A Bayesian Classification Model for Real-Time Intrusion Detection

2003 ◽  
Author(s):  
Ricardo S. Puttini
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Bayesian Classification ◽  
Classification Model

An Intelligent Approach for Intrusion Detection using Convolutional Neural Network

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
P. Manoj Kumar ◽  
M. Parvathy ◽  
C. Abinaya Devi
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
Convolutional Neural Network ◽  
False Alarm Rate ◽  
Real Time ◽  
Detection Rate ◽  
Performance Metrics ◽  
Classification Model ◽  
Real Time Traffic

Intrusion Detection Systems (IDS) is one of the important aspects of cyber security that can detect the anomalies in the network traffic. IDS are a part of Second defense line of a system that can be deployed along with other security measures such as access control, authentication mechanisms and encryption techniques to secure the systems against cyber-attacks. However, IDS suffers from the problem of handling large volume of data and in detecting zero-day attacks (new types of attacks) in a real-time traffic environment. To overcome this problem, an intelligent Deep Learning approach for Intrusion Detection is proposed based on Convolutional Neural Network (CNN-IDS). Initially, the model is trained and tested under a new real-time traffic dataset, CSE-CIC-IDS 2018 dataset. Then, the performance of CNN-IDS model is studied based on three important performance metrics namely, accuracy / training time, detection rate and false alarm rate. Finally, the experimental results are compared with those of various Deep Discriminative models including Recurrent Neural network (RNN), Deep Neural Network (DNN) etc., proposed for IDS under the same dataset. The Comparative results show that the proposed CNN-IDS model is very much suitable for modelling a classification model both in terms of binary and multi-class classification with higher detection rate, accuracy, and lower false alarm rate. The CNN-IDS model improves the accuracy of intrusion detection and provides a new research method for intrusion detection.

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

scholarly journals Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 20
Author(s):  
Reynaldo Villarreal-González ◽  
Antonio J. Acosta-Hoyos ◽  
Jaime A. Garzon-Ochoa ◽  
Nataly J. Galán-Freyle ◽  
Paola Amar-Sepúlveda ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Real Time ◽  
Binary Classification ◽  
Simulated Data ◽  
Classification Model ◽  
Rt Pcr ◽  
Simon Bolivar ◽  
Polymerase Chain ◽  
Available Information

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

scholarly journals An edge based hybrid intrusion detection framework for mobile edge computing

2021 ◽  
Author(s):  
Ashish Singh ◽  
Kakali Chatterjee ◽  
Suresh Chandra Satapathy
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Network Architecture ◽  
Denial Of Service ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Hybrid Detection ◽  
Game Theoretical Approach ◽  
Improved Performance ◽  
Edge Based

AbstractThe Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

scholarly journals Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jian-ye Yuan ◽  
Xin-yuan Nan ◽  
Cheng-rong Li ◽  
Le-le Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Environmental Science ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Models ◽  
Data Set ◽  
Classification Tasks ◽  
Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

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