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1. A Transfer Learning with Deep Neural Network Approach for Network Intrusion Detection

   Mohammad Masum, Hossain Shahriar (April 2021, International journal of intellligent computing research)

   Ayahiko Niimi, Future University-Hakodate (Ed.)

   Traditional Network Intrusion Detection Systems (NIDS) encounter difficulties due to the exponential growth of network traffic data and modern attacks' requirements. This paper presents a novel network intrusion classification framework using transfer learning from the VGG-16 pre-trained model. The framework extracts feature leveraging pre-trained weights trained on the ImageNet dataset in the initial step, and finally, applies a deep neural network to the extracted features for intrusion classification. We applied the presented framework on NSL-KDD, a benchmark dataset for network intrusion, to evaluate the proposed framework's performance. We also implemented other pre-trained models such as VGG19, MobileNet, ResNet-50, and Inception V3 to evaluate and compare performance. This paper also displays both binary classification (normal vs. attack) and multi-class classification (classifying types of attacks) for network intrusion detection. The experimental results show that feature extraction using VGG-16 outperforms other pre-trained models producing better accuracy, precision, recall, and false alarm rates. 

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2. Deep Camera–Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions

   https://doi.org/10.3390/s23146255  

   Ogunrinde, Isaac; Bernadin, Shonda (July 2023, Sensors)

   AVs are affected by reduced maneuverability and performance due to the degradation of sensor performances in fog. Such degradation can cause significant object detection errors in AVs’ safety-critical conditions. For instance, YOLOv5 performs well under favorable weather but is affected by mis-detections and false positives due to atmospheric scattering caused by fog particles. The existing deep object detection techniques often exhibit a high degree of accuracy. Their drawback is being sluggish in object detection in fog. Object detection methods with a fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persists. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image onto the camera image. Using the attention mechanism, we emphasized and improved the important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our results show that the proposed method significantly enhances the detection of small and distant objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed, with an accuracy of 0.849 at 69 fps. 

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3. Autonomous Data Scientist (ADaS): A Practical Example of the Future Role of AI in Cybersecurity

   https://doi.org/10.1109/LCN65610.2025.11146345  

   Green, Simone F; Nelson, Abigail; Zhang, Zimo; Liang, Aimee; Chavis, Jeffrey S; Velez, Paul; Cao, Yinzhi; Watkins, Lanier (October 2025, IEEE)

   Not AvaThe volume of network traffic and elusiveness of modern cyber threats challenge the ability of cyber analysts to identify anomalous behavior in networks. In this work, we propose an autonomous agent to enhance analysts’ detection capabilities. To this end, we designed an agent to analyze feature-rich network traffic by autonomously executing the Data Analytic Development Process: (1) data engineering–prepares datasets, (2) feature engineering–selects the most representative features, (3) model engineering–chooses the best algorithm-feature combination, and (4) analytic deployment–applies the optimized analytic to the dataset and identifies anomalous behavior. We refer to this agent as an Autonomous Data Scientist (ADaS), utilizing reinforcement learning as an orchestrator to determine the optimal combination of features and unsupervised clustering algorithm. ADaS operates without labeled training data, and results on popular public datasets (NB15, IoT-23, and KDD’99) demonstrate high detection and low false alarm rates, making it reliable and novel for anomaly detection. 

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4. TL-NID: Deep Neural Network with Transfer Learning for Network Intrusion Detection

   https://doi.org/10.23919/ICITST51030.2020.9351317  

   Masum, Mohammad; Shahriar, Hossain (December 2020, 15th International Conference for Internet Technology and Secured Transactions (ICITST))

   null (Ed.)

   Network intrusion detection systems (NIDSs) play an essential role in the defense of computer networks by identifying a computer networks' unauthorized access and investigating potential security breaches. Traditional NIDSs encounters difficulties to combat newly created sophisticated and unpredictable security attacks. Hence, there is an increasing need for automatic intrusion detection solution that can detect malicious activities more accurately and prevent high false alarm rates (FPR). In this paper, we propose a novel network intrusion detection framework using a deep neural network based on the pretrained VGG-16 architecture. The framework, TL-NID (Transfer Learning for Network Intrusion Detection), is a two-step process where features are extracted in the first step, using VGG-16 pre-trained on ImageNet dataset and in the 2 nd step a deep neural network is applied to the extracted features for classification. We applied TL-NID on NSL-KDD, a benchmark dataset for network intrusion, to evaluate the performance of the proposed framework. The experimental results show that our proposed method can effectively learn from the NSL-KDD dataset with producing a realistic performance in terms of accuracy, precision, recall, and false alarm. This study also aims to motivate security researchers to exploit different state-of-the-art pre-trained models for network intrusion detection problems through valuable knowledge transfer. 

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5. A Multi-Resolution Attention U-Net for Pavement Distress Segmentation in 3D Images: Architecture and Data-Driven Insights

   https://doi.org/10.3390/math13172752  

   Gong, Haitao; Tao, Jueqiang; Luo, Xiaohua; Wang, Feng (September 2025, Mathematics)

   High-resolution 3D pavement images have become a valuable data source for automated surface distress detection and assessment. However, accurately identifying and segmenting cracks from pavement images remains challenging, due to factors such as low contrast and hair-like thinness. This study investigates key factors affecting segmentation performance and proposes a novel deep learning architecture designed to enhance segmentation robustness under these challenging conditions. The proposed model integrates a multi-resolution feature extraction stream with gated attention mechanisms to improve spatial awareness and selectively fuse information across feature levels. Our extensive experiments on a 3D pavement dataset demonstrated that the proposed method outperformed several state-of-the-art architectures, including FCN, U-Net, DeepLab, DeepCrack, and CrackFormer. Compared with U-Net, it improved F1 from 0.733 to 0.780. The gains were most pronounced on thin cracks, with F1 from 0.531 to 0.626. Our paired t-tests across folds showed the method is statistically better than U-Net and DeepCrack on Recall, IoU, Dice, and F1. These findings highlight the effectiveness of the attention-guided, multi-scale feature fusion method for robust crack segmentation using 3D pavement data. 

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