本文選題：小水電 + 遠(yuǎn)程監(jiān)控系統(tǒng)　； 參考：《浙江工業(yè)大學(xué)》2014年碩士論文

【摘要】：小水電是一種清潔、安全的可再生能源,對(duì)于環(huán)境保護(hù)有重要意義,是國(guó)家能源發(fā)展的重點(diǎn)戰(zhàn)略方向。目前,電站間的通信逐漸開(kāi)始互聯(lián)化,遠(yuǎn)程監(jiān)控系統(tǒng)開(kāi)始運(yùn)用于各種類(lèi)型的水電站,隨之而來(lái)的網(wǎng)絡(luò)安全問(wèn)題也日趨增多。入侵檢測(cè)系統(tǒng)是一種主動(dòng)發(fā)現(xiàn)網(wǎng)絡(luò)異常的安全防護(hù)技術(shù),對(duì)于保障計(jì)算機(jī)網(wǎng)絡(luò)安全十分重要。 本文在分析入侵檢測(cè)技術(shù)研究現(xiàn)狀的基礎(chǔ)上,研究基于機(jī)器學(xué)習(xí)技術(shù)的入侵檢測(cè)方法及系統(tǒng)構(gòu)建,選擇了適合小水電環(huán)境下的檢測(cè)方法,具有重要的研究?jī)r(jià)值。主要工作如下： (1)提出小水電環(huán)境下機(jī)器學(xué)習(xí)算法對(duì)入侵行為識(shí)別效果的評(píng)估指標(biāo),能夠綜合評(píng)估算法的準(zhǔn)確率、召回率、誤差率、建模時(shí)間、樣本復(fù)雜度以及算法本身的復(fù)雜度。通過(guò)六種常用的機(jī)器學(xué)習(xí)算法進(jìn)行了驗(yàn)證,實(shí)驗(yàn)詳細(xì)表述了算法評(píng)估的每一個(gè)過(guò)程,最后得到最佳算法,邏輯回歸。 (2)提出了一種基于高斯異常檢測(cè)和邏輯回歸模型的輕量級(jí)入侵檢測(cè)算法,首先使用訓(xùn)練的高斯異常檢測(cè)模型對(duì)流量數(shù)據(jù)進(jìn)行過(guò)濾,分別得到正常流量、異常流量和待定流量,然后使用邏輯回歸算法對(duì)待定流量再進(jìn)行最后判斷。實(shí)驗(yàn)結(jié)果顯示,該算法僅使用描述網(wǎng)絡(luò)流量行為的2個(gè)特征,即可對(duì)將近一半的網(wǎng)絡(luò)流量進(jìn)行有效的判斷,節(jié)省了檢測(cè)時(shí)間以及硬件計(jì)算資源。 (3)基于上述成果,開(kāi)發(fā)了小水電遠(yuǎn)程監(jiān)控系統(tǒng)網(wǎng)絡(luò)入侵檢測(cè)系統(tǒng),包括了系統(tǒng)架構(gòu)設(shè)計(jì)、數(shù)據(jù)庫(kù)設(shè)計(jì)和軟件開(kāi)發(fā)環(huán)境設(shè)計(jì),為小水電遠(yuǎn)程監(jiān)控系統(tǒng)網(wǎng)絡(luò)入侵檢測(cè)提供了有效的解決方案。
[Abstract]:Small hydropower is a kind of clean and safe renewable energy, which has important significance for environmental protection and is the key strategic direction of national energy development. At present, the communication between power stations is becoming interconnected gradually, remote monitoring system has been applied to all kinds of hydropower stations, and the network security problems are also increasing day by day. Intrusion detection system (IDS) is a kind of security protection technology which can actively detect network anomalies. It is very important to ensure the security of computer network. On the basis of analyzing the present situation of intrusion detection technology, this paper studies the intrusion detection method and system construction based on machine learning technology, and selects the detection method suitable for small hydropower environment, which has important research value. The main work is as follows: (1) the evaluation index of machine learning algorithm for intrusion recognition in small hydropower environment is proposed, which can comprehensively evaluate the accuracy, recall rate, error rate and modeling time of the algorithm. The complexity of the sample and the complexity of the algorithm itself. Six commonly used machine learning algorithms are used to verify the algorithm. The experiment describes each process of algorithm evaluation in detail. Finally, the best algorithm is obtained. (2) A lightweight intrusion detection algorithm based on Gao Si anomaly detection and logical regression model is proposed. Firstly, the trained Gao Si anomaly detection model is used to filter the traffic data to obtain the normal traffic. The abnormal flow and undetermined flow are determined by the logic regression algorithm. The experimental results show that the proposed algorithm can effectively judge nearly half of the network traffic using only two features describing network traffic behavior, and saves detection time and hardware computing resources. (3) based on the above results, The network intrusion detection system of small hydropower remote monitoring system is developed, including system architecture design, database design and software development environment design, which provides an effective solution for small hydropower remote monitoring system network intrusion detection.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP393.08

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