├── LICENSE ├── MemStream.png ├── README.md ├── code ├── memstream-ib.py ├── memstream-knn.py ├── memstream-pca.py ├── memstream-syn.py └── memstream.py └── data ├── conceptdriftdata.txt ├── syn.txt └── synlabel.txt /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /MemStream.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Stream-AD/MemStream/59b5ba0eb5392bd1bd13822dd4023fa8236c8cc3/MemStream.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MemStream 2 | 3 |

4 | 5 | 6 | 7 | 8 |

9 | 10 | Implementation of 11 | 12 | - [MemStream: Memory-Based Streaming Anomaly Detection](https://dl.acm.org/doi/pdf/10.1145/3485447.3512221). *Siddharth Bhatia, Arjit Jain, Shivin Srivastava, Kenji Kawaguchi, Bryan Hooi*. The Web Conference (formerly WWW), 2022. 13 | 14 | MemStream detects anomalies from a multi-aspect data stream. We output an anomaly score for each record. MemStream is a memory augmented feature extractor, allows for quick retraining, gives a theoretical bound on the memory size for effective drift handling, is robust to memory poisoning, and outperforms 11 state-of-the-art streaming anomaly detection baselines. 15 | 16 | ![](MemStream.png) 17 | After an initial training of the feature extractor on a small subset of normal data, MemStream processes records in two steps: (i) It outputs anomaly scores for each record by querying the memory for K-nearest neighbours to the record encoding and calculating a discounted distance and (ii) It updates the memory, in a FIFO manner, if the anomaly score is within an update threshold β. 18 | 19 | 20 | ## Demo 21 | 22 | 1. KDDCUP99: Run `python3 memstream.py --dataset KDD --beta 1 --memlen 256` 23 | 2. NSL-KDD: Run `python3 memstream.py --dataset NSL --beta 0.1 --memlen 2048` 24 | 3. UNSW-NB 15: Run `python3 memstream.py --dataset UNSW --beta 0.1 --memlen 2048` 25 | 4. CICIDS-DoS: Run `python3 memstream.py --dataset DOS --beta 0.1 --memlen 2048` 26 | 5. SYN: Run `python3 memstream-syn.py --dataset SYN --beta 1 --memlen 16` 27 | 6. Ionosphere: Run `python3 memstream.py --dataset ionosphere --beta 0.001 --memlen 4` 28 | 7. Cardiotocography: Run `python3 memstream.py --dataset cardio --beta 1 --memlen 64` 29 | 8. Statlog Landsat Satellite: Run `python3 memstream.py --dataset statlog --beta 0.01 --memlen 32` 30 | 9. Satimage-2: Run `python3 memstream.py --dataset satimage-2 --beta 10 --memlen 256` 31 | 10. Mammography: Run `python3 memstream.py --dataset mammography --beta 0.1 --memlen 128` 32 | 11. Pima Indians Diabetes: Run `python3 memstream.py --dataset pima --beta 0.001 --memlen 64` 33 | 12. Covertype: Run `python3 memstream.py --dataset cover --beta 0.0001 --memlen 2048` 34 | 35 | 36 | ## Command line options 37 | * `--dataset`: The dataset to be used for training. Choices 'NSL', 'KDD', 'UNSW', 'DOS'. (default 'NSL') 38 | * `--beta`: The threshold beta to be used. (default: 0.1) 39 | * `--memlen`: The size of the Memory Module (default: 2048) 40 | * `--dev`: Pytorch device to be used for training like "cpu", "cuda:0" etc. (default: 'cuda:0') 41 | * `--lr`: Learning rate (default: 0.01) 42 | * `--epochs`: Number of epochs (default: 5000) 43 | 44 | ## Input file format 45 | MemStream expects the input multi-aspect record stream to be stored in a contains `,` separated file. 46 | 47 | ## Datasets 48 | Processed Datasets can be downloaded from [here](https://drive.google.com/file/d/1JNrhOr8U3Nqef1hBOqvHQPzBNWzDOFdl/view). Please unzip and place the files in the data folder of the repository. 49 | 50 | 1. [KDDCUP99](http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html) 51 | 2. [NSL-KDD](https://www.unb.ca/cic/datasets/nsl.html) 52 | 3. [UNSW-NB 15](https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/) 53 | 4. [CICIDS-DoS](https://www.unb.ca/cic/datasets/ids-2018.html) 54 | 5. Synthetic Dataset (Introduced in paper) 55 | 6. [Ionosphere](https://archive.ics.uci.edu/ml/index.php) 56 | 7. [Cardiotocography](https://archive.ics.uci.edu/ml/index.php) 57 | 8. [Statlog Landsat Satellite](https://archive.ics.uci.edu/ml/index.php) 58 | 9. [Satimage-2](http://odds.cs.stonybrook.edu) 59 | 10. [Mammography](http://odds.cs.stonybrook.edu) 60 | 11. [Pima Indians Diabetes](https://archive.ics.uci.edu/ml/index.php) 61 | 12. [Covertype](https://archive.ics.uci.edu/ml/index.php) 62 | 63 | ## Environment 64 | This code has been tested on Debian GNU/Linux 9 with a 12GB Nvidia GeForce RTX 2080 Ti GPU, CUDA Version 10.2 and PyTorch 1.5. 