Anomaly detection plays an essential role in network security and traffic classification. Many studies have focused on anomaly detection to improve network security, including machine learning and deep learning methods. These methods often require numerous samples and must obtain the results by classifying the entire data set, thereby limiting their inflexibility. Although transfer and multitask learning have achieved some results in the model’s transferability, these methods must manually label or reprocess the test set. These problems limit the application of previous methods in network security management. To solve these problems, we propose a transferable and adaptable network intrusion detection system (TA-NIDS) based on deep reinforcement learning. The interaction process between the agent and the environment varies every time. A small-scale data set can be used to produce many interactive processes. Therefore, robustness is guaranteed when there are few samples. Then, a reasonable reward function allows the agent to learn how to first choose outliers without classifying the entire data set. This makes the TA-NIDS more adaptable to the scene when we prioritize apparent outliers. More importantly, the original features are transformed into the state of the environment, so no requirement exists for the feature dimension. Furthermore, the general rather than the specific state of one data set makes the model transferable to other data sets. The experimental results for IDS2017, IDS2018, NSL-KDD, UNSW-NB15 and CIC-IoT2023 show that the proposed framework maintains good accuracy when prioritizing outliers and transferability are prioritized simultaneously.

IoT devices have been widely used in many fields, bringing many conveniences to people’s life. With the massive deployment and application of IoT devices, how to maintain the IoT from cyber-attacks has become one of the major concerns of researchers. Due to IoT devices’ limited computational capabilities and storage resources, IoT usually does not have sufficient security defense mechanisms, making it vulnerable to malware or device attacks. However, existing IoT-oriented intrusion detection systems usually only support the detection of specific malicious attacks or require complex models and massive computational resources to obtain high detection accuracy. We propose a lightweight and efficient intrusion detection method based on feature grouping to address the above challenges. We first design a fast protocol parsing method on the raw Packet Capture files to generate semantic-level parsing features. Then, we propose session merging and feature grouping methods. Finally, we verify the proposed features’ effectiveness and analyze the malicious attacks’ working process. The proposed method achieves more than 99.5% classification accuracy on three public IoT datasets. The proposed method requires significantly fewer computational resources than baseline methods in the protocol parsing and model training process. Experimental results show that the proposed method is lightweight, efficient, and extensible. Therefore, the proposed method is suitable for IoT intrusion detection.

We propose an attention-weighted model for parallel extraction of spatial-temporal features to enhance the detection capabilities in the message queuing telemetry transport protocol, widely used in the Internet of Things. Our approach involves constructing perception node collection graphs based on packet header information, which capture transmission-dependent and context-sequence-dependent relationships in the data streams. We leverage a message-passing mechanism to aggregate adjacent nodes and update the weight matrix accordingly. Additionally, we employ a bidirectional long short-term memory model to capture long-distance dependencies in the sequence. The updated graph and the output of the time-series model are fused and processed by a self-attention mechanism, generating weights for classification. The classification results are obtained using a fully connected network. We evaluate our approach on four datasets (ToN-IoT, BoT-IoT, UNSW-NB15, and DoHBrw2020) and compare it against nine different algorithms. Experimental results demonstrate the effectiveness of our method, achieving high accuracy levels, such as 0.8874 on ToN-IoT, 0.9386 on BoT-IoT, 0.9390 on DoHBrw2020, and the best accuracy of 0.8659 on the unbalanced UNSW-NB15 dataset.

With the increase of Internet visits and connections, it is becoming essential and arduous to protect the networks and different devices of the Internet of Things (IoT) from malicious attacks. The intrusion detection systems (IDSs) based on supervised machine learning (ML) methods require a large number of labeled samples. However, the number of abnormal behaviors is far less than that of normal behaviors, let alone that the shots of malicious behavior samples which can be intercepted as training dataset are actually limited. Consequently, it is a key research topic to conduct the anomaly detection for the small number of abnormal behavior samples. This paper proposes an anomaly detection model with a few abnormal samples to solve the problem in few-shot detection based on convolutional neural networks (CNN) and autoencoder (AE). This model mainly consists of the CNN-based supervised pretraining module and the AE-based data reconstruction module. Only a few abnormal samples are utilized to the pretrain module to build the structure of extracting deep features. The data reconstruction module simply chooses the deep features of normal samples as training data. There also exist some effective attention mechanisms in the pretraining module. Through the pretraining of small samples, the accuracy of abnormal detection is improved compared with merely training normal samples with AE. The simulation results prove that this solution can solve the above problems occurring in network behavior anomaly detection. In comparison to the original AE model and other clustering methods, the proposed model advances the detection results in a visible way.

Deep learning has been applied in the field of network intrusion detection and has yielded good results. In malicious network traffic classification tasks, many studies have achieved good performance with respect to the accuracy and recall rate of classification through self-designed models. In deep learning, the design of the model architecture greatly influences the results. However, the design of the network model architecture usually requires substantial professional knowledge. At present, the focus of research in the field of traffic monitoring is often directed elsewhere. Therefore, in the classification task of the network intrusion detection field, there is much room for improvement in the design and optimization of the model architecture. A neural architecture search (NAS) can automatically search the architecture of the model under the premise of a given optimization goal. For this reason, we propose a model that can perform NAS in the field of network traffic classification and search for the optimal architecture suitable for traffic detection based on the network traffic dataset. Each layer of our depth model is constructed according to the principle of maximum coding rate attenuation, which has strong consistency and symmetry in structure. Compared with some manually designed network architectures, classification indicators, such as Top-1 accuracy and F1 score, are also greatly improved while ensuring the lightweight nature of the model. In addition, we introduce a surrogate model in the search task. Compared to using the traditional NAS model to search the network traffic classification model, our NAS model greatly improves the search efficiency under the premise of ensuring that the results are not substantially different. We also manually adjust some operations in the search space of the architecture search to find a set of model operations that are more suitable for traffic classification. Finally, we apply the searched model to other traffic datasets to verify the universality of the model. Compared with several common network models in the traffic field, the searched model (NAS-Net) performs better, and the classification effect is more accurate.

In computer networks, Network Intrusion Detection System (NIDS) plays a very important role in identifying intrusion behaviors. NIDS can identify abnormal behaviors by analyzing network traffic. However, the performance of classifier is not very good in identifying abnormal traffic for minority classes. In order to improve the detection rate on class imbalanced dataset, we propose a network intrusion detection model based on two-layer CNN and Cluster-SMOTE + K-means algorithm (CSK-CNN) to process imbalanced dataset. CSK combines the cluster based Synthetic Minority Over Sampling Technique (Cluster-SMOTE) and K-means based under sampling algorithm. Through the two-layer network, abnormal traffic can not only be identified, but also be classified into specific attack types. This paper has been verified on UNSW-NB15 dataset and CICIDS2017 dataset, and the performance of the proposed model has been evaluated using such indicators as accuracy, recall, precision, F1-score, ROC curve, AUC value, training time and testing time. The experiment shows that the proposed CSK-CNN in this paper is obviously superior to other comparison algorithms in terms of network intrusion detection performance, and is suitable for deployment in the real network environment.