MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)最新文献_第2页

C Program Partitioning with Fine-Grained Security Constraints and Post-Partition Verification 具有细粒度安全约束和分区后验证的C程序分区

MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) Pub Date : 2022-11-28 DOI: 10.1109/MILCOM55135.2022.10017451

Maxwell Levatich, Robert Brotzman, Benjamin Flin, Ta Chen, R. Krishnan, S. Edwards

{"title":"C Program Partitioning with Fine-Grained Security Constraints and Post-Partition Verification","authors":"Maxwell Levatich, Robert Brotzman, Benjamin Flin, Ta Chen, R. Krishnan, S. Edwards","doi":"10.1109/MILCOM55135.2022.10017451","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017451","url":null,"abstract":"We address the problem of program partitioning: dividing a program into isolated compartments that communicate via remote procedure calls to follow a security policy. Existing solutions for C programs often use a simple model that offers only “sensitive or not” control and do not provide formal guarantees of partition correctness. We present a C program partitioner for security-conscious applications that addresses these shortcomings through annotation with fine-grained security constraints (chiefly, declassification of sensitive data to select parties); from these annotations, we automatically determine a partition and auto-generate code for marshaling, serialization, and remote procedure calls. We provide post-partition verification, which leverages translation validation to show that output program partitions are behaviorally equivalent to their input programs and satisfy the security policy specified by annotations. We present results that show our approach is practical when partitioning large realistic C applications with non-trivial security constraints.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132341161","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

Federated Learning-Based Jamming Detection for Distributed Tactical Wireless Networks 基于联邦学习的分布式战术无线网络干扰检测

MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) Pub Date : 2022-11-28 DOI: 10.1109/MILCOM55135.2022.10017755

Aida Meftah, Georges Kaddoum, Tri Nhu Do, C. Talhi

{"title":"Federated Learning-Based Jamming Detection for Distributed Tactical Wireless Networks","authors":"Aida Meftah, Georges Kaddoum, Tri Nhu Do, C. Talhi","doi":"10.1109/MILCOM55135.2022.10017755","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017755","url":null,"abstract":"In this paper, we propose a federated learning (FL)-based JDWC algorithm for distributed tactical wireless networks (TWNs). Specifically, we consider a distributed TWN with multiple clusters under the presence of a mobile jammer, where various types of waveforms are used over the network. On local servers, we perform frequency domain analysis of the received waveforms to extract the unique features from the spectral correlation function (SCF) of each waveform and use these features for training local convolutional neural networks (CNNs) to detect the jammer attacks and classify waveforms. Moreover, considering a practical distributed TWN where each cluster head (CH) has a partial observation of the TWN with insufficient data samples, the proposed algorithm exploits the distributed learning feature of FL, i.e., global learning aggregation, to detect the existence of jammers and to distinguish the types of received waveforms over the entire TWN. We implement a rigorous TWN simulation using Matlab Toolboxes and our proposed algorithm using TensorFlow Federated (TFF). Numerical results show that the proposed algorithm outperforms the standalone local SCF-CNN algorithm. We further demonstrate that using the SCF feature provides more accuracy than using the In-phase/Quadrature (I/Q) features.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131578741","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: 1

Data-Driven Wireless Anomaly Detection Using Spectral Features 基于频谱特征的数据驱动无线异常检测

MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) Pub Date : 2022-11-28 DOI: 10.1109/MILCOM55135.2022.10017642

Stephan D. Frisbie, M. Younis

{"title":"Data-Driven Wireless Anomaly Detection Using Spectral Features","authors":"Stephan D. Frisbie, M. Younis","doi":"10.1109/MILCOM55135.2022.10017642","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017642","url":null,"abstract":"In this work, we present an anomaly detection algorithm for wireless spectrum data and evaluate its ability to accurately detect interfering transmissions. The algorithm considers three types of interfering signals: a co-channel transmission, an interfering continuous-wave transmission, or an adversarial replay attack. At the core of each anomaly detector is a feature space transformation, which learns a compressed representation of the data, followed by various metrics on this representation. Four feature space transformations and four detection metrics are investigated, each of which represents different characterizations of the distribution of the data, totaling 16 configurations of the anomaly detector. Our algorithm is evaluated using Wi-Fi spectrum data from real-world radio frequency (RF) captures in both mild and harsh channel conditions. We present an analysis of the performance of the various configurations under each of the interference types at varying signal-to-interference ratios. Lastly, we discuss the implications of this performance on the the distribution of the data and the recommended model to detect each type of anomaly.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129391019","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

