Interpreting the Values of Confusion Matrix from machine learning - tensorflow

Question

I used confusion_matrix() to evaluate the model that has been trained to detect the DDoS attack.
The result of confusion matrix is like below with my test data set.
I believe that False neg value should be not 0 if it correctly detected the attack that is not DDoS.
Below is the code that I have implemented my ML model. Could you please give me a suggestion to make the model correctly check the benign attacks?
model.add(Dense(units=64, activation='relu', input_dim=7)) # Input Layer
model.add(Dropout(0.5))
model.add(Dense(units=128, activation='relu')) # hidden Layer
model.add(Dropout(0.2))
model.add(Dense(units=64, activation='relu')) # hidden Layer
model.add(Dropout(0.2))
model.add(Dense(units=1, activation='sigmoid')) # Last Layer for output
model.compile(loss='binary_crossentropy',
optimizer=Adam(learning_rate=0.0001),
metrics=['accuracy'])
CSV_FILE = "ddos.csv"
df = pd.read_csv(CSV_FILE)
df.loc[(df.Label == "ddos"), "Label"] = 1.0
df.loc[(df.Label == "Benign"), "Label"] = 0.0
# Data set
x_train = np.array(df[["Flow Duration", "Tot Fwd Pkts", "TotLen Fwd Pkts",
"Flow IAT Mean","Flow IAT Std" ,"Flow IAT Max", "Flow IAT Min"]])
x_train = x_train.astype(float)
normalized_x = preprocessing.normalize(x_train)
y_train = np.array(df[["Label"]])
y_train = np.array(y_train, dtype = 'float')
normalized_y = preprocessing.normalize(y_train)
hist = model.fit(normalized_x, normalized_y, epochs=3, batch_size=128)
y_pred = model.predict(x_train)
y_pred = preprocessing.normalize(y_pred)
cf_matrix = confusion_matrix(y_test, np.rint(y_pred))
Notice that my dataset is not imbalanced, i.e. it has exactly 50% DDoS and 50% Normal traffic information.

Answer 1

Although such questions cannot actually be answered with any degree of certainty, there are indeed some serious issues with your code.
First, you should not normalize your labels y_train; this is a binary classification problem, and the labels are expected to be exactly 0/1. Remove the following lines:
normalized_y = preprocessing.normalize(y_train)
y_pred = preprocessing.normalize(y_pred)
change the labels into integers (not floats), i.e.:
df.loc[(df.Label == "ddos"), "Label"] = 1
df.loc[(df.Label == "Benign"), "Label"] = 0
and your model fit to:
hist = model.fit(normalized_x, y_train, epochs=3, batch_size=128)
Second, although you train with normalized_x, you subsequently request predictions with x_train, which is again wrong; your predictions should be:
y_pred = model.predict(normalized_x)
Third, dropout should not be used by default, but only if we have signs of overfitting - but to do so, our model has first to be able to start learning something, which is not the case here. Comment out all dropout layers, and start putting them back into the model only in case of overfitting.
Last, you should start with the default settings of Adam, which usually (and reportedly) work well out of the box, i.e.:
optimizer=Adam()
And of course you should consider running the model for more than epochs=3.