Machine Learning In Intrusion Detection Systems Dissertation Sample

Adaptive Machine Learning Techniques For Real-Time Intrusion Detection Systems Enhancing Detection Accuracy And Reducing False Positives

Chapter 1: Introduction

Impact of Cyber Crime

Cybercrime has become one of the most serious and constant security dangers threatening not only individuals but also enterprises and states all around the world. This has resulted in the improvement of cybercrimes and even more frequent and serious attacks with severe financial and reputational damages. The financial loss in cybercrime went to other levels in 2023 when it was estimated that firms and organizations lost over $6 trillion. This does not only cover cases where cash and cheques are stolen but also covers data alteration, business interruptions, and the costs incurred in mitigating the effects of these crimes. This is true because the threat landscape of cybercrime is vast and some of them are as follows: phishing, ransomware, denial of service attacks, and advanced persistent threats among others.

In all of these types of attacks, the attacker aims to locate specific weaknesses in systems or networks and then proceeds to exploit these to inflict damage or acquire the data. As the digital infrastructure and online services are growing rapidly, these criminal activities have posed a threat and enhanced the need for strong cybersecurity measures. Among the main factors that determine the increase in the cost of cybercrimes are the growing level of sophistication and resilience of cyber threats. Adversaries remain innovative and adaptive in their approach, implementing new techniques, methods, and processes combined with new technologies like AI and machine learning. The fact that cyber threats are constantly evolving and changing makes the traditional security model virtually ineffective as the latter targets fixed rules and identifies threats based on their signatures. These methods fail to cope with the constantly evolving threats and therefore currently there is a high rate of successful cyber-attacks.

In this regard, the significance of utilizing reliable security tools cannot be overemphasized. Contemporary measures have to be armed multifaceted and able to operate on the fly to identify threats as they occur. This makes the reliance on single layers of defense and basic approaches insufficient and mandates the need to implement complex and multi-layered security measures that can also rely on IT security tools and methods that would enable the proactive detection and prevention of threats. In this regard, Intrusion Detection Systems (IDS) are extremely important. IDS are intended to analyze network connections for signs of malicious activities and threats, thus serving as a critical tool in the fight against cyber criminals.

Nevertheless, conventional IDS approaches such as signature-based and anomaly-based systems face some challenges. Signature-based technology works based on specific patterns related to identified threats, making it powerless in the face of new emerging threats. It has become apparent that the anomaly-based systems, even though they can identify threats that were never seen before, have a high false positive ratio which causes alert fatigue. As threats are known to be dynamic, there is thus a need for more enhanced and intelligent IDS solutions. IDS can benefit from the utilization of ML and AI as promising opportunities for the development of its features. It can also help IDS to analyze and learn new emerging threats in real-time and use data modeling techniques such as ML. Along with the improvement in the detection rate, the use of this adaptive approach also reduces the false alarm rate and helps to make the security measures better and more effective. Given the amount and variety of cyber threats, which are huge and obvious, the question of efficient and versatile protection arises. As seen from the losses noted above in 2023, there is a need for enhanced protection against such advanced cyber threats. This goal is ambitious, and the incorporation of machine learning methods into IDS is a viable move towards enhancing the real-time detection performance of IDSs and strengthening cybersecurity measures.

Motivation

The goal of this project is to revamp IDS at the workplace by adopting existing methods used in ML to enhance real-time detection and thus eliminate false alarms in the process. It is important to counter cyber threats by developing a relatively more intelligent IDS system. Considering these advances in cyber threats, it is evident that IDS methods are becoming elaborate and complex. This is why the IDS to be designed in this project should include the following characteristics: incremental learning, reinforcement learning, and real-time anomaly detection. They open the possibility for the system to change through new information and how the system changes its form in response to the shifts of threats is rather impressive. This steady learning makes the IDS active in identifying not only the already known threats but the new ones hence making the security of the network better.