Maritime Unmanned Systems and JANUS in Support to Distressed Submarine Search & Rescue: The REPMUS21 Experience 海上无人系统和JANUS支持遇险潜艇搜索与救援:REPMUS21经验

MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) Pub Date : 2022-11-28 DOI: 10.1109/MILCOM55135.2022.10017658

R. Petroccia, G. Ferri, P. Stinco, A. Tesei, S. Fioravanti, Thomas C. Furfaro, J. Alves, Andrea Molaschi, J. Bioucas-Dias, P. Dias, J. Sousa

{"title":"Maritime Unmanned Systems and JANUS in Support to Distressed Submarine Search & Rescue: The REPMUS21 Experience","authors":"R. Petroccia, G. Ferri, P. Stinco, A. Tesei, S. Fioravanti, Thomas C. Furfaro, J. Alves, Andrea Molaschi, J. Bioucas-Dias, P. Dias, J. Sousa","doi":"10.1109/MILCOM55135.2022.10017658","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017658","url":null,"abstract":"Currently, communications during submarine rescue operations are performed solely with the analogue underwater telephone and the usage of phonetic codes (Alpha to Zulu), requiring the presence of human operators. This work explores innovative concepts that make use of a cooperative network of Maritime Unmanned Systems (MUS) and digital underwater communications to: 1) support the search and localization of a submarine in distress (DISSUB); 2) promptly exchange relevant information among the unmanned systems, the DISSUB and the rescue teams, with special attention to the employment of standardized solutions, such as STANAG 4748 (JANUS) and machine-to-machine services; and 3) share a common tactical picture among the rescue teams, with real-time updates. These concepts were successfully demonstrated during the Robotics Experimentation and Prototyping MUS (REPMUS) Exercise (Portugal, September 2021). The achievements of REPMUS21 clearly show how digital underwater communications and a network of cooperative MUS can be effectively used to improve the response time and increase the situational awareness of the DISSUB and rescue teams, thus resulting in more effective rescue operations.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"8 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133052896","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: 3

MADRL Based Scheduling for 5G and Beyond 基于MADRL的5G及以后的调度

MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) Pub Date : 2022-11-28 DOI: 10.1109/MILCOM55135.2022.10017708

Haowen Chang, R. B. S. Sree, Hao Chen, Jianzhong Zhang, Lingjia Liu

引用次数: 2

The Future of GNU Radio: Heterogeneous Computing, Distributed Processing, and Scheduler-as-a-Plugin GNU无线电的未来:异构计算、分布式处理和调度器即插件

MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) Pub Date : 2022-11-28 DOI: 10.1109/MILCOM55135.2022.10017973

Joshua Morman, Marc Lichtman, Marcus Müller

引用次数: 1

Generalized Noncoherent Space-Time Block Codes from Quantum Error Correction 量子纠错的广义非相干空时分组码

MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) Pub Date : 2022-11-28 DOI: 10.1109/MILCOM55135.2022.10017902

S. Lanham, Eli Bradley, B. Cour

引用次数: 0

UAV aided FD-NOMA for Energy-efficient Ultra-Reliable Low-latency Communications 无人机辅助FD-NOMA节能超可靠低延迟通信

MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) Pub Date : 2022-11-28 DOI: 10.1109/MILCOM55135.2022.10017683

