Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Image Representation

Ullah, Farhan; Ullah, Shamsher; Naeem, Muhammad Rashid; Mostarda, Leonardo; Rho, Seungmin; Cheng, Xiaochun

doi:10.3390/s22155883

Cited by 17 publications

(7 citation statements)

References 42 publications

(51 reference statements)

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“…We modify the shape of X to yield X r ∈ R S 2 × HW S 2 ×C . Subsequently, the feature vectors undergo a linear transformation to yield three matrices, namely Q, K, and V. The mathematical formulas for these calculations are provided in Equations ( 9)- (11).…”

Section: Biformer Attention Mechanismmentioning

confidence: 99%

“…Nevertheless, this approach encounters substantial operational expenses due to the expansive expanse of forested landscape under consideration. Conventional methods of early forest fire detection predominantly rely on smoke and temperature-sensitive sensors, often in a combined configuration [10][11][12]. These sensors are engineered to detect airborne smoke particulates and swift escalations in ambient temperature, thereby facilitating fire detection.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images

Saydirasulovich,

Mukhiddinov,

Djuraev

et al. 2023

Sensors

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Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting forest fire smoke encounter persistent issues, including a slow identification rate, suboptimal accuracy in detection, and challenges in distinguishing smoke originating from small sources. This study presents an enhanced YOLOv8 model customized to the context of unmanned aerial vehicle (UAV) images to address the challenges above and attain heightened precision in detection accuracy. Firstly, the research incorporates Wise-IoU (WIoU) v3 as a regression loss for bounding boxes, supplemented by a reasonable gradient allocation strategy that prioritizes samples of common quality. This strategic approach enhances the model’s capacity for precise localization. Secondly, the conventional convolutional process within the intermediate neck layer is substituted with the Ghost Shuffle Convolution mechanism. This strategic substitution reduces model parameters and expedites the convergence rate. Thirdly, recognizing the challenge of inadequately capturing salient features of forest fire smoke within intricate wooded settings, this study introduces the BiFormer attention mechanism. This mechanism strategically directs the model’s attention towards the feature intricacies of forest fire smoke, simultaneously suppressing the influence of irrelevant, non-target background information. The obtained experimental findings highlight the enhanced YOLOv8 model’s effectiveness in smoke detection, proving an average precision (AP) of 79.4%, signifying a notable 3.3% enhancement over the baseline. The model’s performance extends to average precision small (APS) and average precision large (APL), registering robust values of 71.3% and 92.6%, respectively.

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Section: Biformer Attention Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images

Saydirasulovich,

Mukhiddinov,

Djuraev

et al. 2023

Sensors

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“…In recent years, numerous scholars have made significant advancements in deeplearning-based object detection methods. Farhan Ullah et al [19] proposed a cyber threat detection system that combines migration learning and multi-model image characterization in a hybrid approach. Du et al [20] introduced BV-YOLOv5S, a modification of YOLOv5S, to achieve real-time defect detection in road pits.…”

Section: Related Workmentioning

confidence: 99%

YOLO-DRS: A Bioinspired Object Detection Algorithm for Remote Sensing Images Incorporating a Multi-Scale Efficient Lightweight Attention Mechanism

Liao,

Zhu

2023

Biomimetics

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Bioinspired object detection in remotely sensed images plays an important role in a variety of fields. Due to the small size of the target, complex background information, and multi-scale remote sensing images, the generalized YOLOv5 detection framework is unable to obtain good detection results. In order to deal with this issue, we proposed YOLO-DRS, a bioinspired object detection algorithm for remote sensing images incorporating a multi-scale efficient lightweight attention mechanism. First, we proposed LEC, a lightweight multi-scale module for efficient attention mechanisms. The fusion of multi-scale feature information allows the LEC module to completely improve the model’s ability to extract multi-scale targets and recognize more targets. Then, we propose a transposed convolutional upsampling alternative to the original nearest-neighbor interpolation algorithm. Transposed convolutional upsampling has the potential to greatly reduce the loss of feature information by learning the feature information dynamically, thereby reducing problems such as missed detections and false detections of small targets by the model. Our proposed YOLO-DRS algorithm exhibits significant improvements over the original YOLOv5s. Specifically, it achieves a 2.3% increase in precision (P), a 3.2% increase in recall (R), and a 2.5% increase in mAP@0.5. Notably, the introduction of the LEC module and transposed convolutional results in a respective improvement of 2.2% and 2.1% in mAP@0.5. In addition, YOLO-DRS only increased the GFLOPs by 0.2. In comparison to the state-of-the-art algorithms, namely YOLOv8s and YOLOv7-tiny, YOLO-DRS demonstrates significant improvements in the mAP@0.5 metrics, with enhancements ranging from 1.8% to 7.3%. It is fully proved that our YOLO-DRS can reduce the missed and false detection problems of remote sensing target detection.

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“…The CNN network is To achieve this, the merged features (ACGs, texture) are fed into CNN. CNN model performs better with diverse data types, including textual, texture, and media features [27,28]. For this purpose, we employ 1-D convolutional layers, pooling layers, dropout layers, and a fully connected layer.…”

Section: Deep Features Selectionmentioning

confidence: 99%

A malware detection system using a hybrid approach of multi-heads attention-based control flow traces and image visualization

2022

Self Cite

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Android is the most widely used mobile platform, making it a prime target for malicious attacks. Therefore, it is imperative to effectively circumvent these attacks. Recently, machine learning has been a promising solution for malware detection, which relies on distinguishing features. While machine learning-based malware scanners have a large number of features, adversaries can avoid detection by using feature-related expertise. Therefore, one of the main tasks of the Android security industry is to consistently propose cutting-edge features that can detect suspicious activity. This study presents a novel feature representation approach for malware detection that combines API-Call Graphs (ACGs) with byte-level image representation. First, the reverse engineering procedure is used to obtain the Java programming codes and Dalvik Executable (DEX) file from Android Package Kit (APK). Second, to depict Android apps with high-level features, we develop ACGs by mining API-Calls and API sequences from Control Flow Graph (CFG). The ACGs can act as a digital fingerprint of the actions taken by Android apps. Next, the multi-head attention-based transfer learning method is used to extract trained features vector from ACGs. Third, the DEX file is converted to a malware image, and the texture features are extracted and highlighted using a combination of FAST (Features from Accelerated Segment Test) and BRIEF (Binary Robust Independent Elementary Features). Finally, the ACGs and texture features are combined for effective malware detection and classification. The proposed method uses a customized dataset prepared from the CIC-InvesAndMal2019 dataset and outperforms state-of-the-art methods with 99.27% accuracy.

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Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Image Representation

Cited by 17 publications

References 42 publications

An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images

An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images

YOLO-DRS: A Bioinspired Object Detection Algorithm for Remote Sensing Images Incorporating a Multi-Scale Efficient Lightweight Attention Mechanism

A malware detection system using a hybrid approach of multi-heads attention-based control flow traces and image visualization

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