Proceedings of the 10th International Conference on Networking, Systems and Security最新文献

Unveiling Key Predictors for Early Heart Attack Detection using Machine Learning and Explainable AI Technique with LIME 与 LIME 合作，利用机器学习和可解释人工智能技术揭示早期心脏病发作检测的关键预测指标

Proceedings of the 10th International Conference on Networking, Systems and Security Pub Date : 2023-12-21 DOI: 10.1145/3629188.3629193

Prakash Paudel, S. K. Karna, Ruby Saud, L. Regmi, Tara Bahadur Thapa, Mohan Bhandari

{"title":"Unveiling Key Predictors for Early Heart Attack Detection using Machine Learning and Explainable AI Technique with LIME","authors":"Prakash Paudel, S. K. Karna, Ruby Saud, L. Regmi, Tara Bahadur Thapa, Mohan Bhandari","doi":"10.1145/3629188.3629193","DOIUrl":"https://doi.org/10.1145/3629188.3629193","url":null,"abstract":"The prominence of cardiovascular diseases, particularly heart attacks, as a leading cause of global mortality is highlighted, with an increasing number of deaths attributed to cardiovascular diseases over the years. Amidst these challenges, artificial intelligence (AI) and machine learning (ML) technologies emerge as powerful tools in healthcare. This study conducts a comparative analysis of predictive features extracted from diverse classification algorithms, including AdaBoost Classifier (ABC), Random Forest (RF), Gradient Boosting Classifier(GBC) and Light Gradient-Boosting Machine (LGBM), aiming to identify common patterns in predictive outcomes. LGBM emerges as the standout performer among classification algorithms, boasting a remarkable average training accuracy of 99.33%. Results demonstrate comparable precision, recall, and F1 scores among RF, GB, and LGBM, while ABC lags behind. The study reveals from eXplainable AI technique that consistent attribution of importance to attributes like \"kcm\" and \"troponin\" across all methods for classifying \"Attack\" instances, indicating their pivotal role in prediction. The research underscores the potential clinical application of machine learning for heart attack diagnosis and suggests the adoption of various deep learning techniques to enhance predictive performance.","PeriodicalId":508572,"journal":{"name":"Proceedings of the 10th International Conference on Networking, Systems and Security","volume":"27 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139166600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IT-SBA: An Improved Timer-based Scalable Broadcast Algorithm for Wireless Ad-hoc Networks IT-SBA：适用于无线 Ad-hoc 网络的基于定时器的改进型可扩展广播算法

Proceedings of the 10th International Conference on Networking, Systems and Security Pub Date : 2023-12-21 DOI: 10.1145/3629188.3629190

Md. Abu Bakar Siddik, Ashikur Rahman

引用次数: 0

Sociala: An Incentivized Decentralized Social Media for writers based on Blockchain using modified Delegated Proof of Stake Sociala：基于区块链的激励型去中心化作家社交媒体，使用修改后的委托证明股权方式

Proceedings of the 10th International Conference on Networking, Systems and Security Pub Date : 2023-12-21 DOI: 10.1145/3629188.3629198

Shahid Hasan, Md. Ahsan Habib, Avishek Roy, Abu Taher Md Shifat

{"title":"Sociala: An Incentivized Decentralized Social Media for writers based on Blockchain using modified Delegated Proof of Stake","authors":"Shahid Hasan, Md. Ahsan Habib, Avishek Roy, Abu Taher Md Shifat","doi":"10.1145/3629188.3629198","DOIUrl":"https://doi.org/10.1145/3629188.3629198","url":null,"abstract":"Over the past few years, there has been a profound evolution in decentralization methodologies, owing largely to the emergence of novel decentralized technologies like blockchain. In the realm of online social media, a paradigm shift has been suggested with the introduction of blockchain-based online social media (BOSM), heralding a potential future for social media platforms wherein users are duly rewarded for their valuable contributions. Despite the widespread adoption of these platforms by millions of users, it is crucial to acknowledge that their decentralization remains incomplete. One prevailing issue within existing blockchain systems resides in the vulnerability of the Delegated Proof of Stake (DPoS) consensus algorithm, which is susceptible to manipulation by the few selected nodes. Anticipating the demands of the forthcoming generation of social media, the pivotal imperative lies in establishing decentralization as its foundational hallmark. In this paper, we present Sociala, an incentivized blockchain-based framework tailored for writers. To effectively address the pitfalls of centralization and minimize the likelihood of nefarious node selection, we proffer an enhanced multi-step DPoS consensus algorithm, which is referred as modified DPoS (mDPoS). This advanced algorithm embraces both the tenets of randomness and diversity, coupled with the inclusion of input from general users, culminating in fortified system stability.","PeriodicalId":508572,"journal":{"name":"Proceedings of the 10th International Conference on Networking, Systems and Security","volume":"62 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139167467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving MaxSAT-Reordered Plans via Block Deordering 通过区块排序改进 MaxSAT 排序计划

