Hybrid Intrusion Detection System using an Unsupervised method for Anomaly-based Detection

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks.

Computer networks are being attacked every day. Intrusion detection systems (IDS) are used to detect and reduce effects of these attacks. The currently used of hybrid intrusion detection systems that was base on signature and anomaly based detection techniques were became inefficient for detecting attacks because it have nearly less than or equal to 95.5% for the detection rate and 1.8% for false positive rate, nowadays these values are unsatisfied for the detection so that the needs to enhanced hybrid intrusion detection system has became the most important issues. In this study, the enhanced hybrid intrusion detection system has been proposed to provide better results with high accuracy of the detection rate and reduce the value of false positive rate that will done by proposing new method based on decision tree of data mining techniques that is based on C4.5 algorithm to show that the proposed model is more efficient and it gives better optimum results.

Network Intrusion Detection Systems (IDS) play a key role in protecting computer networks from advanced cyber-attacks. However, most existing IDS rely on a single machine learning (ML) algorithm, limiting their detection ability for multi-aspect and dynamic threats. Single-model paradigms suffer from issues of overfitting and poor generalizability to new instances. This work introduces a better hybrid IDS with supervised ML techniques for two standard test sets: CICIDS2019 and TII-SSRC 23. Mutual Information-based feature selection was applied in the case of CICIDS2019 to preserve only the most significant features. Massive preprocessing-normalization, balance, and cleaning-were performed on the massive TII-SSRC-23 dataset, reducing its size to 1 GB from 30 GB without losing most traffic features. The Random Forest and SVM models were trained and cross-validated. As a complement to prediction from datasets, the system has a new feature for real-time forecasting of network attacks through Wi-Fi or LAN interfaces in order to have maximum real-world utilization and responsiveness. The hybrid IDS is therefore better positioned to offer precision, responsiveness, and usability in real-world situations than the single-model IDS of the past.

Network Intrusion Detection Systems (NIDS) are utilized to find hostile network connections. This can be accomplished by looking at traffic network activity, but it takes a lot of work. The NIDS heavily utilizes approaches for data extraction and machine learning to find anomalies. In terms of feature selection, NIDS is far more effective. This is accurate since anomaly identification uses a number of time-consuming features. Because of this, the feature selection method influences how long it takes to analyze movement patterns and how clear it is. The goal of the study is to provide NIDS with an attribute selection approach. PSO has been used for that purpose. The Network Intrusion Detection System that is being developed will be able to identify any malicious activity in the network or any unusual behavior in the network, allowing the identification of the illegal activities and safeguarding the enormous amounts of confidential data belonging to the customers from being compromised. In the research, datasets were produced utilising both a network infrastructure and a simulation network. Wireshark is used to gather data packets whereas Cisco Packet Tracer is used to build a network in a simulated environment. Additionally, a physical network consisting of six node MCUs connected to a laptop and a mobile hotspot, has been built and communication packets are being recorded using the Wireshark tool. To train several machine learning models, all the datasets that were gathered—created datasets from our own studies as well as some common datasets like NSDL and UNSW acquired from Kaggle—were employed. Additionally, PSO, which is an optimization method, has been used with these ML algorithms for feature selection. In the research, KNN, decision trees, and ANN have all been combined with PSO for a specific case study. And it was found demonstrated the classification methods PSO + ANN outperformed PSO + KNN and PSO + DT in this case study.

Network intrusion detection system are significant features that contribute to the enterprises and organization network success. In the past decade, Intrusion Detection system (IDS) using several methods have been proposed and implemented to safeguard the networks within an organization to be reliable, secure, and readily available. Although, some of these methods are built utilizing machine learning concepts. IDS utilizing machine learning techniques are accurate, effective and efficient in spotting attacks. However, the machine learning model performance reduces with high dimensional intrusion dataset. Therefore, it is sacrosanct to implement an efficient feature selection technique that can generate a great impact on the classification stage. Also, the feature selection phase is the most serious phase in IDS based on machine learning. This phase is expensive both in time and efforts. Furthermore, most of the proposed machine learning intrusion detection system suffered from low detection accuracy and high false positive rate when the models are experimented on high dimensional dataset. The aim of this paper is to propose a binary firefly algorithm (BFFA) based feature selection for IDS. We first performed normalization in the first stage of the model. Subsequently, the BFFA algorithm was used for feature selection stage. We adopted random forest algorithm for the classification phase. The experiment was performed on high dimensional University of New South Wales-NB 2015 (UNSW-NB15) dataset with seventy five percent of the data used for training the model and twenty percent for testing. The findings showed an accuracy of 99.72%, detection rate of 99.84%, precision of 99.27%, recall of 99.84% and F-score of 99.56%. The results were gauge with the state-of-the-art results and our results were found outstanding.

