scholarly journals Automatic Detection of HTTP Injection Attacks using Convolutional Neural Network and Deep Neural Network

2021 ◽  
Author(s):  
Victor Odumuyiwa ◽  
Analogbei Chibueze
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cyber Security ◽  
Web Application ◽  
False Positive Rate ◽  
Attack Detection ◽  
Detection Model ◽  
Injection Attacks ◽  
Rule Based Approach ◽  
The Right

HTTP injection attacks are well known cyber security threats with fatal consequences. These attacks initiated by malicious entities (either human or computer) send dangerous or unsafe malicious contents into the parameters of HTTP requests. Combatting injection attacks demands for the development of Web Intrusion Detection Systems (WIDS). Common WIDS follow a rule-based approach or a signature-based approach which have the common problem of high false-positive rate (wrongly classifying malicious HTTP requests) hence making them restricted to only one type of web application. They are easily bypassed and unable to detect new kinds of malicious attacks as they lack a sufficient model of understanding the representations of HTTP request parameters. In this paper, deep learning techniques are used to develop models that would automatically detect injection attacks in HTTP requests. A special layer called the character embedding layer in the deep learning models is used to allow the learning of the representation of the request parameter of HTTP requests in higher abstract levels and also aid in learning the relationships between the characters of the request parameter. The experimentation results showed that with deep learning, better injection attack detection is possible and given the right dataset, a deep learning detection model would be able to correctly classify HTTP requests for any web application.

scholarly journals A Learning-based Neural Network Model for the Detection and Classification of SQL Injection Attacks

2017 ◽  
Vol 7 (2) ◽  
pp. 16-41 ◽  
Author(s):  
Naghmeh Moradpoor Sheykhkanloo
Keyword(s):  
Neural Network ◽  
Web Application ◽  
Query Language ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Injection Technique ◽  
Injection Attacks ◽  
Proposed Model ◽  
Positive Rate ◽  
Sql Injection Attacks

Structured Query Language injection (SQLi) attack is a code injection technique where hackers inject SQL commands into a database via a vulnerable web application. Injected SQL commands can modify the back-end SQL database and thus compromise the security of a web application. In the previous publications, the author has proposed a Neural Network (NN)-based model for detections and classifications of the SQLi attacks. The proposed model was built from three elements: 1) a Uniform Resource Locator (URL) generator, 2) a URL classifier, and 3) a NN model. The proposed model was successful to: 1) detect each generated URL as either a benign URL or a malicious, and 2) identify the type of SQLi attack for each malicious URL. The published results proved the effectiveness of the proposal. In this paper, the author re-evaluates the performance of the proposal through two scenarios using controversial data sets. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed model in terms of accuracy, true-positive rate as well as false-positive rate.

scholarly journals DeepHTTP: Anomalous HTTP Traffic Detection and Malicious Pattern Mining Based on Deep Learning

2020 ◽  
pp. 141-161
Author(s):  
Yuqi Yu ◽  
Hanbing Yan ◽  
Yuan Ma ◽  
Hao Zhou ◽  
Hongchao Guan
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Pattern Mining ◽  
Short Term Memory ◽  
Attack Detection ◽  
Attention Mechanism ◽  
Discriminative Ability ◽  
Feature Extraction Method ◽  
Detection Model ◽  
Traffic Detection

AbstractHypertext Transfer Protocol (HTTP) accounts for a large portion of Internet application-layer traffic. Since the payload of HTTP traffic can record website status and user request information, many studies use HTTP protocol traffic for web application attack detection. In this work, we propose DeepHTTP, an HTTP traffic detection framework based on deep learning. Unlike previous studies, this framework not only performs malicious traffic detection but also uses the deep learning model to mine malicious fields of the traffic payload. The detection model is called AT-Bi-LSTM, which is based on Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism. The attention mechanism can improve the discriminative ability and make the result interpretable. To enhance the generalization ability of the model, this paper proposes a novel feature extraction method. Experiments show that DeepHTTP has an excellent performance in malicious traffic discrimination and pattern mining.

scholarly journals A Learning-based Neural Network Model for the Detection and Classification of SQL Injection Attacks

2020 ◽  
pp. 450-475
Author(s):  
Naghmeh Moradpoor Sheykhkanloo
Keyword(s):  
Neural Network ◽  
Web Application ◽  
Query Language ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Injection Technique ◽  
Data Sets ◽  
Injection Attacks ◽  
Proposed Model ◽  
Positive Rate

