In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network...In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.展开更多
Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version...Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version. The proposed method learns an optimal kernel combination and the associated classifier simultaneously. Two feature types are extracted from images, forming basis kernels. Then these basis kernels are weighted combined and resulted the composite kernel exploits interesting points and appearance information of objects simultaneously. Weights and the detection model are finally learnt by a new algorithm. Experimental results show that the proposed method improve detection accuracy to above 88%, yields good interpretation for the selected subset of features and appears sparser than traditional single-kernel RVMs.展开更多
畜禽养殖物联网由于工作环境恶劣、网络传输故障等因素容易产生异常感知数据,为保证数据质量,根据畜禽养殖物联网数据流周期性、时序性等特点,提出了一种基于滑动窗口与支持向量回归(Sliding window and support vector machines for re...畜禽养殖物联网由于工作环境恶劣、网络传输故障等因素容易产生异常感知数据,为保证数据质量,根据畜禽养殖物联网数据流周期性、时序性等特点,提出了一种基于滑动窗口与支持向量回归(Sliding window and support vector machines for regression,SW-SVR)的异常数据实时检测方法。首先根据畜禽物联网数据流特征周期以及采样频率确定滑动窗口尺寸;然后通过SVR模型预测畜禽养殖物联网数据流中某一时刻传感器测量值;最后计算预测区间,根据实际测量值是否落入该区间判断是否异常并对异常数据进行置换处理。采用畜禽养殖物联网环境数据进行试验,结果表明:所提滑动窗口计算方法得到的窗口尺寸预测的MAPE为0.188 4,畜禽养殖物联网异常数据检测率达98%,能够有效检测和处理畜禽养殖物联网数据流中的异常数据。展开更多
基金supported by the National Key Research and Development Program of China(2017YFB1401300,2017YFB1401304)the National Natural Science Foundation of China(61702211,L1724007,61902203)+3 种基金Hubei Provincial Science and Technology Program of China(2017AKA191)the Self-Determined Research Funds of Central China Normal University(CCNU)from the Colleges’Basic Research(CCNU17QD0004,CCNU17GF0002)the Natural Science Foundation of Shandong Province(ZR2017QF015)the Key Research and Development Plan–Major Scientific and Technological Innovation Projects of Shandong Province(2019JZZY020101)。
文摘In recent years, network traffic data have become larger and more complex, leading to higher possibilities of network intrusion. Traditional intrusion detection methods face difficulty in processing high-speed network data and cannot detect currently unknown attacks. Therefore, this paper proposes a network attack detection method combining a flow calculation and deep learning. The method consists of two parts: a real-time detection algorithm based on flow calculations and frequent patterns and a classification algorithm based on the deep belief network and support vector machine(DBN-SVM). Sliding window(SW) stream data processing enables real-time detection, and the DBN-SVM algorithm can improve classification accuracy. Finally, to verify the proposed method, a system is implemented.Based on the CICIDS2017 open source data set, a series of comparative experiments are conducted. The method's real-time detection efficiency is higher than that of traditional machine learning algorithms. The attack classification accuracy is 0.7 percentage points higher than that of a DBN, which is 2 percentage points higher than that of the integrated algorithm boosting and bagging methods. Hence, it is suitable for the real-time detection of high-speed network intrusions.
基金Supported by the National Natural Science Foundation of China (No.41001285)
文摘Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version. The proposed method learns an optimal kernel combination and the associated classifier simultaneously. Two feature types are extracted from images, forming basis kernels. Then these basis kernels are weighted combined and resulted the composite kernel exploits interesting points and appearance information of objects simultaneously. Weights and the detection model are finally learnt by a new algorithm. Experimental results show that the proposed method improve detection accuracy to above 88%, yields good interpretation for the selected subset of features and appears sparser than traditional single-kernel RVMs.
文摘畜禽养殖物联网由于工作环境恶劣、网络传输故障等因素容易产生异常感知数据,为保证数据质量,根据畜禽养殖物联网数据流周期性、时序性等特点,提出了一种基于滑动窗口与支持向量回归(Sliding window and support vector machines for regression,SW-SVR)的异常数据实时检测方法。首先根据畜禽物联网数据流特征周期以及采样频率确定滑动窗口尺寸;然后通过SVR模型预测畜禽养殖物联网数据流中某一时刻传感器测量值;最后计算预测区间,根据实际测量值是否落入该区间判断是否异常并对异常数据进行置换处理。采用畜禽养殖物联网环境数据进行试验,结果表明:所提滑动窗口计算方法得到的窗口尺寸预测的MAPE为0.188 4,畜禽养殖物联网异常数据检测率达98%,能够有效检测和处理畜禽养殖物联网数据流中的异常数据。