With the advent of anomaly-based intrusion detection systems, many approaches and techniques have been developed to track novel attacks on the systems.High detection rate of 98% at alow alarm rate of 1% can be achieved by using these techniques.Though anomaly-based approaches are efficient, signaturebased detection is preferred for mainstream implementation of intrusion detection systems.As a variety of anomaly detection techniques were suggested, it is difficult tocompare the strengths, weaknesses of these methods.The reason why industries don"t favor the anomaly-based intrusion detection methods can be well understood by validating the efficiencies of the all the methods.To investigate this issue, the current state of the experiment practice in the field of anomaly-based intrusion detection is reviewed and survey recent studies in this.This paper contains summarization study and identification of the drawbacks of formerly surveyed works..

Wireless Sensor Networks (WSNs) have become a hot research topic in recent years. They have many potential applications for both civil and military tasks. However, the unattended nature of WSNs and the limited computational and energy resources of their nodes make them susceptible to many types of attacks. Intrusion detection is one of the major and efficient defence methods against attacks in a network infrastructure. Intrusion Detection Systems can be seen as the second line of defence and they complement the security primitives that are adopted in order to prevent attacks against the computer network being protected. The peculiar features of a wireless sensor network pose stringent requirements to the design of intrusion detection systems. In this paper, we propose a hybrid, lightweight, distributed Intrusion Detection System (IDS) for wireless sensor networks. This IDS uses both misuse-based and anomaly-based detection techniques. It is composed of a Central Agent, which performs highly accurate intrusion detection by using data mining techniques, and a number of Local Agents running lighter anomaly-based detection techniques on the motes. Decision trees have been adopted as classification algorithm in the detection process of the Central Agent and their behaviour has been analysed in selected attacks scenarios. The accuracy of the proposed IDS has been measured and validated through an extensive experimental campaign. This paper presents the results of these experimental tests.

Intrusion detection is a familiar phrase in the information and network security domain. An Intrusion Detection System (IDS) is a device or software that will keep track of the networks, for unlawful movements, and policy breaches that arise within the network. There are different forms of IDS, Host Intrusion Detection System (HIDS) helps in identifying unauthorized activities on the host, Network Intrusion Detection System (NIDS) helps in identifying attacks in the network, whereas Distributed Intrusion Detection System (DIDS) consists of multiple IDS over a large area of network where individual IDS communicates with each other or with the central the authorized central server. The proposed work has a three-layered architecture for DIDS for securing data sharing among different IDS. The bottom layer uses multiple IDS, the fog layer is supported with Blockchain functionality, and the cloud service at the upper layer stores required data permanently for future analysis. The fog computing-based architecture for DIDS tries to implement the application in a scalable and trustless environment using distributed ledger technology. The evaluation of the proposed work is carried out for fog, cloud, and integrated fog-cloud with the Blockchain functionality and without Blockchain functionality in measuring performance metrics related to throughput, service latency, response time, block creation time, and block execution time.

Intrusion and intrusive activities have become a bottleneck for both Internet and Intranet users. An intrusion detection system tries to take care of such activities by constantly monitoring the user systems. Although there are two basic approaches in intrusion detection, i.e., misuse detection system and anomaly detection system, respectively, however, recent research works emphasize on hybrid approach which tries to gather the advantage of both misuse and anomaly-based systems. The proposed research work is based on such a hybrid system which uses misuse detection system for known types of intrusions and anomaly detection system for novel attacks. Frequency episode extraction method is specifically used for misuse-based detection and chi-square test is used for anomaly-based detection. Experiments show that the hybrid intrusion detection system is able to consider the real-time traffic of a network as well as the standard available data set for detecting the efficiency of the system. The proposed system learns and trains itself by detecting known attacks from misuse detection system and novel attacks from anomaly detection system, thereby improving the true positive rates and diminishing false negative rates consequently.

