Ref. | ML Technique | Dataset | Features | Evaluation | |
---|---|---|---|---|---|
Settings | Results | ||||
Cannady et al. [85] | RL CMAC-NN (Online) | Prototype Application | Patterns of Ping Flood and UDP Packet Storm attacks | -3 Layers NN -Prototype developed w/ C & Matlab | Learning Error: 2.199-1.94 −07% New Attack Error:2.199-8.53 −14% Recollection Error: 0.038-3.28 −05% Error after Refinement: 1.24% |
Servin et al. [407] | RL Q-Learning (Online) | Generated using NS-2 | Congestion, Delay, and Flow-based | -Number of Agents: 7 -DDoS attacks only -Boltzmann’s rules for E2 | FP: 0-10% Accuracy: ∼ 70%- ∼ 99% Recall: ∼ 30%- ∼ 99% |
Li et al. [273] | DL DBN w/ Auto-Encoder (Offline) | KDD Cup [257] | all 41 features | -494,021 training records -311,029 testing records -Intel Core Duo CPU 2.10 GHz and 2GB RAM -Platform used: Matlab v.7.11 -3 Layers Encoder: 41,300,150,75,* | TPR: 92.20%-96.79% FPR: 1.58%-15.79% Accuracy: 88.95%-92.10% Training time: [1.147-2.625] sec |
Alom et al. [14] | DL DBN (Offline) | NSL-KDD [438] | 39 features | -25,000 training & testing records | DR w/ 40% data for training: 97.45% Training time w/ 40% data for training: 0.32 sec |
Tang et al. [436] | DL DNN (Offline) | NSL-KDD [438] | 6 basic features | -125,975 training records -22,554 testing records -3-Layers DNN: 6,12,6,3,2 -Batch Size: 10 # Epochs: 100 -Best Learning Rate: 0.001 | Accuracy: 72.0%5-75.75% Precision: 79%-83% Recall: 72%-76% F-measure: 72%-75% |
Kim et al. [245] | DL LSTM-RNN (Offline) | KDD Cup [257] | all 41 features | -1,930 training data records -10 test datasets of 5000 records -Intel Core I7 3.60 GHZ, RAM 8GB, OS Ubuntu 14.04 -# Nodes in Input Layer: 41 -# Nodes in Output Layer: 5 -Batch Size:50 #Epoch:500 -Best Learning Rate:0.01 | DR: 98.88% FP: 10.04% Accuracy: 96.93% |
Javaid et al. [213] | DL Self-taught Learning (Offline) | NSL-KDD [438] | all 41 features | -125,973 training records -22,544 testing records -10-fold cross validation | 2-class TP: 88.39% 2-class Precision: 85.44% 2-class Recall: 95.95% 2-class F-measure: 90.4% |