//進階分析，分析行為屬於哪種攻擊手法
private static List <(string, int)> DeepAnalysis(DataFlowStatistics[] FlowStatistics)
{
List <(double[], string)> Inputs = DeepLearningTools.FlowStatisticsToLearningData(FlowStatistics);
List <(string, int)> Result = new List <(string, int)>();
for (int i = 0; i < Inputs.Count; i++)
{
double[] outputValues = DBNetwork.Compute(Inputs[i].Item1);
Result.Add((Inputs[i].Item2, Convert.ToInt32(DeepLearningTools.FormatOutputResult(outputValues))));
}
return(Result);
}
//開始學習
public bool Run()
{
bool IsDone = false;
try
{
FlowDatas db = new FlowDatas();
(double[][] Inputs, double[][] Outputs)
= DeepLearningTools.FlowSampleToLearningData(db.FlowSampleStatistics.Where(c => c.BehaviorNumber != 0).ToArray());
db.Dispose();
//產生DBN網路
DBNetwork = new DeepBeliefNetwork(Inputs.First().Length,
(int)((Inputs.First().Length + Outputs.First().Length) / 1.5),
(int)((Inputs.First().Length + Outputs.First().Length) / 2),
Outputs.First().Length);
//亂數打亂整個網路參數
new GaussianWeights(DBNetwork, 0.1).Randomize();
DBNetwork.UpdateVisibleWeights();
//設定無監督學習組態
DeepBeliefNetworkLearning teacher
= new DeepBeliefNetworkLearning(DBNetwork)
{
Algorithm = (h, v, i) =>
new ContrastiveDivergenceLearning(h, v)
{
LearningRate = 0.01,
Momentum = 0.5,
Decay = 0.001,
}
};
//設置批量輸入學習。
int batchCount1 = Math.Max(1, Inputs.Length / 10);
//創建小批量加速學習。
int[] groups1
= Accord.Statistics.Classes.Random(Inputs.Length, batchCount1);
double[][][] batches = Inputs.Subgroups(groups1);
//學習指定圖層的數據。
double[][][] layerData;
//運行無監督學習。
for (int layerIndex = 0; layerIndex < DBNetwork.Machines.Count - 1; layerIndex++)
{
teacher.LayerIndex = layerIndex;
layerData = teacher.GetLayerInput(batches);
for (int i = 0; i < 200; i++)
{
double error = teacher.RunEpoch(layerData) / Inputs.Length;
if (i % 10 == 0)
{
Console.WriteLine(i + ", Error = " + error);
}
}
}
//對整個網絡進行監督學習，提供輸出分類。
var teacher2 = new ParallelResilientBackpropagationLearning(DBNetwork);
double error1 = double.MaxValue;
//運行監督學習。
for (int i = 0; i < 500; i++)
{
error1 = teacher2.RunEpoch(Inputs, Outputs) / Inputs.Length;
Console.WriteLine(i + ", Error = " + error1);
DBNetwork.Save(Path);
Console.WriteLine("Save Done");
}
DBNetwork.Save(Path);
Console.WriteLine("Save Done");
IsDone = true;
}
catch (Exception ex)
{
Debug.Write(ex.ToString());
}
return(IsDone);
}