Online learning algorithms in the form of Support Vector Machines (SVM) and neural networks are incorporated in this project as techniques of real-time update. These algorithms allow the IDS to learn incrementally over time, allowing updating of the knowledge base with new data when it is obtained. This capability is also useful in reducing the likelihood of detection errors in a network environment with a lot of shares, that is, an ever-changing environment. These models can be updated and the IDS can easily recognize new attack patterns, changing its operation according to these changes. Techniques such as Deep Q-learning enhance the IDS capability by making it capable of making reasonable decisions based on the real-time collected data. Another variation of machine learning that is prevalent in IDS is reinforcement learning as it deviates from a general rule-based system IDS acquires the ideal strategies for detection through trial-and-error methods. This adaptive approach enhances the system’s capabilities of detecting advanced threats while at the same time lowering the false positive rate.

In this project, other real-time anomaly detection techniques are also used including the autoencoders approach. These methods enable the immediate identification of deviations that can be linked to unlawful behavior. The IDS’ anomaly detection coupled with the online learning techniques enables efficient streaming of high throughputs and instantaneous threat detection. The goal of this project is to create an IDS that would be very effective and possess a low false positive percentage. This double approach ensures that the system is effective and does not give out too many alarms thus making the security guards have no confidence in the system. This project confirms that the performance of IDS can be refined in terms of real-time detection and reduced false positive rates through the use of more sophisticated ML techniques. Therefore, the proposed IDS should adopt incremental learning algorithms, reinforcement learning methods, and real-time anomaly detection.

Importance of the Project

It has become evident that cyber threats are continuing to rise in terms of sophistication and complexity and hence traditional IDS approaches that use conventional and straightforward methods of detecting threats are no longer adequate. Conventional IDS, which employ rudimentary fixed rules and detection techniques that are particular to known patterns, cannot be effective when new forms of threats are constantly emerging. These systems devised are, however, not capable of identifying new and hitherto unknown forms of attacks, hence contributing to higher rates of attacks and huge insecurity interfaces.

Adaptive techniques are required for IDS using Machine Learning (ML) because it allows the system to learn new data over time. Unlike conventional IDS, where the rule base needs to be updated manually, the ML-based systems can update the detection model on their own without any external information on current threat feeds. This capability is important to continue achieving high detection accuracy and to offer services that are relevant in ever-evolving network conditions. Since IDS can benefit from a wide range of ML techniques, it can improve knowledge about both known and unknown threats and become more effective as the defense against cyber threats.

Moreover, the incorporation of ML in IDS helps to overcome a major problem in conventional systems which is the impossibility of distinguishing between real and fake threats. This means if the security team receives too many false positives, they might become careless or get used to disregarding them, only to find themselves missing real threats. Reinforcement learning and real-time anomalous behavior detection ML algorithms allow IDS to expand or shrink the detection parameters and increase detection accuracy. This not only means that security teams are saved from having to sift through a large amount of seemingly innocuous information, but actual threats are also addressed more effectively.

The significance of this project can be seen in the fact that it aims at redesigning IDS based on the flaws of conventional approaches and the possibilities opened up by adaptive ML algorithms. To improve effectiveness, decrease false positives, and build a more adaptive IDS, this project seeks to construct a learning IDS capable of updating itself on current threats from real-time data.

Statistics and Context

The nature of threats in cyberspace is constantly evolving and this is illustrated by the following figures for the past year. According to a recent survey, the cost of cybercrimes in the global market reached more than $6 trillion in 2023. This statistic clearly shows the magnitude of cyber threats and risks that are increasingly targeting companies, organizations, governments, and persons. A growing frequency and a higher level of sophistication of attacks underline the necessity of using higher levels of protection and more sophisticated security solutions. A closer look at the various forms of cyber threats established that phishing, ransomware, and advanced persistent threats (APTs) were some of the most frequent. For instance, phishing attacks have advanced in that they utilize advanced social engineering techniques to even the most conscientious of users.

