MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)最新文献_第3页

Detecting Cellphone Camera Status at Distance by Exploiting Electromagnetic Emanations 利用电磁辐射远距离探测手机摄像头状态

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) Pub Date : 2019-11-01 DOI: 10.1109/MILCOM47813.2019.9021060

B. Yilmaz, E. Ugurlu, Milos Prvulović, A. Zajić

引用次数: 8

An Evaluation of Consensus Latency in Partitioning Networks 分区网络中共识延迟的评估

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) Pub Date : 2019-11-01 DOI: 10.1109/MILCOM47813.2019.9020817

Jason A. Tran, G. Ramachandran, C. Danilov, B. Krishnamachari

{"title":"An Evaluation of Consensus Latency in Partitioning Networks","authors":"Jason A. Tran, G. Ramachandran, C. Danilov, B. Krishnamachari","doi":"10.1109/MILCOM47813.2019.9020817","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020817","url":null,"abstract":"Consensus, or state machine replication, is critical for the deployment of distributed battlefield systems. Battlefield networks operate in environments with unpredictable wireless connectivity which lead to sparse networks and frequent partitioning, and this makes deploying centralized architectures where nodes require a connection to a remote server unsuitable. The Extended Virtual Synchrony (EVS) model provides membership views which enables a network to reach consensus even after experiencing a series of partitions and mergers. If a node wants to propose state transitions that require nodes that are not currently in its membership view, then the node needs to wait until it reconnects with those nodes. The time the node has to wait to reconnect to the other nodes introduces consensus delays in the network. In this work, we evaluate consensus latency by focusing on these queued state transition proposals due to both network partition characteristics and distributed application/mission design. The key findings of our results show that consensus delay is least affected by network partitioning when the network splits at a rate equal to or less than 1/4 the rate in which partitions merge. Our evaluation results provide application and mission designers guidelines on the tradeoffs between several network characteristics and desired consensus latency properties.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128874152","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 Collection and Analysis Framework for Mobile Ad Hoc Network Research 移动Ad Hoc网络数据采集与分析框架研究

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) Pub Date : 2019-11-01 DOI: 10.1109/MILCOM47813.2019.9020979

S. Arbiv, R. Amin, T. Goff, Downing Street, Igor Pedan, L. Bressler, Terrence Gibbons, Bow-Nan Cheng, Chayil Timmerman

{"title":"Data Collection and Analysis Framework for Mobile Ad Hoc Network Research","authors":"S. Arbiv, R. Amin, T. Goff, Downing Street, Igor Pedan, L. Bressler, Terrence Gibbons, Bow-Nan Cheng, Chayil Timmerman","doi":"10.1109/MILCOM47813.2019.9020979","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020979","url":null,"abstract":"The U.S. Department of Defense (DoD) has invested significantly in development and deployment of aerial high-capacity backbone (HCB) networks. Understanding the performance of these Mobile Ad-Hoc Networks (MANETs) is challenging, and requires insight into multiple layers of the protocol stack. Tons of data get generated at each layer of the stack. MIT LL has developed a data collection and visualization framework to parse through important data and help monitor and analyze the performance of these networks. The HCB data collection and analysis framework is comprised of pluggable data collection and reporting daemons, a persistent storage component based on a time-series database, and a visualization dashboard capable of displaying network performance metrics in real-time and playback modes. In this paper, we showcase the capabilities of this framework, identifying how it has helped further our research, and how it can be adapted to support other similar research programs that generate tons of data. 11DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. This material is based upon work supported by the Department of the Navy under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Navy.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121303680","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

Modeling Resource Limited Cooperative Broadcasting using Hard Core Poisson Process 基于硬核泊松过程的资源有限合作广播建模

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) Pub Date : 2019-11-01 DOI: 10.1109/MILCOM47813.2019.9020976

Wenjun Huang, Xu Li, Hancheng Ma, Yanan Liang

引用次数: 1

Neural Malware Control with Deep Reinforcement Learning 基于深度强化学习的神经恶意软件控制

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) Pub Date : 2019-11-01 DOI: 10.1109/MILCOM47813.2019.9020862

Yu Wang, J. W. Stokes, M. Marinescu

{"title":"Neural Malware Control with Deep Reinforcement Learning","authors":"Yu Wang, J. W. Stokes, M. Marinescu","doi":"10.1109/MILCOM47813.2019.9020862","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020862","url":null,"abstract":"Antimalware products are a key component in detecting malware attacks, and their engines typically execute unknown programs in a sandbox prior to running them on the native operating system. Files cannot be scanned indefinitely so the engine employs heuristics to determine when to halt execution. Previous research has investigated analyzing the sequence of system calls generated during this emulation process to predict if an unknown file is malicious, but these models often require the emulation to be stopped after executing a fixed number of events from the beginning of the file. Also, these classifiers are not accurate enough to halt emulation in the middle of the file on their own. In this paper, we propose a novel algorithm which overcomes this limitation and learns the best time to halt the file's execution based on deep reinforcement learning (DRL). Because the new DRL-based system continues to emulate the unknown file until it can make a confident decision to stop, it prevents attackers from avoiding detection by initiating malicious activity after a fixed number of system calls. Results show that the proposed malware execution control model automatically halts emulation for 91.3% of the files earlier than heuristics employed by the engine. Furthermore, classifying the files at that time significantly improves the classifier's accuracy. This new model improves the true positive rate by 61.5%, at a false positive rate of 1%, compared to the best baseline classifier.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123866297","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: 9