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. …

Cyberspace is a concept describing a widespread, interconnected digital technology with numerous users. New standards add more concerns with tremendous information gathered from various network sources, which can be utilized for focused cyber-attacks. Digital attacks in networks are getting more complex and subsequently introducing expanding difficulties in precisely distinguishing network intrusions. Inability to forestall the network intrusions could debase the validity of safety administrations, for example, information privacy, trustworthiness, and accessibility. In this work, we center around developing a Network Intrusion Detection System which is implemented utilizing Machine Learning Techniques. IDS dependent on ML techniques are successful and exact in distinguishing varied networks intrusions. Also, many of the ML-based IDS experience the ill effects of an increment in false-positive rate, leading to lower precision and accuracy. Consequently, we present an exploration of the UNSW-NB15 intrusion detection dataset that will be utilized for preparing the machine learning models. In our tests, we carry out various ML approaches namely, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest Classifier (RF), Decision Tree (DT), Gradient boosted decision trees (GBDT). The results demonstrated that the hyperparameter optimization technique using Grid Search and Random Search methods and evaluation using k-fold cross-validation gives better optimization of model parameters and henceforth brings about better execution.KeywordsCross-validationCyberattackDecision tree (DT)Gradient boosted decision trees (GBDT)Hyperparameter TuningLogistic regression (LR)Network intrusion detection system (IDS)Random forest classifier (RF)Support vector machine (SVM)

Critical infrastructure protection and national security are enhanced by the security and reliability of networks. Various types of information circulate on these networks, ranging in classification from open to closed. The consequences of cyberattacks on these networks can be severe, including reputational damage, financial loss, operational disruption and data leakage. Traditional security methods, such as firewalls and anti-virus software, are becoming less effective against modern and ever-changing cyber threats. As a result, powerful network intrusion detection systems (IDS) have become indispensable for proactive detection and mitigation of cyber attacks. Machine learning has become a viable method for creating adaptive intrusion detection tools that can detect new and complex attack patterns. By learning from huge labelled network traffic datasets, ML models can understand the subtle patterns and differentiating features of normal and abnormal or malicious traffic flows. This allows them to detect possible cyber threats and intrusions that traditional signature-based IDSs cannot detect. Extracting discriminative features and training appropriate classification models from such data is a challenging task. In the presented study, we analyse the effectiveness of ML algorithms for detecting cyberattacks, in particular distributed denial of service (DDoS) attacks, in network traffic data. In the presented study, a network attack detection system is developed using ML and deep learning (DL) models and experimented on the CICIDS2017 dataset. The main objectives of the study are to develop a strategy for extracting valuable information from raw network streams; to study the impact of data preparation on the false positive rate; and to conduct a comparative analysis of ML models for cyberattack detection. The main goal of the study is to provide an understanding of the development of a reliable adaptive network intrusion detection system using ML approaches that increase cybersecurity capabilities and protect against future cyberattacks.

Over the years, the vulnerability of the network system has completely revolutionized the security system. Attackers simply exploit these exposures to gain undue access to network resources. It is essential to safeguard network resources with the Intrusion Prevention System (IPS) and Intrusion Detection System (IDS). Hence, to develop an optimized IDS model using ensemble modeling of machine learning technique is paramount. Therefore, this research attempts to build a network IDS using an ensemble of Decision Trees (DT) algorithms to optimize an IDS model. Furthermore, the C4.5 DT classifier used on the University of New South Wales Network-Based 2015 (UNSW-NB15) network data set. Then the ensemble techniques of bagging and AdaBoost are compared during the performance evaluation. The results of this work depict that performance increase as training size increases. Consequently, the results showed that adopting the ensemble model for the C4.5 decision tree classified improved network intrusion classification compared to using the machine learning algorithm in isolation. Moreover, the result also showed an improved classification model for network IDS model. The AdaBoost ensemble model of the C4.5 DT algorithm using partitions of 90% of training and 10% of testing data sets performs the best with 98% accuracy and precision.

This paper describes the ESIDE-Depian intrusion detection and prevention system, which uses Bayesian structural and parametric learning and also evidence propagation and adaptation, in order to improve the accuracy and manageability of network intrusion detection systems (NIDS). Current NIDS do not consider the two main detection paradigms, i.e. misuse detection and anomaly detection, in an unified style, so the analysis is not inherently complete. Besides, historical data are not generally used, neither for analysis nor for sequential adaptation of the knowledge representation models used for detection; hence this wealthy information about the essence and the potential trends of the target system is not commonly considered. Thus, by the generalized use of Bayesian belief networks, ESIDE-Depian achieves the main goal of detecting and preventing both well-known and also zero-day attacks with excellent results, by means of unified real-time analysis of network traffic.

Intrusion detection in Mobile Ad-hoc Networks: Bayesian game formulation