DeepInsight-Convolutional Neural Network for Intrusion Detection Systems

Over the past few years, Internet applications have become more advanced and widely used. This has increased the need for Internet networks to be secured. Intrusion detection systems (IDSs), which employ artificial intelligence (AI) methods, are vital to ensuring network security. As a branch of AI, deep learning (DL) algorithms are now effectively applied in IDSs. Among deep learning neural networks, the convolutional neural network (CNN) is a well-known structure designed to process complex data. The CNN overcomes the typical limitations of conventional machine learning approaches and is mainly used in IDSs. Several CNN-based approaches are employed in IDSs to handle privacy issues and security threats. However, there are no comprehensive surveys of IDS schemes that have utilized CNN to the best of our knowledge. Hence, in this study, our primary focus is on CNN-based IDSs so as to increase our understanding of various uses of the CNN in detecting network intrusions, anomalies, and other types of attacks. This paper innovatively organizes the studied CNN-IDS approaches into multiple categories and describes their primary capabilities and contributions. The main features of these approaches, such as the dataset, architecture, input shape, evaluated metrics, performance, feature extraction, and classifier method, are compared. Because different datasets are used in CNN-IDS research, their experimental results are not comparable. Hence, this study also conducted an empirical experiment to compare different approaches based on standard datasets, and the comparative results are presented in detail.

Intrusion Detection System (IDS) dataset is crucial to detect lateral movement of cyber-attacks. IDS dataset will help to train the IDS classifier model to achieve earliest detection. A good near-realism public dataset is essential to assist the development of advanced IDS classifier models. However, the available public IDS dataset has long been under scrutiny for its practicality to reflect real low-footprint cyber threats, render real-time network scenario, reflect recent malware attack over newly developed DoH protocol, disregard layer 3 information and finally publish contradictory results of classification and analysis between various studies which makes it non-reproducible and without shareable results. This problem can be resolved by sophisticatedly visualizing a new realistic, real-time, low footprint and up-to-date benchmarked dataset. Visualization helps to detect data deformation before designing the optimized and highly accurate classifier model. Therefore, this study aims to review a new realistic benchmarked IDS dataset and apply sophisticated technique to visualize them. The review starts by carefully examining production network features. These are then compared with various well-established public IDS datasets. Many of them are static, unrealistic meta-features and disregard source and destination Internet Protocol (IP) information except CIRA-CIC-DoHBrw-2020 dataset. The study then applies Eigen Centrality (EC) technique from the graph theory to visualize this layer 3 (L3) information. Finally, using various visualization techniques such as Principal Component Analysis (PCA) and Gaussian Mixture Model (GMM), the study further analyzes and subsequently visualizes the data. Results show that the CIRA-CIC-DoHBrw-2020 simulated recent malware attack and has a very imbalanced dataset which reflects the realistic low-footprint cyber-attacks. The centrality graph clearly visualizes IPs that are compromised by recent DoH attack in real-time, and the study concludes decisively that smaller packet length of size 1000 to 2000 bytes is to fit an attack trait.

A novel approach for handling missing data to enhance network intrusion detection system

One of the key challenges of the machine learning (ML) based intrusion detection system (IDS) is the expensive computation time which is largely caused by the redundant, incomplete, and unrelated features contain in the IDS datasets. To overcome such challenges and ensure building efficient and more accurate IDS models, many researchers utilize preprocessing techniques such as normalization and feature selection, and a hybrid modeling approach is typically used. In this work, we propose a hybrid IDS modeling approach with an algorithm for feature selection (FS) and another for building the IDS. The FS method is a wrapper-based FS with a decision tree as the feature evaluator. Five selected ML algorithms are individually used in combination with the proposed FS method to build five IDS models using the UNSW-NB15 dataset. As a baseline, five more IDS models are built, in a single modeling approach, using the full features of the datasets. We evaluate the effectiveness of our proposed method by comparing it with the baseline models and also with state-of-the-art works. Our method achieves the best DR of 97.95% and proved to be quite effective in comparison to state-of-the-art works. We, therefore, recommend its usage especially in IDS modeling with the UNSW-NB15 dataset.

The network security is getting more important due to the wide-spread computer viruses and increasing network attacks. Nowadays, more and more security mechanisms, such as firewalls and intrusion detection systems (IDS), are introduced to protect the network from malicious attacks. This paper proposes an agent and service based intrusion detection and response system for active network. In contrast to a traditional passive network, an active network gives the nodes programmable ability to exercise various active network technologies. The intrusion response, service deployment, and service update mechanisms are centered on this technology. The proposed model of intrusion detection and response system (IDRS) catches network attacks and responses to stop the attacks at the first time to reduce the damage. Detecting, reporting, and responding capabilities are all embedded and integrated in the proposed system. A prototype system is developed using a novel data mining technology (the support vector machine) to enhance the detection function. In addition, several experiments were conducted to verify the system and results showed that the system was able to effectively identify the intrusions and respond promptly. Experiments also showed that the support vector machine outperforms the competitive neural networks in identifying the intrusions.