65 | 66 | ## Citation 67 | 68 | If you use this code for your research, please consider citing our WWW paper. 69 | 70 | ```bibtex 71 | @inproceedings{bhatia2022memstream, 72 | title={MemStream: Memory-Based Streaming Anomaly Detection}, 73 | author={Siddharth Bhatia and Arjit Jain and Shivin Srivastava and Kenji Kawaguchi and Bryan Hooi}, 74 | booktitle={The Web Conference (formerly WWW)}, 75 | year={2022} 76 | } 77 | 78 | -------------------------------------------------------------------------------- /code/memstream-ib.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | import numpy as np 5 | import math 6 | import time 7 | import matplotlib.pyplot as plt 8 | from torch.utils.data import DataLoader 9 | from sklearn import metrics 10 | import scipy.spatial as sp 11 | from torch.autograd import Variable 12 | import argparse 13 | parser = argparse.ArgumentParser() 14 | parser.add_argument('--dataset', default='NSL') 15 | parser.add_argument('--beta', type=float, default=1e-3) 16 | parser.add_argument('--dim', type=int, default=12) 17 | parser.add_argument("--dev", help="device", default="cpu") 18 | parser.add_argument("--epochs", type=int, help="number of epochs for ib", default=5000) 19 | parser.add_argument("--lr", type=float, help="learning rate", default=1e-2) 20 | parser.add_argument("--memlen", type=int, help="size of memory", default=512) 21 | parser.add_argument("--ibbeta", type=float, help="beta value of IB", default=0.5) 22 | parser.add_argument("--seed", type=int, help="random seed", default=0) 23 | args = parser.parse_args() 24 | 25 | nfile = None 26 | lfile = None 27 | if args.dataset == 'NSL': 28 | nfile = '../data/nsl.txt' 29 | lfile = '../data/nsllabel.txt' 30 | elif args.dataset == 'KDD': 31 | nfile = '../data/kdd.txt' 32 | lfile = '../data/kddlabel.txt' 33 | elif args.dataset == 'UNSW': 34 | nfile = '../data/unsw.txt' 35 | lfile = '../data/unswlabel.txt' 36 | elif args.dataset == 'DOS': 37 | nfile = '../data/dos.txt' 38 | lfile = '../data/doslabel.txt' 39 | else: 40 | df = scipy.io.loadmat('../data/'+args.dataset+".mat") 41 | numeric = torch.FloatTensor(df['X']) 42 | labels = (df['y']).astype(float).reshape(-1) 43 | 44 | 45 | device = torch.device(args.dev) 46 | 47 | def compute_distances(x): 48 | x_norm = (x ** 2).sum(1).view(-1, 1) 49 | x_t = torch.transpose(x, 0, 1) 50 | x_t_norm = x_norm.view(1, -1) 51 | dist = x_norm + x_t_norm - 2.0 * torch.mm(x, x_t) 52 | dist = torch.clamp(dist, 0, np.inf) 53 | 54 | return dist 55 | 56 | 57 | def KDE_IXT_estimation(logvar_t, mean_t): 58 | n_batch, d = mean_t.shape 59 | var = torch.exp(logvar_t) + 1e-10 # to avoid 0's in the log 60 | normalization_constant = math.log(n_batch) 61 | dist = compute_distances(mean_t) 62 | distance_contribution = -torch.mean(torch.logsumexp(input=-0.5 * dist / var, dim=1)) 63 | I_XT = normalization_constant + distance_contribution 64 | 65 | return I_XT 66 | 67 | 68 | def get_IXT(mean_t, logvar_t): 69 | IXT = KDE_IXT_estimation(logvar_t, mean_t) # in natts 70 | IXT = IXT / np.log(2) # in bits 71 | return IXT 72 | 73 | 74 | def get_ITY(logits_y, y): 75 | HY_given_T = ce(logits_y, y) 76 | ITY = (np.log(2) - HY_given_T) / np.log(2) # in bits 77 | return ITY 78 | 79 | 80 | def get_loss(IXT_upper, ITY_lower): 81 | loss = -1.0 * (ITY_lower - args.ibbeta * IXT_upper) 82 | return loss 83 | 84 | ce = torch.nn.BCEWithLogitsLoss() 85 | numeric = torch.FloatTensor(np.loadtxt(nfile, delimiter = ',')) 86 | labels = np.loadtxt(lfile, delimiter=',') 87 | if args.dataset == 'KDD': 88 | labels = 1 - labels 89 | inputdim = numeric.shape[1] 90 | 91 | class AutoEncoder(nn.Module): 92 | def __init__(self): 93 | super(AutoEncoder, self).