, the overwhelming majority draws its findings from highly-educated, employed, and Western populations. We situate these findings within recent efforts to capture the current state of algorithmic fairness in mobile and wearable computing, and envision that our results will serve as an open invitation to the design and development of fairer ubiquitous technologies.

关键词： machine learning bias fairness responsible artificial intelligence mobile computing

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A Survey: machine learning and Deep learning in Wheat Disease Detection and Classification 7

A Survey: Machine Learning and Deep Learning in Wheat Diseas...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Sahu, Nitin Bhat, Aruna Delhi Technological University Department of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350397253

Cereals are a significant and vital source of food for humans. To feed the world's expanding population, farmers must produce more crops. Plant diseases, however, have an impact on crop production and food quality. Wheat is among the most important crops consumed worldwide. Since Wheat Disease causes production loss, early disease detection and classification are absolutely essential. This research study has conducted a literature survey on studies published from the year 2017 to 2022 and summarized the three main types of Wheat Disease (fungal, bacterial, and insects), Wheat Disease datasets, and the current state of the art from the last six years of research. This research study has examined 24 studies on disease identification and classification by using various machine learning (ML) and Deep learning (DL) algorithms. The proposed research analysis shows that the majority of the literature on Wheat Disease focused on fungal disease, and the majority of the datasets used were self-acquired. Many State-of-the-art models have already produced excellent results, and many more need to be developed. © 2023 IEEE.

关键词： Deep learning

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Distributed Denial of Service Attack Detection by machine learning Techniques 22

Distributed Denial of Service Attack Detection by Machine Le...

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22nd international learning and Technology conference, L and T 2025

作者： Jewani, Varkha K. Ajmire, Prafulla E. Atique, Mohd. Sharma, Bhisham Dhaou, Imed Ben Awadhesh Chaurasia, Suhashini Sant Gadge Baba Amravati University Amravati India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia RTM Nagpur University Department of Computer Science Nagpur India

ISBN: (纸本)9798331532215

The exponential expansion of computer networks and the internet makes it clear that there is a chance of being attacked and harmed. In the meantime, one of the most crucial defensive measures against the more complex and frequent network attacks is the intrusion detection and prevention systems (IDSs and IPSs). Due to insufficient datasets, anomaly-based research in intrusion detection systems is plagued by imprecise deployment, analysis, and evaluation. The researchers analyzed a variety of datasets, including DARPA98, KDD99, ISC2012, and ADFA13, to assess how well their suggested intrusion detection and intrusion prevention techniques performed. There are several issues: lack of adequate attack coverage, unrepresentative payloads, and anonymised packet information, insufficient traffic volume and diversity, or a deficiency in feature set and metadata. This study is focused on CICIDS-2017 which is the recently updated IDS dataset that meets real-world requirements and is accessible publicly. In addition to benign network flows and distributed denial of service (DDoS) attacks, it contains seven common attack network flows as well. To provide the optimal combination of features for the identification of the attack category, it also assesses the efficacy of several machine learning (ML) algorithms and network traffic feature sets. Even though various ML techniques like Linear Regression, Random Forests, and Decision Tree can be used, and performed very well, Hybrid Algorithm is designed to get better analysis on dataset. © 2025 IEEE.

关键词： Network intrusion

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Ensemble Two Stage machine learning for Network Abnormal Detection 23

Ensemble Two Stage Machine Learning for Network Abnormal Det...

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15th international conference on machine learning and computing, ICMLC 2023

作者： Du, Runze Li, Runzhi Zhang, Zijiao School of Cyberspace Security Zhengzhou University Zhengzhou China Network Center Zhengzhou University Zhengzhou China

ISBN: (纸本)9781450398411

With the endless emergence of network security problems, it also brings the corresponding security threats to the society. IDS is an effective means to deal with network threats, but in the modern large-scale network environment, the traditional IDS has a high false positive rate. Researchers apply machine learning method to intrusion detection and get good results. In this work, based on the network traffic features, we proposed an ensemble two stage machine learning method CNN-CatBoost to detect network attacking behavior. To solve imbalance problem between normal and anomaly traffic, we construct a CNN model with multi-scale convolutional adopted to extract relations among traffic features for anomaly traffic recognition. Next for a multi-class classification problem, we use classic CatBoost model to identify different attacking types. In the experiments, datasets is from CICIDS2017. We deploy comparison experiments among proposed method and other methods. The experimental results show that CNN-CatBoost outperforms others in performance. Lastly, we give feature analysis by SHAP, understand how features influence on the model. © 2023 ACM.

关键词： machine learning

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Edge computing based IDS Detecting Threats using machine learning and PyCaret

Edge Computing based IDS Detecting Threats using Machine Lea...

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2023 international conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Kumar, Amit Kumar, Sanjeev Kumar, Vivek Kumari, Amrita Saini, Ashish Gupta, Sarthak Haridwar India G L Bajaj Institute of Technology and Management Greater Noida India Quantum University Roorkee India Vivek College of Education Bijnor India

ISBN: (纸本)9798350323917

The Internet of Things is getting connected with the edge computing network day by day. These devices generate large amounts of confidential data that is stored and processed on edge computing. There is a need to protect such data and edge computing networks from new types of attacks. Therefore, intrusion detection system (IDS) is an effective defense to secure edge computing network. In this study, an IDS based on machine learning is designed and developed. This article elaborates the technique of Recursive Feature Elimination (RFE) using the Random Forest algorithm (RF). The contribution of this paper is to select a relevant and efficient feature set using the recently developed Python library PyCaret. This paper proposes a novel approach for detecting new types of attacks in network traffic. The recently released dataset CICIDS2017 has been used to evaluate the proposed method, which includes state-of-the-art attacks. In this paper, two classification techniques are also used to build the IDS model. The first is a binary classification technique using the recently developed PyCaret library, and the second is a multiclass classification technique using RFE. © 2023 IEEE.

关键词： Edge computing

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