With the popularization and development of network knowledge, network intruders are increasing, and the attack mode has been updated. Intrusion detection technology is a kind of active defense technology, which can extract the key information from the network system, and quickly judge and protect the internal or external network intrusion. Intrusion detection is a kind of active security technology, which provides real-time protection for internal attacks, external attacks and misuse, and it plays an important role in ensuring network security. However, with the diversification of intrusion technology, the traditional intrusion detection system cannot meet the requirements of the current network security. Therefore, the implementation of intrusion detection needs diversifying. In this context, we apply neural network technology to the network intrusion detection system to solve the problem. In this paper, on the basis of intrusion detection method, we analyze the development history and the present situation of intrusion detection technology, and summarize the intrusion detection system overview and architecture. The neural network intrusion detection is divided into data acquisition, data analysis, pretreatment, intrusion behavior detection and testing.

Our generation has seen the boom and ubiquitous advent of Internet connectivity. Adversaries have been exploiting this omnipresent connectivity as an opportunity to launch cyber attacks. As a consequence, researchers around the globe devoted a big attention to data mining and machine learning with emphasis on improving the accuracy of intrusion detection system (IDS). In this paper, we present a few-shot deep learning approach for improved intrusion detection. We first trained a deep convolutional neural network (CNN) for intrusion detection. We then extracted outputs from different layers in the deep CNN and implemented a linear support vector machine (SVM) and 1-nearest neighbor (1-NN) classifier for few-shot intrusion detection. few-shot learning is a recently developed strategy to handle situation where training samples for a certain class are limited. We applied our proposed method to the two well-known datasets simulating intrusion in a military network: KDD 99 and NSL-KDD. These datasets are imbalanced, and some classes have much less training samples than others. Experimental results show that the proposed method achieved better performances than the state-of-the-art on those two datasets.

In-vehicle network intrusion detection using deep convolutional neural network

Self-mixing interferometry (SMI) is superior to other laser interferometry methods due to its simplicity and compactness. However, SMI signals are often complex and difficult to process due to interference in the form of variations in the effective reflectivity of the target, noisy signals, complex signal shapes, and other dependencies. Deep neural networks have been a very popular area of research in computer artificial intelligence in recent years, allowing more implicit features to be uncovered than traditional shallow machine learning. It has been shown that convolutional and back-propagation neural networks can be used for SMI signal processing. There are also studies that have used machine learning genetic algorithms for absolute distance measurement. Based on the above, this study used convolutional neural networks to form a deep neural network for absolute distance measurement based on SMI technology. We first trained the deep convolutional neural network at different feedback strengths. The results of the Convolutional Neural Network (CNN) model showed a coefficient of determination of 0.9987. which is consistent with the required model. The trained network was then used to estimate absolute distances with and without the addition of noise. The comparison proves that the proposed method is noise-proof and has high adaptability for measurements under different conditions.

Intrusion detection systems help network administrators prepare for and deal with network security attacks. These systems collect information from a variety of systems and network sources, and analyze them for signs of intrusion and misuse. A variety of techniques have been employed for analysis ranging from traditional statistical methods to new data mining approaches. In this study the performance of three data mining methods in detecting network intrusion is examined. An experimental design (3times2x2) is created to evaluate the impact of three data mining methods, two data representation formats, and two data proportion schemes on the classification accuracy of intrusion detection systems. The results indicate that data mining methods and data proportion have a significant impact on classification accuracy. Within data mining methods, rough sets provide better accuracy, followed by neural networks and inductive learning. Balanced data proportion performs better than unbalanced data proportion. There are no major differences in performance between binary and integer data representation.

Network intrusion detection is of great significance for network security in Local Area Network (LAN). Traditional methods such as firewalls do not completely protect against attacks on the LAN due to lack of continuous learning. Recently, the ability of convolutional neural networks (CNN) to extract features in the field of computer vision has received extensive attention. CNN can automatically extract effective complex features to adapt to constantly changing environments, which is especially important in network intrusion detection. In this paper, we focus on network security in the LAN. We propose an approach based on CNN to implement intrusion detection in LAN. This approach can effectively identify network attacks and has an accuracy of 98.34% on the KDD99 dataset. The experimental results show that the proposed approach based on the CNN has high accuracy in intrusion detection.

With the rapid development of network technology, active defending of the network intrusion is more important than before. In order to improve the intelligence and accuracy of network intrusion detection and reduce false alarms, a new deep neural network (NDNN) model based intrusion detection method is designed. A NDNN with four hidden layers is modelled to capture and classify the intrusion features of the KDD99 and NSL‐KDD training data. Experiments on KDD99 and NSL‐KDD dataset shows that the NDNN‐based method improves the performance of the intrusion detection system (IDS) and the accuracy rate can be obtained as high as 99.9%, which is higher when compared with other dozens of intrusion detection methods. This NDNN model can be applied in IDS to make the system more secure.

