和异常检测类似的topic有,novelty detection, out of distribution, defect detection, 方法都是类似的,但是定义的问题是不一样的,其中难度最大的就是异常检测,可以从这些类似的topics中找到灵感进行求解。 我目前总结的一些写论文的思路,这里放出来,欢迎大家一起来讨论: 特定的数据结构,通过大量实验发现出有用的检测...
INTRUSION detection systems (Computer security)ANOMALY detection (Computer security)BOOSTING algorithmsTELECOMMUNICATION systemsIn recent years, the internet has not only enhanced the quality of our lives but also made us susceptible to high‐frequency cyber‐attacks on communication networks. Detect...
subscriberId=projects/<id>/subscriptions/<id>,tableSpec=project:dataset.transactions,outlierTableSpec=project:dataset.fraud_prediction,tempLocation=gs://<bucket>/temp,inputFilePattern=gs://df-ml-anomaly-detection-mock-data/finserv_fraud_detection/fraud_data_kaggle.json,modelId=<id>,versionId=<id>,...
[26] 2021 Artificial neural network (ANN), k-nearest neighbors (KNN), and a support vector machine (SVM) Fraud detection from credit cards data Kaggle dataset Accuracy, Precision, Recall 93.49%, 97.43%, 89.76% Did not focus to overcome the imbalanced data and performances are not enough. [...
Ding, W.; Abdel-Basset, M.; Mohamed, R. DeepAK-IoT: An effective deep learning model for cyberattack detection in IoT networks.Inf. Sci.2023,634, 157–171. [Google Scholar] [CrossRef] Sharma, A.; Babbar, H. BoT-IoT: Detection of Attacks in IoT-Cybersecurity for Smart Transportation...
Network traffic anomaly detection is a key step in identifying and preventing network security threats. This study aims to construct a new deep-learning-based traffic anomaly detection model through in-depth research on new feature-engineering methods, significantly improving the efficiency and accuracy ...
Network anomaly detection with stochastically improved autoencoder based models. In Proceedings of the 2017 IEEE 4th international conference on cyber security and cloud computing (CSCloud), New York, NY, USA, 26–28 June 2017; pp. 193–198. [Google Scholar] Lin, K.; Sheng, S.; Zhou, Y...
Among the 29 challenge winning solutions published at Kaggle's blog during 2015, 17 solutions employed XGBoost. However, its application in the field of intrusion detection has not been seen before, and this study applies XGBoost to perform classification on a contemporary dataset. 4.2.4. Attack ...
An improved LSTM model by adding a “correlation gate” as RLSTM to obtain the final outcome, which is adaptable to anomaly detection in a long time span. (4) The experiments were conducted on two separate sets of data. Network traffic detection data are extracted from Kaggle and China Big...
Anomaly Detection: A Survey. In Lecture Notes in Networks and Systems; ACM: New York, NY, USA, 2022; pp. 391–401. [Google Scholar] Larriva-Novo, X.A.; Vega-Barbas, M.; Villagra, V.A.; Sanz Rodrigo, M. Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to...