With the rise of remote work and the digital industry,advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics,rendering them difficult to detect with conventional intrus...With the rise of remote work and the digital industry,advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics,rendering them difficult to detect with conventional intrusion detection methods.Signature-based intrusion detection methods can be used to detect attacks;however,they cannot detect new malware.Endpoint detection and response(EDR)tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques.However,EDR tools are restricted by the continuous generation of unnecessary logs,resulting in poor detection performance and memory efficiency.Machine learning-based intrusion detection techniques for responding to advanced cyberattacks are memory intensive,using numerous features;they lack optimal feature selection for each attack type.To overcome these limitations,this study proposes a memory-efficient intrusion detection approach incorporating multi-binary classifiers using optimal feature selection.The proposed model detects multiple types of malicious attacks using parallel binary classifiers with optimal features for each attack type.The experimental results showed a 2.95%accuracy improvement and an 88.05%memory reduction using only six features compared to a model with 18 features.Furthermore,compared to a conventional multi-classification model with simple feature selection based on permutation importance,the accuracy improved by 11.67%and the memory usage decreased by 44.87%.The proposed scheme demonstrates that effective intrusion detection is achievable with minimal features,making it suitable for memory-limited mobile and Internet of Things devices.展开更多
针对基本孤立词识别系统中语音信号预处理效果差、模板匹配成功率低、词汇识别耗时长等问题,基于动态时间规整算法(Dynamic Time Warping,DTW)提出了一种改进孤立词识别系统。首先,通过仿真设置合理的帧长和帧移数,并利用改进的端点检...针对基本孤立词识别系统中语音信号预处理效果差、模板匹配成功率低、词汇识别耗时长等问题,基于动态时间规整算法(Dynamic Time Warping,DTW)提出了一种改进孤立词识别系统。首先,通过仿真设置合理的帧长和帧移数,并利用改进的端点检测法确定语音的始末端,提高了语音信号的预处理效果;其次,采用美尔倒谱系数结合一阶差分系数提取了语音信号的特征参数,从而有效降低了计算机的时间复杂度;然后,采用DTW算法有效降低了语音信号的累积失真距离,实现了对孤立词语音信号的有效识别;最后,通过计算机仿真,验证了所提出的设计及参数设置的有效性,并通过对比实验验证了设计的高效性。展开更多
文摘针对常规兵器靶场试验、部队训练及演习过程中非爆弹定位困难的问题,介绍了一种采用低成本声学传感器的终点弹道未爆弹探测技术。根据弹着区范围,布置若干声学传感器,保证其测量范围覆盖整个弹着区。对于每一个声学传感器采集到的气动噪声及落地声信号,执行以下计算步骤:采用快速傅里叶变换与拉普拉斯小波分析技术进行声学信号的降噪与增强;采用短时能量、短时幅度以及短时过零率进行气动噪声与落地声端点检测;采用小波包分析技术提取降噪增强后声学信号的特征;采用基于最小距离的阈值准则进行终点弹道气动噪声及落地声的识别。靶场试验未爆弹落点粗定位结果显示,文中所提技术可用于未爆弹落地点定位,定位精度可达10 m.
基金supported by MOTIE under Training Industrial Security Specialist for High-Tech Industry(RS-2024-00415520)supervised by the Korea Institute for Advancement of Technology(KIAT),and by MSIT under the ICT Challenge and Advanced Network of HRD(ICAN)Program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning&Evaluation(IITP)。
文摘With the rise of remote work and the digital industry,advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics,rendering them difficult to detect with conventional intrusion detection methods.Signature-based intrusion detection methods can be used to detect attacks;however,they cannot detect new malware.Endpoint detection and response(EDR)tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques.However,EDR tools are restricted by the continuous generation of unnecessary logs,resulting in poor detection performance and memory efficiency.Machine learning-based intrusion detection techniques for responding to advanced cyberattacks are memory intensive,using numerous features;they lack optimal feature selection for each attack type.To overcome these limitations,this study proposes a memory-efficient intrusion detection approach incorporating multi-binary classifiers using optimal feature selection.The proposed model detects multiple types of malicious attacks using parallel binary classifiers with optimal features for each attack type.The experimental results showed a 2.95%accuracy improvement and an 88.05%memory reduction using only six features compared to a model with 18 features.Furthermore,compared to a conventional multi-classification model with simple feature selection based on permutation importance,the accuracy improved by 11.67%and the memory usage decreased by 44.87%.The proposed scheme demonstrates that effective intrusion detection is achievable with minimal features,making it suitable for memory-limited mobile and Internet of Things devices.
文摘针对基本孤立词识别系统中语音信号预处理效果差、模板匹配成功率低、词汇识别耗时长等问题,基于动态时间规整算法(Dynamic Time Warping,DTW)提出了一种改进孤立词识别系统。首先,通过仿真设置合理的帧长和帧移数,并利用改进的端点检测法确定语音的始末端,提高了语音信号的预处理效果;其次,采用美尔倒谱系数结合一阶差分系数提取了语音信号的特征参数,从而有效降低了计算机的时间复杂度;然后,采用DTW算法有效降低了语音信号的累积失真距离,实现了对孤立词语音信号的有效识别;最后,通过计算机仿真,验证了所提出的设计及参数设置的有效性,并通过对比实验验证了设计的高效性。
