DRnet: Dynamic Retraining for Malicious Traffic Small-Sample Incremental Learning
نویسندگان
چکیده
Deep learning has achieved good classification results in the field of traffic recent years due to its feature representation ability. However, existing technology cannot meet requirements for incremental tasks online scenarios. In addition, high concealment and fast update speed malicious traffic, number labeled samples that can be captured is scarce, small drive neural network training, resulting poor performance model. Therefore, this paper proposes an method small-sample classification. The uses pruning strategy find redundant structure dynamically allocates neurons training based on proposed measurement according difficulty new class. This enables perform without excessively consuming storage computing resources, reasonable allocation improves accuracy classes. At same time, through knowledge transfer method, model reduce catastrophic forgetting old class, relieve pressure large parameters with data, improve performance. Experiments involving multiple datasets settings show our superior established baseline terms accuracy, 50% less memory.
منابع مشابه
Machine Learning Classification of Malicious Network Traffic
1.1. Intrusion Detection Systems. In our society, information systems are everywhere. They are used by corporations to store proprietary and other sensitive data, by families to store financial and personal information, by universities to keep research data and ideas, and by governments to store defense and security information. It is very important that the information systems that house this ...
متن کاملIncremental Learning With Sample Queries
The classical theory of pattern recognition assumes labeled examples appear according to unknown underlying class conditional probability distributions where the pattern classes are picked randomly in a passive manner according to their a priori probabilities. This paper presents experimental results for an incremental nearest-neighbor learning algorithm which actively selects samples from diff...
متن کاملLearning Invariant Representation for Malicious Network Traffic Detection
Statistical learning theory relies on an assumption that the joint distributions of observations and labels are the same in training and testing data. However, this assumption is violated in many real world problems, such as training a detector of malicious network traffic that can change over time as a result of attacker’s detection evasion efforts. We propose to address this problem by creati...
متن کاملDynamic class imbalance learning for incremental LPSVM
Linear Proximal Support Vector Machines (LPSVMs), like decision trees, classic SVM, etc. are originally not equipped to handle drifting data streams that exhibit high and varying degrees of class imbalance. For online classification of data streams with imbalanced class distribution, we propose a dynamic class imbalance learning (DCIL) approach to incremental LPSVM (IncLPSVM) modeling. In doing...
متن کاملIncremental Learning Algorithm for Dynamic Data Streams
The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Examples include sensor networks, web logs, and computer network traffic. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronics
سال: 2023
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics12122668