model4.py

from keras import regularizers
from keras.models import Model
from keras.layers import Dense, Activation, Dropout, Input, Multiply, LSTM, Conv1D
from keras.layers import Bidirectional, BatchNormalization, Concatenate, GlobalMaxPooling1D
from keras.optimizers import Adam

def malware_detection_model_4():
    X_input = Input(shape=(1000, 102))

    # Normalization 1
    X = BatchNormalization()(X_input)

    # Gated CNN 1
    a_sig = Conv1D(filters=128, kernel_size=(2), strides=1, kernel_regularizer=regularizers.l2(0.0005),
                   activation="sigmoid", padding="same")(X)
    a_relu = Conv1D(filters=128, kernel_size=(2), strides=1, kernel_regularizer=regularizers.l2(0.0005),
                    activation="relu", padding="same")(X)
    a = Multiply()([a_sig, a_relu])

    # Gated CNN 2
    b_sig = Conv1D(filters=128, kernel_size=(3), strides=1, kernel_regularizer=regularizers.l2(0.0005),
                   activation="sigmoid", padding="same")(X)
    b_relu = Conv1D(filters=128, kernel_size=(3), strides=1, kernel_regularizer=regularizers.l2(0.0005),
                    activation="relu", padding="same")(X)
    b = Multiply()([b_sig, b_relu])

    # Concatenate
    X = Concatenate()([a, b])

    # Normalization 2
    X = BatchNormalization()(X)

    # BidirectionalLSTM
    X = Bidirectional(LSTM(100, return_sequences=True))(X)

    X = GlobalMaxPooling1D()(X)

    X = Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.0005))(X)

    X = Dropout(0.5)(X)

    X = Dense(2, kernel_regularizer=regularizers.l2(0.0005))(X)
    X = Activation("softmax")(X)
    model = Model(inputs=X_input, outputs=X)

    opt = Adam(learning_rate=0.001, decay=1e-8)
    model.compile(loss="sparse_categorical_crossentropy", optimizer=opt, metrics=["accuracy"])

    return model