Proceedings of the 10th International Conference on Networking, Systems and Security Pub Date : 2023-12-21 DOI: 10.1145/3629188.3629200

Sabah Binte Noor, Fazlul Hasan Siddiqui

{"title":"Improving MaxSAT-Reordered Plans via Block Deordering","authors":"Sabah Binte Noor, Fazlul Hasan Siddiqui","doi":"10.1145/3629188.3629200","DOIUrl":"https://doi.org/10.1145/3629188.3629200","url":null,"abstract":"Partial-order plans (POPs) provide greater flexibility during execution compared to sequential plans due to their least commitment nature. Optimizing flexibility in a POP involves strategies such as plan deordering, which eliminates unnecessary action orderings, and plan reordering which modifies action orderings arbitrarily. Though traditional plan deordering techniques, such as EOG (explanation-based order generalization), can efficiently find partial orderings in polynomial time, they lack optimality guarantees. This limitation prompts MaxSAT reorderings to encode the optimization of a POP’s orderings as a partial weighted MaxSAT problem. To further elevate the flexibility of the MaxSAT solutions, this work introduces an algorithm that employs block deordering, a distinct form of plan deordering that consolidates coherent actions into blocks, on top of MaxSAT reorderings. Our experiments with benchmark problems from the International Planning Competitions demonstrate that our algorithm not only makes significant enhancements to the satisfiable MaxSAT reordered plans but also takes it a step further by improving the optimal reordered plans in mere seconds.","PeriodicalId":508572,"journal":{"name":"Proceedings of the 10th International Conference on Networking, Systems and Security","volume":"55 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139166483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Learning Based Semi-Supervised Network Intrusion Detection System Robust to Adversarial Attacks 基于深度学习的半监督网络入侵检测系统可抵御对抗性攻击

Proceedings of the 10th International Conference on Networking, Systems and Security Pub Date : 2023-12-21 DOI: 10.1145/3629188.3629189

Syed Md. Mukit Rashid, Md. Toufikuzzaman, Md. Shohrab Hossain

{"title":"A Deep Learning Based Semi-Supervised Network Intrusion Detection System Robust to Adversarial Attacks","authors":"Syed Md. Mukit Rashid, Md. Toufikuzzaman, Md. Shohrab Hossain","doi":"10.1145/3629188.3629189","DOIUrl":"https://doi.org/10.1145/3629188.3629189","url":null,"abstract":"Network intrusion detection systems (NIDS) are used to detect abnormal behavior in network traffic, which is vital for secure communication. Recently, deep learning based solutions have been adopted for NIDS which suffer from two main problems. Most of them are based on supervised learning and cannot utilize the information that can be obtained from unlabeled data. Also, deep learning based methods are shown to be vulnerable to adversarial attacks. In this paper, we propose a novel semi-supervised and adversarially robust deep learning based approach which can utilize both labeled and unlabeled training samples. Our IDS first performs K-Means clustering to soft label part of the unlabeled data and then obtain a decision tree based on labeled and soft labeled samples. It then pretrains an autoencoder based multi-layer perceptron and later learns separate multi-layer perceptrons on each individual leaf of the decision tree. Our results show that the performance of our system is comparable to state-of-the art supervised learning approaches and outperforms existing state-of-the-art semi-supervised NIDS. Furthermore, we have extensively tested the adversarial robustness of our method using the popular blackbox Fast Gradient Sign Method (FGSM) and Generative Adversarial Network based IDSGAN approaches. Comparisons with other state-of-the-art NIDS baselines show that our proposed mechanism provides significantly higher adversarial detection rates, proving the robustness of our system to adversarial attacks.","PeriodicalId":508572,"journal":{"name":"Proceedings of the 10th International Conference on Networking, Systems and Security","volume":"45 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139166529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Soft-Touch or Hard-Touch: Quantifying Touches through A New System Focusing on A Pool Touchpad for Measuring Timing of Swimmers 软触摸还是硬触摸？通过新系统量化触摸，重点关注用于测量游泳者计时的泳池触摸板