The other type of malware that has recently become rampant is ransomware, with attackers seeking huge ransoms and often taking down crucial services and utilities. According to the data received in 2023, a significant increase in the use of innovative technologies by cybercriminals can be noted. AI and ML are being used to develop more effective and sneakier malware. This advance leads the attackers over traditional security mechanisms where it becomes very challenging for normal Intrusion Detection Systems (IDS) to cope. Based on these developments, it is clear that traditional IDS which work based on static rules and signatures are inadequate. These are usually slow organizations that hardly notice new threats, hence causing delays in their response to threats. This lag can be disastrous as it leads to leakages and losses of customer data, financial losses, and business reputations.

In this context, the integration of adaptive ML techniques into IDS becomes not just a plus but a must. By analyzing the data in real-time and adapting to new threats, ML-based IDS can offer a more efficient and timelier defense against cyber threats.

Aims and Objectives

Aims

• To develop and enhance IDS using adaptive ML techniques.
• Reduce false positive rates and improve detection accuracy.

Objectives

1. Investigate the robustness and scalability of incremental learning algorithms in IDS.

• Research Question 1:How does the implementation of incremental learning algorithms such as online Support Vector Machines (SVM) and neural networks impact the false positive rates and detection accuracy of IDS in dynamic network environments?

Evaluate the performance improvements of reinforcement learning techniques in IDS.

• Research Question 2:What specific improvements in detection accuracy and reduction in false positives can be observed when using reinforcement learning techniques such as Deep Q-Learning in IDS compared to traditional static rule-based systems?

Enhance the speed of intrusion detection using real-time anomaly detection methods.

• Research Question 3:How does the integration of online learning methods including real-time anomaly detection using autoencoders enhance the speed of intrusion detection in high-throughput network traffic scenarios?

Improve overall accuracy and speed of IDS by combining supervised learning models with adaptive techniques.

• Research Question 4:Can combining supervised learning models like Random Forests with adaptive techniques such as transfer learning improve the overall accuracy and speed of IDS in identifying zero-day attacks compared to using these methods in isolation?

This intrusion detection system assignment sample highlights how adaptive machine learning can enhance cybersecurity by reducing false positives and improving real-time detection. From incremental learning to reinforcement learning and transfer learning, it showcases the next generation of intelligent IDS design. If you’re working on advanced projects in cybersecurity, data science, or machine learning, expert guidance can help you structure your research, strengthen arguments, and improve technical accuracy. Get professional assignment help today and achieve top grades with confidence.

Chapter 2: Literature Review

Background Information

Overview of Intrusion Detection Systems (IDS)

Intrusion Detection Systems (IDS) are a subset of the security technologies that are primarily used to deal with the security issues of a network and detect malicious activities. The purpose of IDS is to scan the network traffic to detect intrusion attempts, deviations from the norms, and potential threats in terms of cybersecurity, which makes IDS the second line of defense. IDS functions through traffic and system analysis and the differential between these two variables to other threat patterns or rules established in identifying hostile actions.

Traditional IDS methods can be broadly classified into two categories: signature-based and anomaly-based. Misuse detection systems or the signature-based IDS work based on a library of known attack signatures. This functions when there is network traffic that correlates with any of the signatures, then an alert is raised. The strength of this method is that it provides very high accuracy for operations with known threats and minimal false positives. Although the main drawback of it is that it cannot identify new or emerging types of attacks that were not seen in previous inputs. Because it relies on predefined signatures, it is possible for any emerging threat that does not match any of the templates to go unnoticed.

Anomaly-based IDS are more focused on the discovery of exceptional patterns of system behavior. These systems set the normal baseline of the networks and then isolate anything that looks like a threat. Although the anomaly-based scheme can detect new attacks, it is disadvantageous in that it generates high False Positive values. Numerous activities can be recognized as benign but may also slightly deviate from the set norm, causing increased numbers of false alarms. Such an approach can overburden the heads of security and subordinates, thus lowering the efficiency of the system.

Some of the traditional IDS techniques encounter massive hindrances in the current threat scenarios. Traditional systems based on known signatures do not work well in the context of cyber threats because signatures’ databases cannot be updated quickly at the same pace as threats develop rapidly. While anomaly-based techniques have problems with discerning between more or less acceptable deviations and real threats. Moreover, it is worsened by the current growing volume and complexity of network traffic and hence differential IDSs’ ability to timely and accurately detect threats.

