Computer Networks最新文献_第9页

Malware communication in smart factories: A network traffic data set 智能工厂中的恶意软件通信：网络流量数据集

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-09 DOI: 10.1016/j.comnet.2024.110804

Bernhard Brenner , Joachim Fabini , Magnus Offermanns , Sabrina Semper , Tanja Zseby

{"title":"Malware communication in smart factories: A network traffic data set","authors":"Bernhard Brenner , Joachim Fabini , Magnus Offermanns , Sabrina Semper , Tanja Zseby","doi":"10.1016/j.comnet.2024.110804","DOIUrl":"10.1016/j.comnet.2024.110804","url":null,"abstract":"<div><div>Machine learning-based intrusion detection requires suitable and realistic data sets for training and testing. However, data sets that originate from real networks are rare. Network data is considered privacy sensitive and the purposeful introduction of malicious traffic is usually not possible. In this paper we introduce a labeled data set captured at a smart factory located in Vienna, Austria during normal operation and during penetration tests with different attack types. The data set consists of 173 GB of Packet Capture (PCAP) files, which represent 16 days (395 h) of factory operation. It includes Message Queuing Telemetry Transport (MQTT), OPC Unified Architecture (OPC UA), and Modbus/TCP traffic. The captured malicious traffic was originated by a professional penetration tester who performed two types of attacks: (a) aggressive attacks that are easier to detect and (b) stealthy attacks that are harder to detect. Our data set includes the raw PCAP files and extracted flow data. Labels for packets and flows indicate whether packets (or flows) originated from a specific attack or from benign communication. We describe the methodology for creating the data set, conduct an analysis of the data and provide detailed information about the recorded traffic itself. The data set is freely available to support reproducible research and the comparability of results in the area of intrusion detection in industrial networks.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110804"},"PeriodicalIF":4.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Local Differential Privacy for correlated location data release in ITS 在智能交通系统中发布相关位置数据的本地差分保密功能

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-09 DOI: 10.1016/j.comnet.2024.110830

Kah Meng Chong , Amizah Malip

{"title":"Local Differential Privacy for correlated location data release in ITS","authors":"Kah Meng Chong , Amizah Malip","doi":"10.1016/j.comnet.2024.110830","DOIUrl":"10.1016/j.comnet.2024.110830","url":null,"abstract":"<div><div>The ubiquity of location positioning devices has facilitated the implementation of various Intelligent Transportation System (ITS) applications that generate an enormous volume of location data. Recently, Local Differential Privacy (LDP) has been proposed as a rigorous privacy framework that permits the continuous release of aggregate location statistics without relying on a trusted data curator. However, the conventional LDP was built upon the assumption of independent data, which may not be suitable for inherently correlated location data. This paper investigates the quantification of potential privacy leakage in a correlated location data release scenario under a local setting, which has not been addressed in the literature. Our analysis shows that the privacy guarantee of LDP could be degraded in the presence of spatial–temporal and user correlations, albeit the perturbation is performed locally and independently by the users. This privacy guarantee is bounded by a privacy barrier that is affected by the intensity of correlations. We derive several important closed-form expressions and design efficient algorithms to compute such privacy leakage in a correlated location data. We subsequently propose a <span><math><mi>Δ</mi></math></span>-CLDP model that enhances the conventional LDP by incorporating the data correlations, and design a generic LDP data release framework that renders adaptive personalization of privacy preservation. Extensive theoretical analyses and simulations on scalable real datasets validate the security and performance efficiency of our work.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110830"},"PeriodicalIF":4.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks WAOA：用于无线传感器网络高能效路由选择的鲸蚂蚁混合优化算法

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-09 DOI: 10.1016/j.comnet.2024.110845