Structured Query Language injection (SQLi) attack is a code injection technique where hackers inject SQL commands into a database via a vulnerable web application. Injected SQL commands can modify the back-end SQL database and thus compromise the security of a web application. In the previous publications, the author has proposed a Neural Network (NN)-based model for detections and classifications of the SQLi attacks. The proposed model was built from three elements: 1) a Uniform Resource Locator (URL) generator, 2) a URL classifier, and 3) a NN model. The proposed model was successful to: 1) detect each generated URL as either a benign URL or a malicious, and 2) identify the type of SQLi attack for each malicious URL. The published results proved the effectiveness of the proposal. In this paper, the author re-evaluates the performance of the proposal through two scenarios using controversial data sets. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed model in terms of accuracy, true-positive rate as well as false-positive rate.

scholarly journals A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks

2020 ◽  
Vol 17 (22) ◽  
pp. 8447
Author(s):  
Dima M. Alalharith ◽  
Hajar M. Alharthi ◽  
Wejdan M. Alghamdi ◽  
Yasmine M. Alsenbel ◽  
Nida Aslam ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Periodontal Disease ◽  
Convolutional Neural Network ◽  
Region Of Interest ◽  
Training Dataset ◽  
Detection Accuracy ◽  
Detection Model ◽  
Art Object ◽  
Orthodontic Patients

Computer-based technologies play a central role in the dentistry field, as they present many methods for diagnosing and detecting various diseases, such as periodontitis. The current study aimed to develop and evaluate the state-of-the-art object detection and recognition techniques and deep learning algorithms for the automatic detection of periodontal disease in orthodontic patients using intraoral images. In this study, a total of 134 intraoral images were divided into a training dataset (n = 107 [80%]) and a test dataset (n = 27 [20%]). Two Faster Region-based Convolutional Neural Network (R-CNN) models using ResNet-50 Convolutional Neural Network (CNN) were developed. The first model detects the teeth to locate the region of interest (ROI), while the second model detects gingival inflammation. The detection accuracy, precision, recall, and mean average precision (mAP) were calculated to verify the significance of the proposed model. The teeth detection model achieved an accuracy, precision, recall, and mAP of 100 %, 100%, 51.85%, and 100%, respectively. The inflammation detection model achieved an accuracy, precision, recall, and mAP of 77.12%, 88.02%, 41.75%, and 68.19%, respectively. This study proved the viability of deep learning models for the detection and diagnosis of gingivitis in intraoral images. Hence, this highlights its potential usability in the field of dentistry and aiding in reducing the severity of periodontal disease globally through preemptive non-invasive diagnosis.

scholarly journals Deep Learning Enabled Data Offloading With Cyber Attack Detection Model in Mobile Edge Computing Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185938-185949
Author(s):  
T. Gopalakrishnan ◽  
D. Ruby ◽  
Fadi Al-Turjman ◽  
Deepak Gupta ◽  
Irina V. Pustokhina ◽  
...  
Keyword(s):  
Deep Learning ◽  
Attack Detection ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Cyber Attack ◽  
Computing Systems ◽  
Data Offloading ◽  
Detection Model

scholarly journals End-to-End Deep Learning Fusion of Fingerprint and Electrocardiogram Signals for Presentation Attack Detection

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2085 ◽  
Author(s):  
Rami M. Jomaa ◽  
Hassan Mathkour ◽  
Yakoub Bazi ◽  
Md Saiful Islam
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Attack Detection ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Biometric Systems ◽  
Fingerprint Biometrics ◽  
End To End ◽  
The Impact

Although fingerprint-based systems are the commonly used biometric systems, they suffer from a critical vulnerability to a presentation attack (PA). Therefore, several approaches based on a fingerprint biometrics have been developed to increase the robustness against a PA. We propose an alternative approach based on the combination of fingerprint and electrocardiogram (ECG) signals. An ECG signal has advantageous characteristics that prevent the replication. Combining a fingerprint with an ECG signal is a potentially interesting solution to reduce the impact of PAs in biometric systems. We also propose a novel end-to-end deep learning-based fusion neural architecture between a fingerprint and an ECG signal to improve PA detection in fingerprint biometrics. Our model uses state-of-the-art EfficientNets for generating a fingerprint feature representation. For the ECG, we investigate three different architectures based on fully-connected layers (FC), a 1D-convolutional neural network (1D-CNN), and a 2D-convolutional neural network (2D-CNN). The 2D-CNN converts the ECG signals into an image and uses inverted Mobilenet-v2 layers for feature generation. We evaluated the method on a multimodal dataset, that is, a customized fusion of the LivDet 2015 fingerprint dataset and ECG data from real subjects. Experimental results reveal that this architecture yields a better average classification accuracy compared to a single fingerprint modality.

