Therefore, it is necessary to perform high-precision anomaly detection on unlabeled samples. This paper proposes a traffic anomaly detection model using K-means and Active Learning Method (ALM), which is mainly composed of a feature extraction module and an anomaly detection module. Firstly, the ...
Traffic anomaly detection using k-means clustering Early Internet architecture design goals did not put security as a high priority. However, today Internet security is a quickly growing concern. The preval... G Münz,S Li,G Carle - IEEE 被引量: 157发表: 2012年 Intrusion detection based on ...
used a K-means clustering algorithm to perform anomaly detection in network traffic data (Münz, Li, & Carle, 2007). They C-LSTM neural network The proposed C-LSTM consists of CNN and LSTM layers, and is connected in a linear structure (Zhou, Sun, Liu, & Lau, 2015). Fig. 6 ...
In this paper, we present a statistical analysis of six traffic features based on entropy and distinct feature number at the packet level, and we find that, although these traffic features are unstable and show seaso...
Traditional intrusion detection systems are hasty in the sense that they use a set of signatures, which raise at the same rate as new technique are discovered, to identify malicious traffic patterns. Anomaly detection systems are another branch of intrusion detection systems that act more proactively...
In general, the traffic anomaly is detected using a threshold in network traffic management. 通常, 在网络流量管理中使用阈值来检测流量异常. 互联网 And classifying network traffic anomalies is realized in the experimental environment of campus networks. 该方法综合运用子空间方法和k-means 分类方法,并以...
Traffic Anomaly Detection and Annotation Using Hybridization of Deep Learning Method and Haar Cascade Classifier doi:10.1007/978-3-031-70789-6_20The increase in population in most countries has raised the issue of effective traffic monitoring and control. Nowadays, analyzing urban traffic conditions is...
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And the attentional mechanism can exactly solve a difficult problem, attention mechanism is a resource alloca- tion scheme that is the main means to solve the problem of information overload. The rational and effective utilization of computing resources enables the detection model to focus on the ...
detection efficiency. It is challenging to capture potential patterns in complex data effectively and cannot fully meet the needs of practical applications. To address these challenges, this paper proposes an enhanced anomaly traffic detection framework using bidirectional generative adversarial networks (BiG...