__init__() 94 | self.e1 = nn.Linear(inputdim, args.dim) 95 | self.output_layer = nn.Linear(args.dim, 1) 96 | 97 | def forward(self, x): 98 | mu = self.e1(x) 99 | intermed = mu + torch.randn_like(mu) * 1 100 | x = self.output_layer(intermed) 101 | return x, mu 102 | 103 | 104 | class MemStream(nn.Module): 105 | def __init__(self, in_dim, params): 106 | super(MemStream, self).__init__() 107 | self.params = params 108 | self.in_dim = in_dim 109 | self.out_dim = params['code_len'] 110 | self.memory_len = params['memory_len'] 111 | self.max_thres = torch.tensor(params['beta']).to(device) 112 | self.memory = torch.randn(self.memory_len, self.out_dim).to(device) 113 | self.mem_data = torch.randn(self.memory_len, self.in_dim).to(device) 114 | self.mem_idx = torch.from_numpy(np.arange(self.memory_len)) 115 | self.memory.requires_grad = False 116 | self.mem_data.requires_grad = False 117 | self.mem_idx.requires_grad = False 118 | self.batch_size = params['memory_len'] 119 | self.num_mem_update = 0 120 | self.ae = AutoEncoder().to(device) 121 | self.clock = 0 122 | self.last_update = -1 123 | self.updates = [] 124 | self.optimizer = torch.optim.Adam(self.parameters(), lr=params['lr']) 125 | self.loss_fn = nn.MSELoss() 126 | self.count = 0 127 | 128 | 129 | def train_autoencoder(self, data, epochs, labels): 130 | self.mean, self.std = self.mem_data.mean(0), self.mem_data.std(0) 131 | new = (data - self.mean) / self.std 132 | new[:, self.std == 0] = 0 133 | new = Variable(new) 134 | train_y = Variable(labels).to(device) 135 | logvar_t = torch.Tensor([0]).to(device) 136 | for epoch in range(args.epochs): 137 | self.optimizer.zero_grad() 138 | train_logits_y, train_mean_t = self.ae(new) #new + 0.001*torch.randn_like(new).to(device) 139 | train_ITY = get_ITY(train_logits_y, train_y) 140 | train_IXT = get_IXT(train_mean_t, logvar_t) 141 | loss = get_loss(train_IXT, train_ITY) 142 | loss.backward() 143 | self.optimizer.step() 144 | 145 | 146 | def update_memory(self, output_loss, encoder_output, data): 147 | if output_loss <= self.max_thres: 148 | least_used_pos = torch.argmin(self.mem_idx) 149 | self.memory[least_used_pos] = encoder_output 150 | self.mem_data[least_used_pos] = data 151 | self.mean, self.std = self.mem_data.mean(0), self.mem_data.std(0) 152 | self.mem_idx[least_used_pos] = self.count 153 | self.count += 1 154 | self.num_mem_update += 1 155 | return 1 156 | return 0 157 | 158 | def initialize_memory(self, x): 159 | mean, std = model.mem_data.mean(0), model.mem_data.std(0) 160 | new = (x - mean) / std 161 | new[:, std == 0] = 0 162 | self.memory = self.ae.e1(new) 163 | self.memory.requires_grad = False 164 | self.mem_data = x 165 | 166 | def forward(self, x): 167 | new = (x - self.mean) / self.std 168 | new[:, self.std == 0] = 0 169 | encoder_output = self.ae.e1(new) 170 | loss_values = torch.norm(self.memory - encoder_output, dim=1, p=1).min() 171 | self.updates.append(self.update_memory(loss_values, encoder_output, x)) 172 | return loss_values 173 | 174 | 175 | torch.manual_seed(args.seed) 176 | N = args.memlen 177 | params = { 178 | 'beta': args.beta, 'code_len': args.dim, 'memory_len': N, 'batch_size':1, 'lr':args.lr 179 | } 180 | 181 | model = MemStream(numeric[0].shape[0],params).to(device) 182 | 183 | batch_size = params['batch_size'] 184 | print(args.dataset, args.beta, args.dim, args.memlen, args.lr, args.epochs) 185 | data_loader = DataLoader(numeric, batch_size=batch_size) 186 | init_data = numeric[labels == 0][:N].to(device) 187 | model.mem_data = init_data 188 | torch.set_grad_enabled(True) 189 | model.train_autoencoder(numeric[:N].to(device), epochs=args.epochs, labels=torch.Tensor(labels[:N].reshape(-1, 1))) 190 | torch.set_grad_enabled(False) 191 | model.initialize_memory(numeric[labels == 0][:N].to(device)) 192 | err = [] 193 | for data in data_loader: 194 | output = model(data.to(device)) 195 | err.append(output) 196 | scores = np.array([i.cpu() for i in err]) 197 | auc = metrics.roc_auc_score(labels, scores) 198 | print("ROC-AUC", auc) 199 | -------------------------------------------------------------------------------- /code/memstream-knn.