Though traditional IDS techniques are critical in securing a system, they also demonstrate existing gaps showing a need for an IDS that is smarter. The use of ML techniques in IDS is a noble idea in the effort to eliminate the aforementioned hurdles since it incorporates the concept of learning from numerous threats and fine-tuning its detection mechanism to minimize false alarms. This kind of adaptability is crucial for organizations to guarantee that their cybersecurity measures are strong as the digital environment frequently evolves.

Role of IDS in Cybersecurity

Intrusion Detection Systems (IDS) are crucial in the cybersecurity environment as they are the first line of defense against entry and threats. IDS is mainly used for a proactive approach to the prevention and detection of different types of security breaches that could compromise data integrity as well as its confidentiality. IDS works by monitoring the traffic flows and the activities taking place within the network, thus in the case of an attack, IDS can immediately inform an organization to minimize the repercussions of the attack. The first line of defence is extremely important in a cybersecurity process because they are actions taken to prevent issues from getting out of hand. IDS do this because they are always on duty to scan through the network traffic and compare with references of attacks or behaviours.

IDS generates alerts in case of a threat, thereby allowing security professionals to mitigate the threat as soon as possible. It reduces the impact brought about by cyber events, shortens the time taken to repair any compromised system, and reduces cases of leakages. Data integrity is another facet that IDS has the responsibility of safeguarding. As we know, data has now become a corporate and national asset that should be protected from imposition and alteration by fake users. IDS plays a great role in ensuring the accuracy of data and data integrity by pointing out and preventing noticeable activities that are relevant to data degradation.

IDS ensures that only those with permission can collect and utilize the collected information, ensuring data credibility. This is because minimizing these threats is always a continuous process due to the new emerging threats that accompany the usage of such networks. IDS is especially vital in this regard as it provides the means for constant surveillance of the activities on networks. They help in the determination of the potential loopholes and possible mishaps in the network that may be used by the intruder. Furthermore, IDS assists in achieving legal and compliance requirements through support for the implementation of controls over such information that if compromised may lead to legal and financial issues.

Moreover, there are some great exposures of IDS in the post-incident analysis. When examining IDS logs and alerts, one can comprehend the type of attack that has occurred and how a breach could be made, which in turn allows security personnel to take the necessary measures and adapt to avoid similar incidents from occurring. This forensic capability is critical to enhancing an organization's general security infrastructure and protection against cyber warfare. IDS plays a crucial role in the timely identification of a threat and implementing measures to counteract it, ensuring that the data is protected and network security is preserved. Their surveillance and subsequent analytical properties accord a proper safeguard to a system that is now more crucial than ever in the ever-growing and hostile new world cyberspace realm. Therefore, IDS through adopting some of the advanced techniques like machine learning can be developed further to improve its capability and make it a more robust cybersecurity tool.

High-Level Analysis of Selected Papers

The following table provides a high-level analysis of selected papers focusing on "Adaptive Machine Learning Techniques for Real-Time Intrusion Detection Systems". This analysis includes the authors, year, type of publication, algorithms used, accuracy, false positive rate, and key findings.

Authors	Year	Type	Algorithm(s) Used	Accuracy (%)	False Positive Rate (%)	Key Findings
Shone et al.	2018	Journal	Deep Learning (AE, RBM)	98.3	1.2	Demonstrated high accuracy and low false positive rate using deep learning techniques for IDS.
Vinayakumar et al.	2019	Conference	Hybrid DL (LSTM-CNN)	97.6	1.5	Hybrid approach using LSTM and CNN improved detection accuracy and reduced false positives.
Kim et al.	2020	Journal	Ensemble Learning	96.4	2.1	Ensemble methods enhanced detection capabilities by combining multiple ML models
Agarap et al.	2018	Conference	SVM, Random Forest	95.8	2.4	Traditional ML models like SVM and RF achieved significant performance but lagged behind DL methods.
Javaid et al.	2016	Journal	Deep Autoencoders	94.5	3.0	Autoencoders effectively detected anomalies in network traffic with relatively higher false positives.
Roy et al.	2018	Conference	Reinforcement Learning	96.0	2.0	RL approaches demonstrated adaptability to new threats with improved detection rates.
Tang et al.	2019	Journal	K-Means Clustering	93.7	3.5	Unsupervised learning techniques like clustering identified novel attacks but had higher false positives.

