""" DATASET SOURCE is from https://github.com/arjbah/nsl-kdd.git (include the most attack types) https://github.com/defcom17/NSL_KDD.git """ train_file = 'https://raw.githubusercontent.com/arjbah/nsl-kdd/master/nsl-kdd/KDDTrain%2B.txt' test_file = 'https://raw.githubuserconten...
DATASET SOURCE is from https://github.com/arjbah/nsl-kdd.git (include the most attack types) https://github.com/defcom17/NSL_KDD.git """ train_file = 'https://raw.githubusercontent.com/arjbah/nsl-kdd/master/nsl-kdd/KDDTrain%2B.txt' test_file = 'https://raw.githubusercontent.com...
DATASET SOURCE is from https://github.com/arjbah/nsl-kdd.git (include the most attack types) https://github.com/defcom17/NSL_KDD.git """ train_file ='https://raw.githubusercontent.com/arjbah/nsl-kdd/master/nsl-kdd/KDDTrain%2B.txt' test_file ='https://raw.githubusercontent.com/ar...
代码入场: # import packagesimport pandas as pd"""DATASET SOURCE is from https://github.com/arjbah/nsl-kdd.git (include the most attack types)https://github.com/defcom17/NSL_KDD.git"""train_file = 'https://raw.githubusercontent.com/arjbah/nsl-kdd/master/nsl-kdd/KDDTrain%2B.txt'test...
smurf: Smurf attacks in KDD dataset useICMP echorequest packets directed to IP broadcast addresses from remote locations to create DoS attack. It can be identified by watching large number of Echo requests and replies from the victim machine. The coloumn'count'values from the dataset can be read...
NSL-KDD datasetdeep-learningMACHINE-LEARNINGCNN channel Attentionnetwork securityIntrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online threats.The goal of this research is to meet the urgent need for small-footprint,...
DATASET SOURCE is from https://github.com/arjbah/nsl-kdd.git (include the most attack types) https://github.com/defcom17/NSL_KDD.git """ train_file = 'https://raw./arjbah/nsl-kdd/master/nsl-kdd/KDDTrain%2B.txt' test_file = 'https://raw./arjbah/nsl-kdd/master/nsl-kdd/KDDTes...
On the NSL-KDD dataset, our CNN-based approach achieves an astounding 99.728% accuracy rate when paired with channel attention. Compared to previous approaches such as ensemble learning, CNN, RBM (Boltzmann machine), ANN, hybrid auto-encoders with CNN, MCNN, and ANN, and adaptive algorithms, ...
# Function to load dataset and divide it into 8 partitions def load_dataset(path): dataset_rdd = sc.textFile(path, 8).map(lambda line: line.split(',')) dataset_df = (dataset_rdd.toDF(col_names.tolist()).select( col('duration').cast(DoubleType()), col('protocol_type').cast(Str...
Feature based analysis using ML classifiers on the NSL-KDD Dataset - arijeetsat/NSL-KDD-Dataset-Analysis