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | import numpy as np 5 | import time 6 | import matplotlib.pyplot as plt 7 | from torch.utils.data import DataLoader 8 | from sklearn import metrics 9 | import scipy.spatial as sp 10 | from torch.autograd import Variable 11 | import argparse 12 | import scipy.io 13 | 14 | parser = argparse.ArgumentParser() 15 | parser.add_argument('--dataset', default='NSL') 16 | parser.add_argument('--beta', type=float, default=0.1) 17 | parser.add_argument("--dev", help="device", default="cuda:0") 18 | parser.add_argument("--epochs", type=int, help="number of epochs for ae", default=5000) 19 | parser.add_argument("--lr", type=float, help="learning rate", default=1e-2) 20 | parser.add_argument("--memlen", type=int, help="size of memory", default=2048) 21 | parser.add_argument("--seed", type=int, help="random seed", default=0) 22 | parser.add_argument("--gamma", type=float, help="knn coefficient", default=0) 23 | args = parser.parse_args() 24 | 25 | torch.manual_seed(args.seed) 26 | nfile = None 27 | lfile = None 28 | if args.dataset == 'NSL': 29 | nfile = '../data/nsl.txt' 30 | lfile = '../data/nsllabel.txt' 31 | elif args.dataset == 'KDD': 32 | nfile = '../data/kdd.txt' 33 | lfile = '../data/kddlabel.txt' 34 | elif args.dataset == 'UNSW': 35 | nfile = '../data/unsw.txt' 36 | lfile = '../data/unswlabel.txt' 37 | elif args.dataset == 'DOS': 38 | nfile = '../data/dos.txt' 39 | lfile = '../data/doslabel.txt' 40 | else: 41 | df = scipy.io.loadmat('../data/'+args.dataset+".mat") 42 | numeric = torch.FloatTensor(df['X']) 43 | labels = (df['y']).astype(float).reshape(-1) 44 | 45 | device = torch.device(args.dev) 46 | 47 | class MemStream(nn.Module): 48 | def __init__(self, in_dim, params): 49 | super(MemStream, self).__init__() 50 | self.params = params 51 | self.in_dim = in_dim 52 | self.out_dim = in_dim*2 53 | self.memory_len = params['memory_len'] 54 | self.gamma = params['gamma'] 55 | self.max_thres = torch.tensor(params['beta']).to(device) 56 | self.memory = torch.randn(self.memory_len, self.out_dim).to(device) 57 | self.mem_data = torch.randn(self.memory_len, self.in_dim).to(device) 58 | self.memory.requires_grad = False 59 | self.mem_data.requires_grad = False 60 | self.batch_size = params['memory_len'] 61 | self.num_mem_update = 0 62 | self.encoder = nn.Sequential( 63 | nn.Linear(self.in_dim, self.out_dim), 64 | nn.Tanh(), 65 | ).to(device) 66 | self.decoder = nn.Sequential( 67 | nn.Linear(self.out_dim, self.in_dim) 68 | ).to(device) 69 | self.clock = 0 70 | self.last_update = -1 71 | self.optimizer = torch.optim.Adam(self.parameters(), lr=params['lr']) 72 | self.loss_fn = nn.MSELoss() 73 | self.count = 0 74 | self.K = 3 75 | self.exp = torch.Tensor([self.gamma**i for i in range(self.K)]).to(device) 76 | 77 | 78 | def train_autoencoder(self, data, epochs): 79 | self.mean, self.std = self.mem_data.mean(0), self.mem_data.std(0) 80 | new = (data - self.mean) / self.std 81 | new[:, self.std == 0] = 0 82 | new = Variable(new) 83 | for epoch in range(epochs): 84 | self.optimizer.zero_grad() 85 | output = self.decoder(self.encoder(new + 0.001*torch.randn_like(new).to(device))) 86 | loss = self.loss_fn(output, new) 87 | loss.backward() 88 | self.optimizer.step() 89 | 90 | 91 | def update_memory(self, output_loss, encoder_output, data): 92 | if output_loss <= self.max_thres: 93 | least_used_pos = self.count%self.memory_len 94 | self.memory[least_used_pos] = encoder_output 95 | self.mem_data[least_used_pos] = data 96 | self.mean, self.std = self.mem_data.mean(0), self.mem_data.std(0) 97 | self.count += 1 98 | return 1 99 | return 0 100 | 101 | def initialize_memory(self, x): 102 | mean, std = model.mem_data.mean(0), model.mem_data.std(0) 103 | new = (x - mean) / std 104 | new[:, std == 0] = 0 105 | self.memory = self.encoder(new) 106 | self.memory.requires_grad = False 107 | self.mem_data = x 108 | 109 | def forward(self, x): 110 | new = (x - self.mean) / self.std 111 | new[:, self.std == 0] = 0 112 | encoder_output = self.encoder(new) 113 | # loss_values = torch.norm(self.memory - encoder_output, dim=1, p=1).min() 114 | loss_values = (torch.topk(torch.norm(self.memory - encoder_output, dim=1, p=1), k=self.K, largest=False)[0]*self.exp).sum()/self.exp.sum() 115 | self.update_memory(loss_values, encoder_output, x) 116 | return loss_values 117 | 118 | if args.dataset in ['KDD', 'NSL', 'UNSW', 'DOS']: 119 | numeric = torch.FloatTensor(np.loadtxt(nfile, delimiter = ',')) 120 | labels = np.loadtxt(lfile, delimiter=',') 121 | 122 | if args.dataset == 'KDD': 123 | labels = 1 - labels 124 | torch.manual_seed(args.seed) 125 | N = args.memlen 126 | params = { 127 | 'beta': args.beta, 'memory_len': N, 'batch_size':1, 'lr':args.lr, 'gamma':args.gamma 128 | } 129 | 130 | model = MemStream(numeric[0].shape[0],params).to(device) 131 | 132 | batch_size = params['batch_size'] 133 | print(args.dataset, args.beta, args.gamma, args.memlen, args.lr, args.epochs) 134 | data_loader = DataLoader(numeric, batch_size=batch_size) 135 | init_data = numeric[labels == 0][:N].to(device) 136 | model.mem_data = init_data 137 | torch.set_grad_enabled(True) 138 | model.train_autoencoder(Variable(init_data).to(device), epochs=args.epochs) 139 | torch.set_grad_enabled(False) 140 | model.initialize_memory(Variable(init_data[:N])) 141 | err = [] 142 | for data in data_loader: 143 | output = model(data.to(device)) 144 | err.append(output) 145 | scores = np.array([i.cpu() for i in err]) 146 | auc = metrics.roc_auc_score(labels, scores) 147 | print("ROC-AUC", auc) 148 | -------------------------------------------------------------------------------- /code/memstream-pca.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import time 3 | from sklearn.decomposition import PCA 4 | import matplotlib.pyplot as plt 5 | from sklearn import metrics 6 | import scipy.spatial as sp 7 | np.seterr(divide="ignore", invalid="ignore") 8 | import argparse 9 | parser = argparse.ArgumentParser() 10 | parser.add_argument('--dataset', default='NSL') 11 | parser.add_argument('--beta', type=float, default=1e-3) 12 | parser.add_argument('--dim', type=int, default=12) 13 | parser.add_argument("--memlen", type=int, help="size of memory", default=512) 14 | args = parser.parse_args() 15 | 16 | nfile = None 17 | lfile = None 18 | if args.dataset == 'NSL': 19 | nfile = '../data/nsl.txt' 20 | lfile = '../data/nsllabel.txt' 21 | elif args.dataset == 'KDD': 22 | nfile = '../data/kdd.txt' 23 | lfile = '../data/kddlabel.txt' 24 | elif args.dataset == 'UNSW': 25 | nfile = '../data/unsw.txt' 26 | lfile = '../data/unswlabel.txt' 27 | elif args.dataset == 'DOS': 28 | nfile = '../data/dos.txt' 29 | lfile = '../data/doslabel.txt' 30 | else: 31 | df = scipy.io.loadmat('../data/'+args.dataset+".mat") 32 | numeric = torch.FloatTensor(df['X']) 33 | labels = (df['y']).astype(float).reshape(-1) 34 | 35 | 36 | class MemStream(): 37 | def __init__(self, in_dim, params): 38 | super(MemStream, self).__init__() 39 | self.params = params 40 | self.in_dim = in_dim 41 | self.out_dim = params['code_len'] 42 | self.memory_len = params['memory_len'] 43 | self.max_thres = params['beta'] 44 | self.memory = np.random.randn(self.memory_len, self.out_dim) 45 | self.mem_data = np.random.randn(self.memory_len, self.in_dim) 46 | self.mem_idx = np.arange(self.memory_len) 47 | self.batch_size = params['memory_len'] 48 | self.num_mem_update = 0 49 | self.pca = PCA(n_components=args.dim) 50 | self.clock = 0 51 | self.last_update = -1 52 | self.updates = [] 53 | self.count = 0 54 | 55 | 56 | def train_autoencoder(self, data): 57 | self.mean, self.std = self.mem_data.mean(0), self.mem_data.std(0) 58 | new = (data - self.mean) / self.std 59 | new[:, self.std == 0] = 0 60 | self.pca.fit(new) 61 | 62 | 63 | def update_memory(self, output_loss, encoder_output, data): 64 | if output_loss <= self.max_thres: 65 | least_used_pos = np.argmin(self.mem_idx) 66 | self.memory[least_used_pos] = encoder_output 67 | self.mem_data[least_used_pos] = data 68 | self.mean, self.std = self.mem_data.mean(0), self.mem_data.std(0) 69 | self.mem_idx[least_used_pos] = self.count 70 | self.count += 1 71 | self.num_mem_update += 1 72 | return 1 73 | return 0 74 | 75 | def initialize_memory(self, x): 76 | mean, std = model.mem_data.mean(0), model.mem_data.std(0) 77 | new = (x - mean) / std 78 | new[:, std == 0] = 0 79 | self.memory = self.pca.transform(new) 80 | self.mem_data = x 81 | 82 | def forward(self, x): 83 | new = (x - self.mean) / self.std 84 | new[:, self.std == 0] = 0 85 | encoder_output = self.pca.transform(new) 86 | loss_values = np.linalg.norm(self.memory - encoder_output, axis=1, ord=1).min() 87 | self.updates.append(self.update_memory(loss_values, encoder_output, x)) 88 | return loss_values 89 | 90 | 91 | numeric = np.loadtxt(nfile, delimiter = ',') 92 | labels = np.loadtxt(lfile, delimiter=',') 93 | if args.dataset == 'KDD': 94 | labels = 1 - labels 95 | np.random.seed(0) 96 | N = args.memlen 97 | params = { 98 | 'beta': args.beta, 'code_len': args.dim, 'memory_len': N, 'batch_size':1 99 | } 100 | 101 | model = MemStream(numeric[0].shape[0],params) 102 | 103 | batch_size = params['batch_size'] 104 | print(args.dataset, args.beta, args.dim, args.memlen) 105 | init_data = numeric[labels == 0][:N] 106 | model.mem_data = init_data 107 | model.train_autoencoder(init_data) 108 | model.initialize_memory(init_data[:N]) 109 | err = [] 110 | for i in range(numeric.shape[0]): 111 | output = model.forward(numeric[i:i+1]) 112 | err.append(output) 113 | scores = np.array(err) 114 | auc = metrics.roc_auc_score(labels, scores) 115 | print("ROC-AUC", auc) 116 | -------------------------------------------------------------------------------- /code/memstream-syn.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | import numpy as np 5 | import time 6 | import matplotlib.pyplot as plt 7 | from torch.utils.data import DataLoader 8 | from sklearn import metrics 9 | import scipy.spatial as sp 10 | from torch.autograd import Variable 11 | import argparse 12 | parser = argparse.ArgumentParser() 13 | parser.add_argument('--dataset', default='SYN') 14 | parser.add_argument('--beta', type=float, default=1) 15 | parser.add_argument('--seed', type=int, default=0) 16 | parser.add_argument("--dev", help="device", default="cpu") 17 | parser.add_argument("--epochs", type=int, help="number of epochs for ae", default=5000) 18 | parser.add_argument("--lr", type=float, help="learning rate", default=1e-2) 19 | parser.add_argument("--memlen", type=int, help="size of memory", default=16) 20 | args = parser.parse_args() 21 | 22 | nfile = None 23 | lfile = None 24 | if args.dataset == 'SYN': 25 | nfile = '../data/syn.txt' 26 | lfile = '../data/synlabel.txt' 27 | 28 | device = torch.device(args.dev) 29 | 30 | class MemStream(nn.Module): 31 | def __init__(self, in_dim, params): 32 | super(MemStream, self).__init__() 33 | self.params = params 34 | self.in_dim = in_dim 35 | self.out_dim = 1 36 | self.memory_len = params['memory_len'] 37 | self.max_thres = torch.tensor(params['beta']).to(device) 38 | self.memory = torch.randn(self.memory_len, self.out_dim).to(device) 39 | self.mem_data = torch.randn(self.memory_len, self.in_dim).to(device) 40 | self.mem_idx = torch.from_numpy(np.arange(self.memory_len)) 41 | self.memory.requires_grad = False 42 | self.mem_data.requires_grad = False 43 | self.mem_idx.requires_grad = False 44 | self.batch_size = params['memory_len'] 45 | self.num_mem_update = 0 46 | self.encoder = nn.Identity().to(device) 47 | self.decoder = nn.Identity().to(device) 48 | self.clock = 0 49 | self.last_update = -1 50 | self.updates = [] 51 | self.loss_fn = nn.MSELoss() 52 | self.count = 0 53 | 54 | 55 | def train_autoencoder(self, data, epochs): 56 | self.mean, self.std = self.mem_data.mean(0), self.mem_data.std(0) 57 | 58 | def update_memory(self, output_loss, encoder_output, data): 59 | if output_loss <= self.max_thres: 60 | least_used_pos = torch.argmin(self.mem_idx) 61 | self.memory[least_used_pos] = encoder_output 62 | self.mem_data[least_used_pos] = data 63 | self.mean, self.std = self.mem_data.mean(0), self.mem_data.std(0) 64 | self.mem_idx[least_used_pos] = self.count 65 | self.count += 1 66 | self.num_mem_update += 1 67 | return 1 68 | return 0 69 | 70 | def initialize_memory(self, x): 71 | mean, std = model.mem_data.mean(0), model.mem_data.std(0) 72 | new = (x - mean) / std 73 | new[:, std == 0] = 0 74 | self.memory = self.encoder(new) 75 | self.memory.requires_grad = False 76 | self.mem_data = x 77 | 78 | def forward(self, x): 79 | new = (x - self.mean) / self.std 80 | new[:, self.std == 0] = 0 81 | encoder_output = self.encoder(new) 82 | loss_values = torch.norm(self.memory - encoder_output, dim=1, p=1).min() 83 | self.updates.append(self.update_memory(loss_values, encoder_output, x)) 84 | return loss_values 85 | 86 | 87 | numeric = torch.FloatTensor(np.loadtxt(nfile, delimiter = ',')).reshape(-1, 1) 88 | labels = np.loadtxt(lfile, delimiter=',') 89 | torch.manual_seed(args.seed) 90 | N = args.memlen 91 | params = { 92 | 'beta': args.beta, 'memory_len': N, 'batch_size':1, 'lr':args.lr 93 | } 94 | 95 | model = MemStream(numeric[0].shape[0],params).to(device) 96 | model.max_thres=model.max_thres.float() 97 | batch_size = params['batch_size'] 98 | print(args.dataset, args.beta, args.memlen, args.lr, args.epochs) 99 | data_loader = DataLoader(numeric, batch_size=batch_size) 100 | init_data = numeric[labels == 0][:N].to(device) 101 | model.mem_data = init_data 102 | torch.set_grad_enabled(True) 103 | model.train_autoencoder(Variable(init_data).to(device), epochs=args.epochs) 104 | torch.set_grad_enabled(False) 105 | model.initialize_memory(Variable(init_data[:N])) 106 | err = [] 107 | for data in data_loader: 108 | output = model(data.to(device)) 109 | err.append(output) 110 | scores = np.array([i.cpu() for i in err]) 111 | auc = metrics.roc_auc_score(labels, scores) 112 | print("ROC-AUC", auc) 113 | -------------------------------------------------------------------------------- /code/memstream.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | import numpy as np 5 | import time 6 | import matplotlib.pyplot as plt 7 | from torch.utils.data import DataLoader 8 | from sklearn import metrics 9 | import scipy.spatial as sp 10 | from torch.autograd import Variable 11 | import argparse 12 | import scipy.io 13 | 14 | parser = argparse.ArgumentParser() 15 | parser.add_argument('--dataset', default='NSL') 16 | parser.add_argument('--beta', type=float, default=0.1) 17 | parser.add_argument("--dev", help="device", default="cuda:0") 18 | parser.add_argument("--epochs", type=int, help="number of epochs for ae", default=5000) 19 | parser.add_argument("--lr", type=float, help="learning rate", default=1e-2) 20 | parser.add_argument("--memlen", type=int, help="size of memory", default=2048) 21 | parser.add_argument("--seed", type=int, help="random seed", default=0) 22 | args = parser.parse_args() 23 | 24 | torch.manual_seed(args.seed) 25 | nfile = None 26 | lfile = None 27 | if args.dataset == 'NSL': 28 | nfile = '../data/nsl.txt' 29 | lfile = '../data/nsllabel.txt' 30 | elif args.dataset == 'KDD': 31 | nfile = '../data/kdd.txt' 32 | lfile = '../data/kddlabel.txt' 33 | elif args.dataset == 'UNSW': 34 | nfile = '../data/unsw.txt' 35 | lfile = '../data/unswlabel.txt' 36 | elif args.dataset == 'DOS': 37 | nfile = '../data/dos.txt' 38 | lfile = '../data/doslabel.txt' 39 | else: 40 | df = scipy.io.loadmat('../data/'+args.dataset+".mat") 41 | numeric = torch.FloatTensor(df['X']) 42 | labels = (df['y']).astype(float).reshape(-1) 43 | 44 | device = torch.device(args.dev) 45 | 46 | class MemStream(nn.Module): 47 | def __init__(self, in_dim, params): 48 | super(MemStream, self).