2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)最新文献

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On Running Disabled Networking Features: A Taxonomy with Security Implications 关于运行被禁用的网络功能：具有安全含义的分类标准

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10427438

Dtv Ramakrishna Rao, Manjul Khandelwal

引用次数: 0

Enhancing Cooperative Spectrum Sensing in Cognitive Radio Systems: Mitigating Byzantine Attacks with a Weighted Algorithm 增强认知无线电系统中的合作频谱传感：用加权算法缓解拜占庭攻击

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10427318

Ankit Chouhan, Ashok Parmar, Kamal M. Captain, Pawan Maurya, Jignesh Patel

{"title":"Enhancing Cooperative Spectrum Sensing in Cognitive Radio Systems: Mitigating Byzantine Attacks with a Weighted Algorithm","authors":"Ankit Chouhan, Ashok Parmar, Kamal M. Captain, Pawan Maurya, Jignesh Patel","doi":"10.1109/COMSNETS59351.2024.10427318","DOIUrl":"https://doi.org/10.1109/COMSNETS59351.2024.10427318","url":null,"abstract":"Cooperative spectrum sensing (CSS) is a key approach in cognitive radio (CR) systems for dealing with fading, shadowing, and concealed node problems. CSS improves detection performance by utilizing the spatial range that results from the cooperative secondary users (CSUs). As part of centralized CSS, these CSUs collaborate to share information with a fusion center (FC), which makes global decisions. However, malicious users (MUs) can significantly decrease the sensing operation's accuracy. The crucial problem of Byzantine attacks is addressed in this paper through a weighted algorithm for MU detection in CSS environments. The proposed weighted algorithm efficiently detects and eliminates the effects of MU. A comprehensive analysis utilizes simulations of how well the proposed algorithm performs. The results are provided in a series of plots that show how superior the proposed algorithm is in terms of its resistance to Byzantine attacks and its capacity to increase CSS's overall dependability in the cognitive radio network (CRN).","PeriodicalId":518748,"journal":{"name":"2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)","volume":"116 1-3","pages":"465-469"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532742","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

Anomaly Detection in IoT Networks Based on Intelligent Security Event Correlation 基于智能安全事件关联的物联网网络异常检测

2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS) Pub Date : 2024-01-03 DOI: 10.1109/COMSNETS59351.2024.10426939

Igor V. Kotenko, Diana Levshun

{"title":"Anomaly Detection in IoT Networks Based on Intelligent Security Event Correlation","authors":"Igor V. Kotenko, Diana Levshun","doi":"10.1109/COMSNETS59351.2024.10426939","DOIUrl":"https://doi.org/10.1109/COMSNETS59351.2024.10426939","url":null,"abstract":"Modern Internet of Things networks combine many devices and sensors that transmit and process large amounts of data. Security tools identify security events that contain information about detected system or network states. In turn, high-performance data anomaly detection methods are required to ensure stability and reliability of work processes. Information about the correlation of identified security events can be used to detect and explain deviations from normal states. This study proposes an anomaly detection approach based on the causal correlation of security events using machine learning. The proposed approach does not require prior knowledge of event scenarios. Using cluster analysis and a convolutional recurrent neural network, we construct a security state correlation graph corresponding to the normal behavior of the system. Cluster analysis determines the similarity of events to each other. A convolutional LSTM, analyzes the spatio-temporal relationship of events. Using the identified event correlation thresholds, we look for anomalies in real time. Experimental results on an Internet of Things sensor dataset show that the proposed method is efficient in anomaly detection tasks.","PeriodicalId":518748,"journal":{"name":"2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS)","volume":"294 5","pages":"816-824"},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140532823","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

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