A Covert Network Attack Detection Method Based on LSTM

In recent years, driven by economic and political interests, hackers have used social engineering methods and system vulnerabilities to invade the system, and have long been latent, constantly searching and stealing high-value information, this attack method is called covert network attack. The use of these advanced technologies and long-term latency strategies makes it impossible for traditional detection technologies to effectively detect, track, and analyze it. Concealed network attacks have long-term latency, periodic connections, and other characteristics. Long-term and short-term memory networks are used to analyze their network connections. Experiments show that this method improves the accuracy of hidden network attack recognition and reduces the false positive rate.

[1]  Mitsuaki Akiyama,et al.  Efficient Dynamic Malware Analysis Based on Network Behavior Using Deep Learning , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[2]  Aina Musdholifah,et al.  The Implementation of Genetic Algorithm in Smote (Synthetic Minority Oversampling Technique) for Handling Imbalanced Dataset Problem , 2018, 2018 4th International Conference on Science and Technology (ICST).

[3]  Junho Choi,et al.  APT attack behavior pattern mining using the FP-growth algorithm , 2017, 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[4]  Abdullah Mubarak,et al.  Modelling an Adaptive e-Learning System Using LSTM and Random Forest Classification , 2018, 2018 IEEE Conference on e-Learning, e-Management and e-Services (IC3e).

[5]  Prawidya Destarianto,et al.  Imbalance Data Handling using Neighborhood Cleaning Rule (NCL) Sampling Method for Precision Student Modeling , 2019, 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE).

[6]  Prapa Rattadilok,et al.  Towards using transfer learning for Botnet Detection , 2017, 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST).

[7]  Wen-Guey Tzeng,et al.  Effective Botnet Detection Through Neural Networks on Convolutional Features , 2018, 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).

[8]  Ali H. Mirza,et al.  Computer network intrusion detection using sequential LSTM Neural Networks autoencoders , 2018, 2018 26th Signal Processing and Communications Applications Conference (SIU).