__init__() 49 | self.params = params 50 | self.in_dim = in_dim 51 | self.out_dim = in_dim*2 52 | self.memory_len = params['memory_len'] 53 | self.max_thres = torch.tensor(params['beta']).to(device) 54 | self.memory = torch.randn(self.memory_len, self.out_dim).to(device) 55 | self.mem_data = torch.randn(self.memory_len, self.in_dim).to(device) 56 | self.memory.requires_grad = False 57 | self.mem_data.requires_grad = False 58 | self.batch_size = params['memory_len'] 59 | self.num_mem_update = 0 60 | self.encoder = nn.Sequential( 61 | nn.Linear(self.in_dim, self.out_dim), 62 | nn.Tanh(), 63 | ).to(device) 64 | self.decoder = nn.Sequential( 65 | nn.Linear(self.out_dim, self.in_dim) 66 | ).to(device) 67 | self.clock = 0 68 | self.last_update = -1 69 | self.optimizer = torch.optim.Adam(self.parameters(), lr=params['lr']) 70 | self.loss_fn = nn.MSELoss() 71 | self.count = 0 72 | 73 | 74 | def train_autoencoder(self, data, epochs): 75 | self.mean, self.std = self.mem_data.mean(0), self.mem_data.std(0) 76 | new = (data - self.mean) / self.std 77 | new[:, self.std == 0] = 0 78 | new = Variable(new) 79 | for epoch in range(epochs): 80 | self.optimizer.zero_grad() 81 | output = self.decoder(self.encoder(new + 0.001*torch.randn_like(new).to(device))) 82 | loss = self.loss_fn(output, new) 83 | loss.backward() 84 | self.optimizer.step() 85 | 86 | 87 | def update_memory(self, output_loss, encoder_output, data): 88 | if output_loss <= self.max_thres: 89 | least_used_pos = self.count%self.memory_len 90 | self.memory[least_used_pos] = encoder_output 91 | self.mem_data[least_used_pos] = data 92 | self.mean, self.std = self.mem_data.mean(0), self.mem_data.std(0) 93 | self.count += 1 94 | return 1 95 | return 0 96 | 97 | def initialize_memory(self, x): 98 | mean, std = model.mem_data.mean(0), model.mem_data.std(0) 99 | new = (x - mean) / std 100 | new[:, std == 0] = 0 101 | self.memory = self.encoder(new) 102 | self.memory.requires_grad = False 103 | self.mem_data = x 104 | 105 | def forward(self, x): 106 | new = (x - self.mean) / self.std 107 | new[:, self.std == 0] = 0 108 | encoder_output = self.encoder(new) 109 | loss_values = torch.norm(self.memory - encoder_output, dim=1, p=1).min() 110 | self.update_memory(loss_values, encoder_output, x) 111 | return loss_values 112 | 113 | if args.dataset in ['KDD', 'NSL', 'UNSW', 'DOS']: 114 | numeric = torch.FloatTensor(np.loadtxt(nfile, delimiter = ',')) 115 | labels = np.loadtxt(lfile, delimiter=',') 116 | 117 | if args.dataset == 'KDD': 118 | labels = 1 - labels 119 | torch.manual_seed(args.seed) 120 | N = args.memlen 121 | params = { 122 | 'beta': args.beta, 'memory_len': N, 'batch_size':1, 'lr':args.lr 123 | } 124 | 125 | model = MemStream(numeric[0].shape[0],params).to(device) 126 | 127 | batch_size = params['batch_size'] 128 | print(args.dataset, args.beta, args.memlen, args.lr, args.epochs) 129 | data_loader = DataLoader(numeric, batch_size=batch_size) 130 | init_data = numeric[labels == 0][:N].to(device) 131 | model.mem_data = init_data 132 | torch.set_grad_enabled(True) 133 | model.train_autoencoder(Variable(init_data).to(device), epochs=args.epochs) 134 | torch.set_grad_enabled(False) 135 | model.initialize_memory(Variable(init_data[:N])) 136 | err = [] 137 | for data in data_loader: 138 | output = model(data.to(device)) 139 | err.append(output) 140 | scores = np.array([i.cpu() for i in err]) 141 | auc = metrics.roc_auc_score(labels, scores) 142 | print("ROC-AUC", auc) 143 | -------------------------------------------------------------------------------- /data/synlabel.txt: -------------------------------------------------------------------------------- 1 | 0 2 | 0 3 | 0 4 | 0 5 | 0 6 | 0 7 | 0 8 | 0 9 | 0 10 | 0 11 | 0 12 | 0 13 | 0 14 | 0 15 | 0 16 | 0 17 | 0 18 | 0 19 | 0 20 | 0 21 | 0 22 | 0 23 | 0 24 | 0 25 | 0 26 | 0 27 | 0 28 | 0 29 | 0 30 | 0 31 | 0 32 | 0 33 | 0 34 | 0 35 | 0 36 | 0 37 | 1 38 | 0 39 | 0 40 | 0 41 | 0 42 | 0 43 | 0 44 | 0 45 | 0 46 | 1 47 | 0 48 | 0 49 | 0 50 | 1 51 | 0 52 | 0 53 | 0 54 | 0 55 | 0 56 | 0 57 | 1 58 | 0 59 | 0 60 | 0 61 | 0 62 | 0 63 | 0 64 | 0 65 | 1 66 | 0 67 | 1 68 | 0 69 | 0 70 | 0 71 | 1 72 | 0 73 | 0 74 | 0 75 | 0 76 | 0 77 | 1 78 | 0 79 | 0 80 | 0 81 | 0 82 | 0 83 | 0 84 | 0 85 | 0 86 | 1 87 | 0 88 | 0 89 | 0 90 | 0 91 | 0 92 | 1 93 | 0 94 | 1 95 | 0 96 | 0 97 | 0 98 | 0 99 | 0 100 | 0 101 | 0 102 | 0 103 | 0 104 | 0 105 | 0 106 | 0 107 | 0 108 | 0 109 | 1 110 | 0 111 | 0 112 | 0 113 | 0 114 | 0 115 | 0 116 | 0 117 | 0 118 | 0 119 | 0 120 | 0 121 | 0 122 | 0 123 | 0 124 | 0 125 | 0 126 | 0 127 | 0 128 | 0 129 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