Mayur Katwe, Rasika Deshpande, Keshav Singh, Chih-Peng Li

{"title":"UAV aided FD-NOMA for Energy-efficient Ultra-Reliable Low-latency Communications","authors":"Mayur Katwe, Rasika Deshpande, Keshav Singh, Chih-Peng Li","doi":"10.1109/MILCOM55135.2022.10017683","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017683","url":null,"abstract":"Unmanned aerial vehicle (UAV) aided network and ultra-reliable low-latency communication (URLLC) systems are expected to enable a new paradigm for future mission-critical and military communications. This paper considers a novel framework of energy-efficient resource allocation design for a UAV-aided full-duplex non-orthogonal multiple access (FD-NOMA) system with URLLC. In particular, we formulate a problem of energy-efficiency maximization subject to the joint design of 3-D UAV placement and power allocation for uplink (UL) and downlink (DL) users of the FD-NOMA system under a finite block-length regime. We propose an alternating optimization framework where the problems of power allocation and UAV placement are alternatively solved using general convex approximation techniques and simulated annealing algorithms, respectively. Finally, numerical results are presented to validate the effectiveness and the superiority of the proposed UAV-aided FD-NOMA URLLC system over conventional orthogonal multiple access (OMA) and half-duplex (HD-NOMA) schemes, while maintaining low packet-size and high reliability.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114790472","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

GAN-Based Detection of Adversarial EM Signal Waveforms 基于gan的对抗电磁信号波形检测

MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) Pub Date : 2022-11-28 DOI: 10.1109/MILCOM55135.2022.10017943

A. Gkelias, K. Leung

{"title":"GAN-Based Detection of Adversarial EM Signal Waveforms","authors":"A. Gkelias, K. Leung","doi":"10.1109/MILCOM55135.2022.10017943","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017943","url":null,"abstract":"Detection of unauthorised or malicious electromagnetic (EM) transmissions in the wireless spectrum is highly important in both military and commercial systems. In military wireless networks, and particularly in congested EM environments, the detection of unknown radar or communication waveforms can lead to timely identification of potentially adversarial transmissions or intruders in the area. On the other hand, in cognitive radio networks the identification of unauthorised communication waveforms can prevent and mitigate security threats, such as Primary User Emulation (PUE) attacks. However, data of such waveforms are usually of insignificant size to be effectively modelled or even there are no prior data available since they appear for the first time, which makes their timely detection particularly difficult. In this paper, we present a Generative Adversarial Network (GAN) based system which trains on available (presumably friendly) EM signals to detect any previously unseen types of EM waveforms, which can be potentially characterised as unauthorised or malicious. The proposed system is successfully trained and tested on a synthetic dataset comprising different pulsed radar and communication modulated signals impaired with Rician multipath fading, AWGN and random clock offset, resulting in center frequency offset and sampling time drift, and it was shown to successfully detect any previously unseen types of EM waveforms even in low SNR.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"71 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130694303","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

Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning 基于深度学习的下一个信号分类的对抗性机器学习和防御博弈

MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM) Pub Date : 2022-11-28 DOI: 10.1109/MILCOM55135.2022.10017674

Y. Sagduyu

{"title":"Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning","authors":"Y. Sagduyu","doi":"10.1109/MILCOM55135.2022.10017674","DOIUrl":"https://doi.org/10.1109/MILCOM55135.2022.10017674","url":null,"abstract":"This paper presents a game-theoretic framework to study the interactions of attack and defense for deep learning-based NextG signal classification. NextG systems such as the one envisioned for a massive number of IoT devices can employ deep neural networks (DNNs) for various tasks such as user equipment identification, physical layer authentication, and detection of incumbent users (such as in the Citizens Broadband Radio Service (CBRS) band). By training another DNN as the surrogate model, an adversary can launch an inference (exploratory) attack to learn the behavior of the victim model, predict successful operation modes (e.g., channel access), and jam them. A defense mechanism can increase the adversary's uncertainty by introducing controlled errors in the victim model's decisions (i.e., poisoning the adversary's training data). This defense is effective against an attack but reduces the performance when there is no attack. The interactions between the defender and the adversary are formulated as a non-cooperative game, where the defender selects the probability of defending or the defense level itself (i.e., the ratio of falsified decisions) and the adversary selects the probability of attacking. The defender's objective is to maximize its reward (e.g., throughput or transmission success ratio), whereas the adversary's objective is to minimize this reward and its attack cost. The Nash equilibrium strategies are determined as operation modes such that no player can unilaterally improve its utility given the other's strategy is fixed. A fictitious play is formulated for each player to play the game repeatedly in response to the empirical frequency of the opponent's actions. The performance in Nash equilibrium is compared to the fixed attack and defense cases, and the resilience of NextG signal classification against attacks is quantified.","PeriodicalId":239804,"journal":{"name":"MILCOM 2022 - 2022 IEEE Military Communications Conference (MILCOM)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132419943","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