Autonomic Clustering in Temporal Network Graphs 时间网络图的自主聚类

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) Pub Date : 2019-11-01 DOI: 10.1109/MILCOM47813.2019.9020970

J. Macker, Jeffery W. Weston, David J. Claypool

{"title":"Autonomic Clustering in Temporal Network Graphs","authors":"J. Macker, Jeffery W. Weston, David J. Claypool","doi":"10.1109/MILCOM47813.2019.9020970","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020970","url":null,"abstract":"In this paper, we examine the use of autonomic clustering algorithms on temporal graph topologies representing mobile communication networks. We introduce basic group-based mobility scenarios including periods of overlapping group clusters and present both emulation and simulation models of these scenarios. From extracted temporal graph models, we demonstrate how periods of clustering overlap introduce specific challenges in the autonomic clustering of temporal graph models. We perform several group mobility experiments on classes of autonomic clustering approaches and we focus in on some high quality clustering algorithm performers including: Spectral clustering, multilevel clustering, and information theoretic clustering. We present quality metrics and examine basic measures of accuracy and stability and further demonstrate challenges associated with both measuring quality and effectively partitioning evolving graphs. We then demonstrate improvements in detecting the temporal “ground truth” clustering by the use of a time-windowed, weighted graph representation. We conclude with a discussion of future areas of work and summarize initial experiments.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"3 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113980575","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

Hardening Containers for Cross-Domain Applications 加固跨域应用容器

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) Pub Date : 2019-11-01 DOI: 10.1109/MILCOM47813.2019.9020992

Jason Dahlstrom, Jim Brock, Mekedem Tenaw, M. Shaver, Stephen Taylor

{"title":"Hardening Containers for Cross-Domain Applications","authors":"Jason Dahlstrom, Jim Brock, Mekedem Tenaw, M. Shaver, Stephen Taylor","doi":"10.1109/MILCOM47813.2019.9020992","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020992","url":null,"abstract":"Cross-domain platforms control the sharing of information at multiple classification levels. For example, some cross-domain systems allow a single user to view multiple screens, at different security classification levels, on a single monitor. The core security guarantees rest on a base-of-trust in hardware established primarily through hypervisor technology. Unfortunately, over the years, hypervisors and their associated management interfaces have steadily grown in complexity, to the point where they now exceed the size of the operating system kernels they seek to protect. This has made it increasingly difficult to verify security properties in the face of kernel-level zero-day exploits and advanced persistent threats. At the same time, there has been a radical shift in computing methodology motivated by the realization that reliable deployment at scale requires an application to be associated with a specific operating system version with carefully designated libraries. This realization has resulted in an alternative computing paradigm ― containers - that wrap application attributes and execute through a shared kernel. This paper describes a novel embedded systems technology, the nano-marshal: a light-weight container system, compliant with the Open Containers Initiative (OCI), that supports cross-domain applications. The system permits container security to be hardened through innovative hardware mechanisms, hidden within Field Programmable Gate Arrays (FPGA's).","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131778563","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: 2

VINE: Zero-Control-Packet Routing for Ultra-Low-Capacity Mobile Ad Hoc Networks VINE:超低容量移动自组织网络的零控制分组路由

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) Pub Date : 2019-11-01 DOI: 10.1109/MILCOM47813.2019.9020768

Ayush Dusia, R. Ramanathan, Warren Ramanathan, Christophe Servaes, A. Sethi

引用次数: 5

Detection of Encrypted Malicious Network Traffic using Machine Learning 使用机器学习检测加密的恶意网络流量

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) Pub Date : 2019-11-01 DOI: 10.1109/MILCOM47813.2019.9020856

Michael J. De Lucia, Chase Cotton

{"title":"Detection of Encrypted Malicious Network Traffic using Machine Learning","authors":"Michael J. De Lucia, Chase Cotton","doi":"10.1109/MILCOM47813.2019.9020856","DOIUrl":"https://doi.org/10.1109/MILCOM47813.2019.9020856","url":null,"abstract":"The proliferation of encrypted network traffic necessitates an innovative machine learning traffic analysis approach which does not rely on pattern matching or the payload content of the packets to detect malicious / suspicious communications. Encryption of Internet traffic has increasingly become a typical best practice, making network packet content analysis yield diminishing returns. A majority of internet traffic is now protected using the cryptographic protocol known as Transport Layer Security (TLS). Malware authors have also followed this trend with the use of TLS to hide malicious network communications. We propose a malicious communication detection mechanism using a Support Vector Machine (SVM) and an alternative with a Convolutional Neural Network (CNN). Both methods achieve respectable results and a low False Positive Rate (FPR). However, the SVM method outperforms the CNN method in all evaluation metrics presented. Lastly, we propose future work to experiment with transport layer size and direction as features and automate feature engineering by using raw packet traffic with a CNN augmented with a Long Short-term Memory (LSTM) for detection of malicious traffic.","PeriodicalId":371812,"journal":{"name":"MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134298398","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: 22

CH-MAC: A Cluster-based, Hybrid TDMA MAC Protocol over Wireless Ad-hoc Networks CH-MAC:无线自组织网络上基于集群的混合TDMA MAC协议

MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM) Pub Date : 2019-11-01 DOI: 10.1109/MILCOM47813.2019.9020769

Hoyong Choi, Jihwan Bang, Namjo Ahn, Jinhwan Jung, Jungwook Choi, Soobum Park, Yung Yi

引用次数: 3