MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)最新文献_第10页

Network Data Curation Toolkit: Cybersecurity Data Collection, Aided-Labeling, and Rule Generation 网络数据管理工具包:网络安全数据收集、辅助标记和规则生成

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) Pub Date : 2021-11-29 DOI: 10.1109/MILCOM52596.2021.9653049

Jaime C. Acosta, Stephanie Medina, J. Ellis, Luisana Clarke, Veronica Rivas, Allison Newcomb

{"title":"Network Data Curation Toolkit: Cybersecurity Data Collection, Aided-Labeling, and Rule Generation","authors":"Jaime C. Acosta, Stephanie Medina, J. Ellis, Luisana Clarke, Veronica Rivas, Allison Newcomb","doi":"10.1109/MILCOM52596.2021.9653049","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9653049","url":null,"abstract":"Cybersecurity network data curation is the collection, labeling, and packaging of datasets that contain artifacts that are important in the cybersecurity domain. These assets are essential for cybersecurity research and key for defense technologies and systems to detect and respond to anomalies caused by adversaries. However, tools for data curation are lacking in all domains of cybersecurity, including enterprise and the military. Curation fuels empirical research and validation of protection, detection, and prevention techniques. Closing the gap will require the development of research-driven tools and technologies that facilitate and enforce not only collection and labeling, but also standardization and distribution. This paper describes a novel tool, called the Network Data Curation Toolkit (NDCT), which simplifies the process of collecting network traffic, keystrokes, mouse clicks; allows network packet labeling; automatically generates intrusion detection rules; and provides a visualization of results. Moreover, the tool has a built-in mechanism for exporting all data into a single distributable file. The tool is modular to allow extension and to facilitate its incorporation into existing workflows. We demonstrate the use of NDCT in two case studies. We first show how NDCT can augment cybersecurity exercises by having participants label their network data. We then describe a separate system that was embedded with the NDCT, which provides a workspace, allowing users to curate data through a multi-session environment, including generating intrusion detection rules for malware.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"116 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126388123","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 Novel Adaptable Framework for Covert Communications in Anonymized Protocols 一种新的匿名协议中隐蔽通信的自适应框架

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) Pub Date : 2021-11-29 DOI: 10.1109/MILCOM52596.2021.9652912

V. Kanth, Chad A. Bollmann, M. Tummala, J. McEachen

引用次数: 1

Towards Transformer-Based Real-Time Object Detection at the Edge: A Benchmarking Study 基于变压器的边缘实时目标检测:基准研究

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) Pub Date : 2021-11-29 DOI: 10.1109/MILCOM52596.2021.9653052

Colin Samplawski, Benjamin M. Marlin

引用次数: 1

Performance Analysis of Distributed Beamforming in Wireless Networks: The Effect of Synchronization and Doppler spread 无线网络中分布式波束形成的性能分析:同步和多普勒扩散的影响

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) Pub Date : 2021-11-29 DOI: 10.1109/MILCOM52596.2021.9653042

I. Dagres, A. Polydoros, A. L. Moustakas

引用次数: 4

Indoor 90 GHz Channel Measurement Results for LOS to NLOS Transitions LOS到NLOS过渡的室内90ghz信道测量结果

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) Pub Date : 2021-11-29 DOI: 10.1109/MILCOM52596.2021.9653035

Zeenat Afroze, Mohanad Mohsen, D. Matolak, Hudson Dye

引用次数: 0

The Design and Validation of ICN-Enabled Hybrid Unmanned Aerial System 基于icn的混合无人机系统设计与验证

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) Pub Date : 2021-11-29 DOI: 10.1109/MILCOM52596.2021.9653062

Manveen Kaur, R. Amin, Jim Martin

引用次数: 2

Ray-tracing based Channel Modeling for Rough-boundary Environments 基于光线追踪的粗糙边界环境通道建模

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) Pub Date : 2021-11-29 DOI: 10.1109/MILCOM52596.2021.9653090