Navneet Kumar , Karan Singh , Jaime Lloret

{"title":"WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks","authors":"Navneet Kumar , Karan Singh , Jaime Lloret","doi":"10.1016/j.comnet.2024.110845","DOIUrl":"10.1016/j.comnet.2024.110845","url":null,"abstract":"<div><div>Wireless Sensor Networks (WSNs) are vital for collecting data from remote environments. Nevertheless, the limited energy resources of sensor nodes render energy-efficient routing a critical concern for the successful operation of WSNs. To address these concerns, clustering, and routing are essential tasks in WSNs; clustering aims to organize sensor nodes into groups or clusters to minimize energy usage and prolong the network's lifespan. On the other hand, routing involves determining the optimum paths for transmitting data from the source nodes to the destination nodes. Nonetheless, it has been established that the current energy-efficient routing problem is an NP-hard, requiring a trade-off between energy and overall network performance. In this paper, we proposed a Hybrid Whale-Ant Optimization Algorithm (WAOA) for energy-efficient routing in WSNs. The proposed WAOA utilizes the Whale Optimization Algorithm (WOA) to find the suitable cluster head in the predefined search space, while the Ant Colony Optimization (ACO) searches the optimal route from the source cluster sensors to the cluster head within its predefined space. Linear programming construction is employed to formulate optimization problems for cluster head selection and search for the optimal route. The performance analysis demonstrates that the proposed WAOA performs better than MOORP, MMABC, and AZEBR by 5.78 %,16.11 %, and 18.52 %, respectively, in terms of network lifetime.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110845"},"PeriodicalIF":4.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization 无人机安装的 IRS 辅助无线供电移动边缘计算系统：联合波束成形设计、资源分配和位置优化

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-09 DOI: 10.1016/j.comnet.2024.110846

Majid Hadi, Reza Ghazizadeh

{"title":"UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization","authors":"Majid Hadi, Reza Ghazizadeh","doi":"10.1016/j.comnet.2024.110846","DOIUrl":"10.1016/j.comnet.2024.110846","url":null,"abstract":"<div><div>Intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have been recently used in wireless-powered mobile edge computing (MEC) systems to enhance the computation bits and energy harvesting performance. However, in the conventional IRS- and UAV-aided MEC systems, the IRS is installed at fixed locations on a building, which restricts the computation performance. UAV-mounted IRS (UAV-IRS), as a promising technology, combines the advantages of UAV and IRS. Hence, in this work, we study a UAV-IRS wireless-powered MEC system, where multiple UAV-IRSs are considered between Internet of Things (IoT) devices and the base station to improve the computation bits and energy harvesting. The multi-antenna base station first charges the IoT devices via radio frequency signals, and then IoT devices offload their computation tasks to the base station via UAV-IRSs. We formulate a computation bits maximization problem for all IoT devices by jointly determining detection beamforming at IoT devices, active energy beamforming at the base station, power allocation, time slot assignment, CPU frequency, the phase shifts design in the wireless energy transfer (WET) and task offloading, and UAV-IRSs positions. A block coordinate descent (BCD) algorithm by decomposing the introduced problem into four blocks is proposed, while the detection beamforming, active energy beamforming, transmit power, time slot assignment, CPU frequency, and the phase shifts design in the task offloading are derived in closed-form results. Also, the successive convex approximation and semidefinite relaxation (SDR) are adopted to obtain the UAV-IRS positions and the phase shifts in the WET, respectively. The simulation results verify the effectiveness of the presented BCD method compared with the different benchmark schemes.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110846"},"PeriodicalIF":4.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated deep learning models for detecting RPL attacks on large-scale hybrid IoT networks 用于检测大规模混合物联网网络 RPL 攻击的联合深度学习模型

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-06 DOI: 10.1016/j.comnet.2024.110837

Mohammed Albishari , Mingchu Li , Majid Ayoubi , Ala Alsanabani , Jiyu Tian

{"title":"Federated deep learning models for detecting RPL attacks on large-scale hybrid IoT networks","authors":"Mohammed Albishari , Mingchu Li , Majid Ayoubi , Ala Alsanabani , Jiyu Tian","doi":"10.1016/j.comnet.2024.110837","DOIUrl":"10.1016/j.comnet.2024.110837","url":null,"abstract":"<div><div>With the rapid spread of the Internet of Things (IoT), smart applications and services become increasingly crucial, making them an easily accessible source of personally identifiable information. Over the last few years, the use of machine learning in securing routing layers, particularly routing protocol for low-power and lossy networks (RPL), has become fundamental in ensuring successful routing and privacy preservation as a crucial consideration among edge nodes. In recent works, training of collected data on a central server has increased concerns regarding data privacy. Consequently, decentralized learning is currently a solution for privacy preservation. It has gained popularity in IoT networks in which the models are trained on hybrid data located in edge nodes and enable global decision-making without sharing global data, causing high communication costs during weight updates. We propose a federated learning of routing protocol (Fed-RPL)-based gated recurrent unit (GRU) model for decentralized training rounds and quantization method (Q-8bit) to decrease the number of weight updates that can significantly mitigate the communication overhead and maintain the local model with high accuracy. Meanwhile, the ensemble unit aggregates the updates and selects the best local model to enhance the global model accuracy. Our experiments show that Fed-RPL outperforms classical machine learning (ML) methods in privacy-preserving edge data, significantly reduces the communication cost in non-IID scenarios, and achieves higher detection accuracy than recent FL approaches.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110837"},"PeriodicalIF":4.4,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NeSt: A QoS differentiating end-to-end networked storage simulator NeSt：服务质量差异化端到端网络存储模拟器