Proceedings of the 10th International Conference on Networking, Systems and Security Pub Date : 2023-12-21 DOI: 10.1145/3629188.3629194

Adnan Quaium, Md. Ashraful Islam, Mohammad Sohel Rahman, A. A. A. Islam

{"title":"Soft-Touch or Hard-Touch: Quantifying Touches through A New System Focusing on A Pool Touchpad for Measuring Timing of Swimmers","authors":"Adnan Quaium, Md. Ashraful Islam, Mohammad Sohel Rahman, A. A. A. Islam","doi":"10.1145/3629188.3629194","DOIUrl":"https://doi.org/10.1145/3629188.3629194","url":null,"abstract":"The swimming turn or finishing presents an integral part of competitive swimming, and the time it takes by a swimmer to push off the wall is a significant factor in their ratings. Therefore, it is essential to have a reliable way to measure the timing of touch, i.e., wall-contact, over the finishing or turning points. To do so, swimming pool touchpads are widely used in swimming competitions to overcome human error in measurements by enabling touch-sensing systems at the finishing or turning points. The touch-sensing devices use their sensitive touch sensors to record time with millisecond accuracy, providing athletes with the most precise measurements possible. However, these devices might need a minimal force applied from the swimmers to sense the touch, and thus, to record the timing of the touch. As such, insufficient force applied on the touchpad during swimming could cause skipping the time log. As a result, it is critical to have a trustworthy method for measuring the extent of touch or wall-contact exerted over the touch-sensing systems. This study, for the first time in the literature to the best of our knowledge, proposes such a method for measuring the extent of touch or wall-contact for swimming pool touchpads. In the process of proposing the method, we design and develop a bi-modal sensing module to distinguish the soft-touch and hard-touch exerted over a real pool touchpad used in a swimming pool organizing national and international competitions. Our experiments show that the pool touchpad under experimentation is not always accurate in registering touches. It sometimes fails to register low-force touches while generally registering touches with high force.","PeriodicalId":508572,"journal":{"name":"Proceedings of the 10th International Conference on Networking, Systems and Security","volume":"4 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139166701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy and Latency-aware Computation Load Distribution of Hybrid Split and Federated Learning on IoT Devices 物联网设备上混合拆分和联合学习的能量和延迟感知计算负载分布

Proceedings of the 10th International Conference on Networking, Systems and Security Pub Date : 2023-12-21 DOI: 10.1145/3629188.3629201

Sakhaouth Hossan, Farhan Mahmud, P. Roy, M. Razzaque, Md. Mustafizur Rahman

{"title":"Energy and Latency-aware Computation Load Distribution of Hybrid Split and Federated Learning on IoT Devices","authors":"Sakhaouth Hossan, Farhan Mahmud, P. Roy, M. Razzaque, Md. Mustafizur Rahman","doi":"10.1145/3629188.3629201","DOIUrl":"https://doi.org/10.1145/3629188.3629201","url":null,"abstract":"Split learning (SL) and Federated Learning (FL) are popular distributed learning frameworks used to increase data privacy and reduce computation loads of Internet of Things (IoT) devices. However, one of the major challenges of distributed learning on IoT devices is determining the portion of computation load to be assigned for the devices compared to the server side. The contributions of the existing works in the literature are either limited by consideration of homogeneous resources available at all IoT devices or by not distributing computation loads among the devices in an efficient way. In this paper, we propose an adaptive clustering-based computation load distribution method for IoT devices, with heterogeneous resource capacities, participating in the model training. The clustering makes the optimal determination of the split point of the learning model, which is scalable even for a large number of devices. The numerical evaluation of the proposed learning model implemented using Python 3.0 and the comparative performance results show that the proposed load distribution policy for the learning models reduces the time by 160 times on average compared to the usual brute force method.","PeriodicalId":508572,"journal":{"name":"Proceedings of the 10th International Conference on Networking, Systems and Security","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139166754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Delay and Cost Aware Adaptive Deployment and Migration of Service Function Chains in 5G 5G 中服务功能链的延迟和成本感知自适应部署和迁移