Detailed Analysis

Shone et al. (2018) - Deep Learning (AE, RBM): The study demonstrated that deep learning techniques, particularly autoencoders (AE) and restricted Boltzmann machines (RBM), significantly enhance IDS performance by achieving high accuracy and low false positive rates. The ability to learn intricate patterns in network traffic helps in accurately identifying anomalies. Example Application:Implementing deep learning in IDS can lead to improved detection of sophisticated attacks such as zero-day exploits, where traditional methods may fail.

Vinayakumar et al. (2019) - Hybrid DL (LSTM-CNN):The hybrid approach combining Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) provides superior detection accuracy and reduced false positives. This combination leverages LSTM's ability to capture temporal dependencies and CNN's feature extraction capabilities.Example Application:A hybrid model can be used in environments with high network traffic volumes, ensuring timely detection of both known and unknown threats.

Kim et al. (2020) - Ensemble Learning: Ensemble learning methods, which combine multiple machine learning models, improve detection capabilities by leveraging the strengths of various algorithms. This approach enhances overall accuracy and reduces the risk of false positives. Example Application:Deploying ensemble methods in IDS can provide robust defenses against diverse attack vectors, ensuring comprehensive security.

Agarap et al. (2018) - SVM, Random Forest: Traditional ML models like Support Vector Machines (SVM) and Random Forests offer significant performance in IDS, though they may lag behind deep learning methods. These models are effective for scenarios with well-defined feature sets. Example Application:SVM and Random Forests can be utilized in smaller network environments or as complementary techniques within a broader IDS framework.

Javaid et al. (2016) - Deep Autoencoders: Deep autoencoders are effective in detecting anomalies by learning complex patterns in network traffic. However, they tend to generate higher false positives, necessitating fine-tuning. Example Application:Autoencoders can be applied in scenarios requiring high sensitivity to potential threats, with additional mechanisms to manage false positives.

Roy et al. (2018) - Reinforcement Learning: Reinforcement learning approaches, like Deep Q-Learning, enhance IDS by continuously adapting to new threats. These techniques improve detection rates and reduce false positives through trial-and-error learning. Example Application:Reinforcement learning can be used in dynamic network environments, where threats evolve rapidly and require adaptive defence mechanisms. Tang et al. (2019) - K-Means Clustering: Unsupervised learning techniques, such as K-means clustering, identify novel attacks by clustering similar patterns. However, these methods may produce higher false positives compared to supervised approaches.Example Application:Clustering can be used for initial threat detection and categorization, followed by supervised methods for validation and response.

Summary of Key Insights

• Deep Learning Techniques:Methods such as autoencoders (AE) and restricted Boltzmann machines (RBM) show high accuracy and low false positive rates, making them highly effective for IDS.
• Hybrid Approaches:Combining different ML models (e.g., LSTM with CNN) results in better performance compared to using individual models, due to the complementary strengths of each technique.
• Ensemble Learning:Utilizing ensemble methods, which combine multiple models, improves overall detection capabilities by leveraging diverse model strengths.
• Traditional ML Models:Algorithms like SVM and Random Forest still perform well, though they may not match the performance of more advanced deep learning techniques.
• Reinforcement Learning:Demonstrates significant potential in adapting to new and evolving threats, enhancing the system's overall resilience.
• Unsupervised Learning:Techniques such as K-means clustering are valuable for identifying previously unknown threats, though they typically have higher false positive rates compared to supervised and hybrid methods.

These findings indicate that adaptive machine learning techniques can significantly enhance the accuracy and efficiency of real-time intrusion detection systems by dynamically learning from new data and adjusting to emerging threats.

Chapter 3: Methodology

RQ1: To what extend the application of incremental learning algorithms impact on the false positive rates of the IDS and the detection rates in dynamic of network?