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-05 DOI: 10.1016/j.comnet.2024.110833

Jit Gupta, Sourav Das, Krishna Kant

引用次数: 0

MC-Det: Multi-channel representation fusion for malicious domain name detection MC-Det：多通道表示融合用于恶意域名检测

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-05 DOI: 10.1016/j.comnet.2024.110847

Yabo Wang , Ruizhi Xiao , Jiakun Sun , Shuyuan Jin

{"title":"MC-Det: Multi-channel representation fusion for malicious domain name detection","authors":"Yabo Wang , Ruizhi Xiao , Jiakun Sun , Shuyuan Jin","doi":"10.1016/j.comnet.2024.110847","DOIUrl":"10.1016/j.comnet.2024.110847","url":null,"abstract":"<div><div>As the essential fundamental infrastructure of the current network, the Domain Name System is widely abused by cyber attackers, malicious domain detection has become a crucial task in combating cyber crime. Most existing methods focus on local attributes, treating each domain name individually. Alternatively, they prioritize global associations among domain names, but ignore the attributes of the domains themselves, allowing malicious domain names to survive through sophisticated evasion techniques. In this paper, we propose MC-Det, a hybrid framework for detecting malicious domain names by fusing a Multi-channel representation of domain names. MC-Det first abstracts the domain name resolution process into three spatially independent information channels: Attribute space, which contains the intrinsic information in the domain name string itself, Constraint space, which involves the potential constraints imposed on the network activity behind the domain name, Topological space, which represents the actual usage and deployment of the domain name. Subsequently, it generates proper embedding representations of domain names for each channel. This novel Multi-channel representation provides a comprehensive understanding of domain name resolution process. Finally, a Multi-channel fusion strategy employing by attention mechanism is used to generate the final representation of domain names for the classifier, making MC-Det suitable for malicious domain name detection in different application scenarios. Experimental results demonstrate that MC-Det outperforms other state-of-the-art techniques, while only utilizing the resource information revealed in the domain name resolution phase.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110847"},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Themis: A passive-active hybrid framework with in-network intelligence for lightweight failure localization Themis：具有网络内智能的被动-主动混合框架，用于轻量级故障定位

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-05 DOI: 10.1016/j.comnet.2024.110836

Jingyu Xiao , Qing Li , Dan Zhao , Xudong Zuo , Wenxin Tang , Yong Jiang

{"title":"Themis: A passive-active hybrid framework with in-network intelligence for lightweight failure localization","authors":"Jingyu Xiao , Qing Li , Dan Zhao , Xudong Zuo , Wenxin Tang , Yong Jiang","doi":"10.1016/j.comnet.2024.110836","DOIUrl":"10.1016/j.comnet.2024.110836","url":null,"abstract":"<div><div>The fast and efficient failure detection and localization is essential for stable network transmission. Unfortunately, existing schemes suffer from a few drawbacks such as significant resource consumption, lack of support for fast online failure localization, and limited applicable topologies. In this paper, we design Themis, a lightweight learning-based failure localization scheme for general networks. In the data plane, Themis achieves line-speed high performance failure detection using in-network classifiers and fine-grained traffic features. To reduce communication overhead, only coarse-grained traffic features are reported to the control plane for localization when a failure occurs. In the control plane, we propose a two-stage passive-active hybrid failure localization approach to accurately locate the failure without incurring excessive probing traffic. First, passive detection is conducted through the lightweight model XGBoost to infer a Potential Failure Link Set (PFLS). Then, active detection is done by only sending out probing packets to locations in the PFLS for precise failure localization. Comprehensive experiments demonstrate that Themis achieves ms-level failure localization with at least 95.63% accuracy, while saving 87.41% of bandwidth and 41.88% of hardware resource overhead on average compared with the state-of-the-art schemes.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"255 ","pages":"Article 110836"},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on indoor multi-floor positioning method based on LoRa 基于 LoRa 的室内多楼层定位方法研究