Proceedings of the 10th International Conference on Networking, Systems and Security Pub Date : 2023-12-21 DOI: 10.1145/3629188.3629196

Joy Munshi, Sumaya Sultana, Md. Jahid Hassan, P. Roy, M. Razzaque, Syed Ahsanul Kabir

引用次数: 0

Random Data Poisoning Attacks on Nonlinear Regression Learning 非线性回归学习的随机数据中毒攻击

Proceedings of the 10th International Conference on Networking, Systems and Security Pub Date : 2023-12-21 DOI: 10.1145/3629188.3629199

Md. Nazmul Hasan Sakib, A. B. M. A. Al Islam

{"title":"Random Data Poisoning Attacks on Nonlinear Regression Learning","authors":"Md. Nazmul Hasan Sakib, A. B. M. A. Al Islam","doi":"10.1145/3629188.3629199","DOIUrl":"https://doi.org/10.1145/3629188.3629199","url":null,"abstract":"Nonlinear regression has numerous applications in diverse fields, including biology, economics, engineering, and more, where it is used to model and analyze complex relationships between variables that cannot be adequately represented by linear models. However, these models are susceptible to malicious attacks that manipulate input data to yield false results. This study focuses on unpredictable data poisoning threats based on randomization in nonlinear regression learning and assesses the iTrim defense mechanism’s efficacy. Multiple nonlinear regression datasets and common techniques were used in experiments. Random Data poisoning attack involves regenerating data points with altered labels and inserting them into the training set. The polluted dataset underwent iTrim defense, and model performance on a test set gauged effectiveness. Results show that models suffer significant performance degradation when exposed to random data poisoning attacks. Malicious points cause overfitting and poor test set generalization. This study underscores nonlinear regression models’ vulnerability to random data poisoning and the need for robust security measures, while iTrim offers some protection, further research is vital to develop more potent defense systems against complex attacks.","PeriodicalId":508572,"journal":{"name":"Proceedings of the 10th International Conference on Networking, Systems and Security","volume":"21 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139167416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disparity-Aware Federated Learning for Intrusion Detection Systems in Imbalanced Non-IID Settings 在不平衡的非入侵检测设置中，针对入侵检测系统的差异感知联合学习

Proceedings of the 10th International Conference on Networking, Systems and Security Pub Date : 2023-12-21 DOI: 10.1145/3629188.3629197

Md Mohaiminul Islam, A. A. A. Islam

{"title":"Disparity-Aware Federated Learning for Intrusion Detection Systems in Imbalanced Non-IID Settings","authors":"Md Mohaiminul Islam, A. A. A. Islam","doi":"10.1145/3629188.3629197","DOIUrl":"https://doi.org/10.1145/3629188.3629197","url":null,"abstract":"Many variants of Federated Learning have been proposed to settle different challenges that come with numerous practical applications, one of which is dealing with non-IID data sources. As decentralized data sources in real life are bound to be non-IID, this is one of the hardest challenges, and yet the earliest federated algorithms struggle to resolve this issue, resulting in worse non-IID performance. Also, applications that require capturing really intricate insights from data while upholding the latest data privacy standards, such as Intrusion Detection Systems (IDS) have enabled the use of FL in those domains. In this article, we propose a novel Disparity-Aware federated learning approach that tackles non-IID and data imbalance from both global and local learning steps of FL. Our method capitalizes on state-of-the-art loss functions to tackle data imbalance at the client level and a class distribution-dependent clustering algorithm at the server to tackle class distribution skew. The nature of the process renders it applicable even in asynchronous federated learning schemes. Experiments with multiple benchmark intrusion detection datasets reveal improved performance over traditional deep learning approaches as well as earlier federated learning techniques.","PeriodicalId":508572,"journal":{"name":"Proceedings of the 10th International Conference on Networking, Systems and Security","volume":"60 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139167947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0