This paper presents an overview of the technologies and the methodologies used in Network Intrusion Detection and Prevention Systems (NIDPS). Intrusion Detection and Prevention System (IDPS) technologies are differentiated by types of events that IDPSs can recognize, by types of devices that IDPSs monitor and by activity. NIDPSs monitor and analyze the streams of network packets in order to detect security incidents. The main methodology used by NIDPSs is protocol analysis. Protocol analysis requires good knowledge of the theory of the main protocols, their definition, how each protocol works.Keywords: Intrusion Detection and Prevention System, Protocol Analysis, Sensor, Signature, State(ProQuest: ... denotes formulae omitted.)1 IntroductionIncreasing size and complexity of the Internet and Intranet networks have led to increasing number of vulnerabilities that could be exploited. Thus, the internal and external attacks on the information systems are increasing at an alarming rate. Also, these are becoming more severe and sophisticated. The attackers find ingenious ways to bypass the security controls and to compromise the security and the well functioning of the information systems. They are motivated by financial, political, and military objectives. In this context, defending wide area networks from malicious traffic, unauthorized access to systems involves many problems.In security information systems Network Intrusion Detection and Prevention Systems (NIDPS) are important tools to detect possible incidents and also, to attempt to stop them in real time. Due to changing attacks, intrusion detection methodologies and technologies continuously evolve, adding new detection capabilities, to avoid detection. They must adapt to new forms of malware, to the public networks, increased traffic.2 Concepts of Intrusion DetectionAn intrusion is a successful action to gain access to an information system, to compromise it or to make it unavailable. This is possible due to the presence of vulnerability in the target system that can be exploited by a motivated intruder.Intrusion Detection and Prevention is the process of monitoring the information systems by sensors or agents and analyzing the collected information to detect and to attempt to stop the attacks in real time, identifying vulnerabilities, the violation of security policies or standard security practices.An Intrusion Detection and Prevention System (IDPS) is a tool that monitors information systems, collects, analyzes information, and initiates responses when an intrusion is detected.Intrusion Detection Systems (IDSs) mainly work as defensive mechanisms. They only alert the system administrators that an incident has occurred. Intrusion Prevention Systems (IPSs) can take some actions to attempt to stop the attack, such as breaking the connection or modifying the firewall rules to deny access to the intruder. The response of the classic IDS can be slow if the system administrator is busy while the response of the IPS is automatic. An architecture that uses together IPS and IDS technologies is the best solution for defense in depth.Conceptually, a generic IDPS consists of modular components. It mainly has the following components: monitoring system, storage, analyzer, and responder.* Monitoring system - monitors and logs the events in a computer system or network;* Storage - stores information, called audit record, about suspicious activities or intrusions; also, the security policies used in analysis are stored;* Analyzer - uses different analysis methodologies to detect the incidents;* Responder - the response mechanism of incidents.The IDPSs could be classified as:* By detection methodology [12], [18]:- misuse-based detection- anomaly-based detection- stateful protocol analysis* By activity [12]:- network-based- wireless-based- network behavior analysis- host-based* By behavior on detection:- passive- active* By collection and analysis frequency:- continuous- periodicThe detection methodologies describe the characteristics of the analyzer. …

Intrusion detection system plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. Due to huge increase in network traffic and different types of attacks, accurately classifying the malicious and legitimate network traffic is time consuming and computational intensive. Recently, more and more researchers applied deep neural networks (DNNs) to solve intrusion detection problems. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), the two main types of DNN architectures, are widely explored to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of CNN and RNN on the deep learning based intrusion detection systems, aiming to give basic guidance for DNN selection.

Automated image classification is an essential task of the computer vision field. The tagging of images into a set of predefined groups is referred to as image classification. The implementation of computer vision to automate image classification would be beneficial because manual image evaluation and identification can be time-consuming, particularly when there are many images of different classes. Deep learning approaches are proven to overperform existing machine learning techniques in many fields in recent years, and computer vision is one of the most notable examples. The very deep neural network (VDCNN) is a powerful deep learning model for image classification, and this paper examines it briefly using MNIST handwritten digit dataset. This dataset is used to prove the efficacy of very deep neural networks over other deep learning models. The proposed study aims to comprehend the very deep neural network architecture used to accomplish a handwritten digit recognition task. The feasibility of the proposed model is evaluated using mean accuracy, validation accuracy, and standard deviation. The study results of the very deep neural network model are compared to a convolutional neural network and convolutional neural network with batch normalization. According to the results of the comparison study, very deep neural networks achieve high accuracy of 99.1% for a handwritten dataset. The outcome of the proposed work is used to interpret how well a very deep neural network performs when compared to the other two models of deep neural networks. This proposed architecture may be used to automate the classification of handwritten digits dataset.