scholarly journals OTHR-13. A DEEP LEARNING APPROACH TO DETECT CANCER METASTASES TO THE BRAIN IN MRI

2019 ◽  
Vol 1 (Supplement_1) ◽  
pp. i20-i21
Author(s):  
Min Zhang ◽  
Geoffrey Young ◽  
Huai Chen ◽  
Lei Qin ◽  
Xinhua Cao ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Metastases ◽  
False Positive ◽  
False Positive Rate ◽  
Brain Mri ◽  
Ground Truth ◽  
Learning Approach ◽  
Cancer Metastases ◽  
The Brain

Abstract BACKGROUND AND OBJECTIVE: Brain metastases have been found to account for one-fourth of all cancer metastases seen in clinics. Magnetic resonance imaging (MRI) is widely used for detecting brain metastases. Accurate detection of the brain metastases is critical to design radiotherapy to treat the cancer and monitor their progression or response to the therapy and prognosis. However, finding metastases on brain MRI is very challenging as many metastases are small and manifest as objects of weak contrast on the images. In this work we present a deep learning approach integrated with a classification scheme to detect cancer metastases to the brain on MRI. MATERIALS AND METHODS: We retrospectively extracted 101 metastases patients, equal to 1535 metastases on 10192 slices of images in a total of 336 scans from our PACS and manually marked the lesions on T1-weighted contrast enhanced MRI as the ground-truth. We then randomly separated the cases into training, validation, and test sets for developing and optimizing the deep learning neural network. We designed a 2-step computer-aided detection (CAD) pipeline by first applying a fast region-based convolutional neural network method (R-CNN) to sequentially process each slice of an axial brain MRI to find abnormal hyper-intensity that may correspond to a brain metastasis and, second, applying a random under sampling boost (RUSBoost) classification method to reduce the false positive metastases. RESULTS: The computational pipeline was tested on real brain images. A sensitivity of 97.28% and false positive rate of 36.25 per scan over the images were achieved by using the proposed method. CONCLUSION: Our results demonstrated the deep learning-based method can detect metastases in very challenging cases and can serve as CAD tool to help radiologists interpret brain MRIs in a time-constrained environment.

Long Short-Term Memory Neural Network-Based Attack Detection Model for In-Vehicle Network Security

2020 ◽  
Vol 4 (6) ◽  
pp. 1-4
Author(s):  
Zadid Khan ◽  
Mashrur Chowdhury ◽  
Mhafuzul Islam ◽  
Chin-Ya Huang ◽  
Mizanur Rahman
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Short Term Memory ◽  
Attack Detection ◽  
Short Term ◽  
Term Memory ◽  
Detection Model ◽  
Long Short Term Memory ◽  
Vehicle Network

scholarly journals Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

2021 ◽  
Vol 11 (15) ◽  
pp. 7147
Author(s):  
Jinmo Gu ◽  
Jinhyuk Na ◽  
Jeongeun Park ◽  
Hayoung Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
False Positive Rate ◽  
Direct Marketing ◽  
Network Models ◽  
Learning Models ◽  
Insurance Policy ◽  
Neural Network Models ◽  
Potential Customers

Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers.

scholarly journals BLNN: Multiscale Feature Fusion-Based Bilinear Fine-Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingyu Gao ◽  
Fei Wang ◽  
Peng Song ◽  
Junyan Liu ◽  
DaWei Qi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Defect Detection ◽  
Feature Fusion ◽  
Image Features ◽  
Optical Image ◽  
Training Time ◽  
Fine Grained ◽  
Detection Model ◽  
Defect Recognition

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves the wood utilization. The traditional neural network technique is unemployed for the wood defect detection of optical image used, which results from a long training time, low recognition accuracy, and nonautomatic extraction of defect image features. In this paper, a wood knot defect detection model (so-called BLNN) combined deep learning is reported. Two subnetworks composed of convolutional neural networks are trained by Pytorch. By using the feature extraction capabilities of the two subnetworks and combining the bilinear join operation, the fine-grained features of the image are obtained. The experimental results show that the accuracy has reached up 99.20%, and the training time is obviously reduced with the speed of defect detection about 0.0795 s/image. It indicates that BLNN has the ability to improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

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