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malware detection and machine learning(EMBER)

作者:互联网

EMBER

https://github.com/elastic/ember\
paper:  https://arxiv.org/abs/1804.04637

特征

9个特征组,可以分为两大部分

文件结构无关特征

文件结构相关特征

分别如下:

模型

lightgbm

        params = {
            "boosting": "gbdt",
            "objective": "binary",
            "num_iterations": 1000,
            "learning_rate": 0.05,
            "num_leaves": 2048,
            "max_depth": 15,
            "min_data_in_leaf": 50,
            "feature_fraction": 0.5
        }

malconv

        maxlen = 2**20 # 1MB
        embedding_size = 8 

        # define model structure
        inp = Input( shape=(maxlen,))
        emb = Embedding( input_dim, embedding_size )( inp )
        filt = Conv1D( filters=128, kernel_size=500, strides=500, use_bias=True, activation='relu', padding='valid' )(emb)
        attn = Conv1D( filters=128, kernel_size=500, strides=500, use_bias=True, activation='sigmoid', padding='valid')(emb)
        gated = Multiply()([filt,attn])
        feat = GlobalMaxPooling1D()( gated )
        dense = Dense(128, activation='relu')(feat)
        outp = Dense(1, activation='sigmoid')(dense)

        basemodel = Model( inp, outp )

标签:machine,malware,name,virtual,detection,version,address,TABLE,size
来源: https://www.cnblogs.com/gongyanzh/p/16324332.html