This research paper describes the design and implementation of the Consultative Committee for Space Data Systems (CCSDS) standards REF _Ref401069962 \r \h \* MERGEFORMAT [1] for Space Data Link Layer Protocol (SDLP). ...This research paper describes the design and implementation of the Consultative Committee for Space Data Systems (CCSDS) standards REF _Ref401069962 \r \h \* MERGEFORMAT [1] for Space Data Link Layer Protocol (SDLP). The primer focus is the telecommand (TC) part of the standard. The implementation of the standard was in the form of DLL functions using C++ programming language. The second objective of this paper was to use the DLL functions with OMNeT++ simulating environment to create a simulator in order to analyze the mean end-to-end Packet Delay, maximum achievable application layer throughput for a given fixed link capacity and normalized protocol overhead, defined as the total number of bytes transmitted on the link in a given period of time (e.g. per second) divided by the number of bytes of application data received at the application layer model data sink. In addition, the DLL was also integrated with Ground Support Equipment Operating System (GSEOS), a software system for space instruments and small spacecrafts especially suited for low budget missions. The SDLP is designed for rapid test system design and high flexibility for changing telemetry and command requirements. GSEOS can be seamlessly moved from EM/FM development (bench testing) to flight operations. It features the Python programming language as a configuration/scripting tool and can easily be extended to accommodate custom hardware interfaces. This paper also shows the results of the simulations and its analysis.展开更多
Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such atta...Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.展开更多
针对国内对于专用通信引擎的研究空缺,实现了一种支持多协议的可配置通信引擎设计,并以典型的数据链路层协议——高级数据链路控制(High 1evel Data Link Control,HDLC)协议的引擎块实现为例,采用System Verilog搭建仿真平台,通过C语言...针对国内对于专用通信引擎的研究空缺,实现了一种支持多协议的可配置通信引擎设计,并以典型的数据链路层协议——高级数据链路控制(High 1evel Data Link Control,HDLC)协议的引擎块实现为例,采用System Verilog搭建仿真平台,通过C语言编写测试case,以回环验证的方式保证设计正确性。可配置引擎块以自研RSIC核为核心,采用AHB总线互连,内部集成HDLC、UART等通信协议以及DMA、TDM、GPIO等通用外设,实现通信协议的处理及数据传输,有助于解放处理器负载,提高数据处理效率,同时将HDLC与可配置通信引擎相结合,解决了多路信号的HDLC对处理器资源的占用率高等问题。展开更多
文摘This research paper describes the design and implementation of the Consultative Committee for Space Data Systems (CCSDS) standards REF _Ref401069962 \r \h \* MERGEFORMAT [1] for Space Data Link Layer Protocol (SDLP). The primer focus is the telecommand (TC) part of the standard. The implementation of the standard was in the form of DLL functions using C++ programming language. The second objective of this paper was to use the DLL functions with OMNeT++ simulating environment to create a simulator in order to analyze the mean end-to-end Packet Delay, maximum achievable application layer throughput for a given fixed link capacity and normalized protocol overhead, defined as the total number of bytes transmitted on the link in a given period of time (e.g. per second) divided by the number of bytes of application data received at the application layer model data sink. In addition, the DLL was also integrated with Ground Support Equipment Operating System (GSEOS), a software system for space instruments and small spacecrafts especially suited for low budget missions. The SDLP is designed for rapid test system design and high flexibility for changing telemetry and command requirements. GSEOS can be seamlessly moved from EM/FM development (bench testing) to flight operations. It features the Python programming language as a configuration/scripting tool and can easily be extended to accommodate custom hardware interfaces. This paper also shows the results of the simulations and its analysis.
文摘Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.
文摘针对国内对于专用通信引擎的研究空缺,实现了一种支持多协议的可配置通信引擎设计,并以典型的数据链路层协议——高级数据链路控制(High 1evel Data Link Control,HDLC)协议的引擎块实现为例,采用System Verilog搭建仿真平台,通过C语言编写测试case,以回环验证的方式保证设计正确性。可配置引擎块以自研RSIC核为核心,采用AHB总线互连,内部集成HDLC、UART等通信协议以及DMA、TDM、GPIO等通用外设,实现通信协议的处理及数据传输,有助于解放处理器负载,提高数据处理效率,同时将HDLC与可配置通信引擎相结合,解决了多路信号的HDLC对处理器资源的占用率高等问题。
