model2.py

from keras import regularizers
from keras.models import Model
from keras.layers import Dense, Activation, Dropout, Input, Multiply, LSTM, Conv1D, Flatten, \
    RepeatVector, Permute, Lambda
from keras.layers import Bidirectional, BatchNormalization, Concatenate
from keras.optimizers import Adam
from keras import backend as K
import tensorflow as tf

def focal_loss(y_true, y_pred):
    gamma = 2.0
    alpha = 0.25
    pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
    pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
    return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1))-K.sum((1-alpha) * K.pow( pt_0, gamma) * K.log(1. - pt_0))

# Attention based model
def malware_detection_model_2():
    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)

    attention = Dense(1, activation='tanh')(X)
    attention = Flatten()(attention)
    attention = Activation('softmax')(attention)
    attention = RepeatVector(200)(attention)
    attention = Permute([2, 1])(attention)
    s_r = Multiply()([X, attention])
    s_r = Lambda(lambda xin: K.sum(xin, axis=-2), output_shape=(200,))(s_r)

    X = Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.0005))(s_r)
    X = Dropout(0.2)(X)
    X = Dense(1, kernel_regularizer=regularizers.l2(0.0005))(X)
    X = Activation("sigmoid")(X)
    model = Model(inputs=X_input, outputs=X)

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

    return model