Advancements in intrusion detection: A lightweight hybrid RNN-RF model

Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats

A Two-branch Edge Guided Lightweight Network for infrared image saliency detection

In the dynamic landscape of saliency detection, convolutional neural networks have emerged as catalysts for innovation, but remain largely tailored for RGB imagery, falling short in the context of infrared images, particularly in memory-restricted environments. These existing approaches tend to overlook the wealth of contour information vital for a nuanced analysis of infrared images. Addressing this notable gap, we introduce the novel Two-branch Edge Guided Lightweight Network (TBENet), designed explicitly for the robust analysis of infrared image saliency detection. The main contributions of this paper are as follows. First, we formulate the saliency detection task as two subtasks, contour enhancement and foreground segmentation. Therefore, the TBENet is divided into two specialized branches: a contour prediction branch for extracting target contour and a saliency map generation branch for separating the foreground from the background. The first branch employs an encoder–decoder architecture to meticulously delineate object contours, serving as a guiding blueprint for the second branch. This latter segment adeptly integrates spatial and semantic data, creating a precise saliency map that is refined further by an innovative edge-weighted contour loss function. Second, to enhance feature integration capabilities, we propose depthwise multi-scale and multi-cue modules, facilitating sophisticated feature aggregation. Third, a high-level linear bottleneck module is devised to ensure the extraction of rich semantic information, and by replacing the standard convolution with the depthwise convolution, it is beneficial to reduce model complexity. Additional, we reduce the number of channels of the feature maps from each stage of the decoder to further enhance the lightweight of the model. Last, we construct a novel infrared ship dataset Small-IRShip to train and evaluate our proposed model. Experimental results on the homemade dataset Small-IRShip and two publicly available datasets, namely RGB-T and IRSTD-1k, demonstrate TBENet’s superior performance over state-of-the-art methods, affirming its effectiveness in harnessing edge information and incorporating advanced feature integration strategies