Kyoung-Min Park, Eunji Lee, Jinwook Kim, Jaehoon Jung, Seong-Cheol Kim

{"title":"Ray-tracing based Channel Modeling for Rough-boundary Environments","authors":"Kyoung-Min Park, Eunji Lee, Jinwook Kim, Jaehoon Jung, Seong-Cheol Kim","doi":"10.1109/MILCOM52596.2021.9653090","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9653090","url":null,"abstract":"Wireless ad-hoc network which has not been supported by centralized infrastructure is widely used because of its utilitarian ability. Thanks to a low-complex aspect, It is favorable for IoBT (Internet of Battlefield Things) applications. Propagation channel analysis prior to the network configuration is required to the appropriate sensor deployment. Although experimental approaches warrant an accuracy, ray-tracing simulator is employed because site measurements are highly prohibitive and labor-absorbing. The scattering mechanisms are tough to be implemented by a ray-tracing simulator, which often causes low accuracy in harsh areas, such as subterranean environments. In this paper, the surface scattering theory that considers an incident wave at a rough boundary as the radiation source is exploited to revise the existing ray-tracing simulator. The accuracy of the revised simulator is verified by the channel sounding conducted in the subterranean area which has much roughness. The measurement result indicates that a propagation channel could be well analyzed by employing the surface scattering theory for the ray-tracing based channel analysis.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124237685","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

Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification 基于自对比学习的半监督无线电调制分类

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) Pub Date : 2021-11-29 DOI: 10.1109/MILCOM52596.2021.9652914

Dongxin Liu, Peng Wang, Tianshi Wang, T. Abdelzaher

引用次数: 12

HoneyModels: Machine Learning Honeypots HoneyModels:机器学习蜜罐

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) Pub Date : 2021-11-29 DOI: 10.1109/MILCOM52596.2021.9652947

Ahmed Abdou, Ryan Sheatsley, Yohan Beugin, Tyler J. Shipp, P. Mcdaniel

{"title":"HoneyModels: Machine Learning Honeypots","authors":"Ahmed Abdou, Ryan Sheatsley, Yohan Beugin, Tyler J. Shipp, P. Mcdaniel","doi":"10.1109/MILCOM52596.2021.9652947","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9652947","url":null,"abstract":"Machine Learning is becoming a pivotal aspect of many systems today, offering newfound performance on classification and prediction tasks, but this rapid integration also comes with new unforeseen vulnerabilities. To harden these systems the ever-growing field of Adversarial Machine Learning has proposed new attack and defense mechanisms. However, a great asymmetry exists as these defensive methods can only provide security to certain models and lack scalability, computational efficiency, and practicality due to overly restrictive constraints. Moreover, newly introduced attacks can easily bypass defensive strategies by making subtle alterations. In this paper, we study an alternate approach inspired by honeypots to detect adversaries. Our approach yields learned models with an embedded watermark. When an adversary initiates an interaction with our model, attacks are encouraged to add this predetermined watermark stimulating detection of adversarial examples. We show that HoneyModels can reveal 69.5% of adversaries attempting to attack a Neural Network while preserving the original functionality of the model. HoneyModels offer an alternate direction to secure Machine Learning that slightly affects the accuracy while encouraging the creation of watermarked adversarial samples detectable by the HoneyModel but indistinguishable from others for the adversary.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122497686","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

Combinatorial Boosting of Ensembles of Diversified Classifiers for Defense Against Evasion Attacks 防御逃避攻击的多元分类器集合的组合增强

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) Pub Date : 2021-11-29 DOI: 10.1109/MILCOM52596.2021.9653040

R. Izmailov, Peter Lin, S. Venkatesan, Shridatt Sugrim

{"title":"Combinatorial Boosting of Ensembles of Diversified Classifiers for Defense Against Evasion Attacks","authors":"R. Izmailov, Peter Lin, S. Venkatesan, Shridatt Sugrim","doi":"10.1109/MILCOM52596.2021.9653040","DOIUrl":"https://doi.org/10.1109/MILCOM52596.2021.9653040","url":null,"abstract":"Adversarial evasion attacks challenge the integrity of machine learning models by creating out-of-distribution samples that are then consistently misclassified. With a variety of detection and mitigation approaches proposed already, more sophisticated attacks typically defeat them. One of the most promising group of such approaches is based on creating multiple diversified models and leverage their ensemble properties for detection and mitigation of attacks. However, such approaches entail heavy computational cost for designing and training a significant number of models. The paper proposes (i) a combinatorial boosting of the number of diversified models that provides an exponentially expanded scope of reliable decisions, and (ii) robust methods for fusion of the resulting models and their combinations towards enhanced decisions in both benign and adversarial scenarios. Several versions of the approach were implemented and tested for network intrusion detection and color image classification tasks; the results show significant increase of resiliency against evasion attacks with low impact on benign performance.","PeriodicalId":187645,"journal":{"name":"MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121529818","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