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-05 DOI: 10.1016/j.comnet.2024.110838

Honghong Chen, Jie Yang, Zhanjun Hao, Tian Qi, TingTing Liu

{"title":"Research on indoor multi-floor positioning method based on LoRa","authors":"Honghong Chen, Jie Yang, Zhanjun Hao, Tian Qi, TingTing Liu","doi":"10.1016/j.comnet.2024.110838","DOIUrl":"10.1016/j.comnet.2024.110838","url":null,"abstract":"<div><div>Existing floor localization methods are plagued by low accuracy, high algorithmic complexity, dense node deployment, susceptibility to environmental factors, and the inability to track trajectories. This paper introduces a localization method designed to address the challenges of multi-floor environments, leveraging LoRa technology. The approach involves deploying LoRa vertical positioning devices and establishing offline and threshold fingerprint databases. To enhance localization accuracy, it combines Time-of-Flight (TOF) ranging values (referred to as \"RANGE\" in this paper) with Received Signal Strength Indicator (RSSI) values, referred to as \"RSSI-RANGE\". Subsequently, a multi-floor determination is achieved using the RSSI-RANGE floor determination algorithm and a range-based signal source autonomous switching mechanism. The fingerprinting technique is then employed for trajectory recognition. Comprehensive vertical information is obtained by combining floor determination and trajectory award. Gaussian filtering is utilized for fingerprint preprocessing to eliminate gross errors. The particle swarm optimization algorithm is employed to fine-tune the hyperparameters of the random forest algorithm following noise reduction. Using the random forest algorithm, optimal RSSI-RANGE values are derived, and the offline fingerprint database is established by applying Kriging interpolation. Localization is then achieved in the concluding online recognition phase. Empirical findings illustrate the system's high floor accuracy rate of 97.8%, achieving high determination accuracy and comprehensive floor localization when combined with trajectory recognition.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110838"},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Edge-device collaborative computing for multi-view classification 多视角分类的边缘设备协同计算

IF 4.4 2区计算机科学

Computer Networks Pub Date : 2024-10-05 DOI: 10.1016/j.comnet.2024.110823

Marco Palena , Tania Cerquitelli , Carla Fabiana Chiasserini

{"title":"Edge-device collaborative computing for multi-view classification","authors":"Marco Palena , Tania Cerquitelli , Carla Fabiana Chiasserini","doi":"10.1016/j.comnet.2024.110823","DOIUrl":"10.1016/j.comnet.2024.110823","url":null,"abstract":"<div><div>Motivated by the proliferation of Internet-of-Thing (IoT) devices and the rapid advances in the field of deep learning, there is a growing interest in pushing deep learning computations, conventionally handled by the cloud, to the edge of the network to deliver faster responses to end users, reduce bandwidth consumption to the cloud, and address privacy concerns. However, to fully realize deep learning at the edge, two main challenges still need to be addressed: (i) how to meet the high resource requirements of deep learning on resource-constrained devices, and (ii) how to leverage the availability of multiple streams of spatially correlated data, to increase the effectiveness of deep learning and improve application-level performance. To address the above challenges, we explore <em>collaborative inference</em> at the edge, in which edge nodes and end devices share correlated data and the inference computational burden by leveraging different ways to split computation and fuse data. Besides traditional centralized and distributed schemes for edge-end device collaborative inference, we introduce <em>selective schemes</em> that decrease bandwidth resource consumption by effectively reducing data redundancy. As a reference scenario, we focus on multi-view classification in a networked system in which sensing nodes can capture overlapping fields of view. The proposed schemes are compared in terms of accuracy, computational expenditure at the nodes, communication overhead, inference latency, robustness, and noise sensitivity. Experimental results highlight that selective collaborative schemes can achieve different trade-offs between the above performance metrics, with some of them bringing substantial communication savings (from 18% to 74% of the transmitted data with respect to centralized inference) while still keeping the inference accuracy well above 90%.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"254 ","pages":"Article 110823"},"PeriodicalIF":4.4,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0