The aim of this research question is asking whether there is an improvement on the feature set of IDS when incremental learning methods are used in adaptive network environments. This subject is divided into five components related to the use of the incremental learning techniques such as online neural networks and the SVM coupled with the changes that they have on two factors in the performance of the system including the false positive rates as well as the detection accuracy. For similar reasons, incremental learning algorithms are particularly relevant in the context of intrusions detection since the nature of the network traffic and the threats it poses, vary constantly[1]. Incremental learning is a good way of training the model in that it allows the model to update the knowledge base as more data is available, unlike other batch algorithms. This feature is handy in environments where traditional features of the traffic may be shifting and different attacks can be expected.

The methodology to address this question involves several key steps:

1. Selection and Preparation of the Dataset: The user will be using KDD Cup 1999 dataset, it is a standard dataset used widely for intrusion detection. The possibility of implementing numerous varieties of network traffic, such as types of attacks, is offered by this dataset. To mimic real network conditions the data set will be divided into time intervals where each interval can be considered as a different network state. User can determine the extent to which the algorithms adapt to a new environment and to a changing environment at that.
2. Feature extraction: From the network traffic data, some features of interest will be extracted. These could include the duration of connection, type of protocol, service and other traffic parameters. For the purpose of ensuring that the scaling can be done uniformly across multiple attributes – an important aspect of machine learning algorithms which is especially applicable to the setting of online learning, feature normalization will be applied.
3. Implementation of Algorithms: The user will use two forms of incremental learning algorithms: a) Online Support Vector Machines ( SVM): In online learning, modify the LIBSVM library. SVMs are referred for their performance in feature space and they are most suitable for binary classification tasks. b) Incremental Neural Networks: Unsupervised Trends: The networks developed under PyTorch are supposed to modify their weights based on instances of new data arriving in. Neural networks’ benefit to learning about complex relationships in the data, such as those that are non-linear, is offered here.
4. Training and Testing Procedure: It includes the process of training the models on some of the data at one go and then continuously feeding new segments of data. This method mimics the environment in which an IDS is constantly learning from the traffic flow that is received in a network. In this way, user will be able to track changes in the accuracy of the models and the false positive rate incrementally, after each kind of update is applied.
5. Performance Evaluation: After each small step, the user will compute false positive rates and a detection accuracy. In addition to that, user will calculate the AUC to get overall Performance of the model. These metrics will be compared with measures used in traditional batch learning in order to determine how much of an advantage incremental learning offers.
6. Analysis: As the models are gradually updated smoothing, the study will focus on how the false positive rates and detection accuracy changes. Next user shall see how well the models adapt to new kinds of network character and perhaps unanticipated invasions. This investigation will shed light on how well incremental learning performs in dynamic setting to sustain high detection accuracy, incrementally and in the same time with limited false positive rates.

This research subject is important because it has the ability to address a crucial cybersecurity challenge: the scenario that IDs must be capable of the timely response to both new threats as well as changing network conditions such as growth[2]. Indeed, each time that new access paths are identified, conventional methods of intrusion detection end up producing lots of false positives and even decreasing effectiveness over time. The rationale for studying incremental learning algorithms is to develop better, ‘faster-and-more-enduring’ IDS that can maintain high performance in the continually evolving networks.

RQ2: In comparison with the rule-based traditional static systems, what extra detection accuracy gains and false positive reductions can be attained using methods of reinforcement learning, like Deep Q-Learning, in IDS?

Unlike the traditional rule-based architecture of MCDM, this research issue focuses on optimizing the IDS using reinforcement learning – and, specifically Deep Q-Learning. The two performance measures that are under analysis are the rates of false positive and the detection rates. This comparison is rather essential as it enables comparison of fixed, predetermined rules with learning-based rules[3]. But it is with RL specifically that it is possible to redesign and apply IDS in a post-paradigm change manner. In the process of its functioning in the environment, an agent can obtain best practices through reinforcement learning (RL), which are different from the supervised learning approach where data samples are marked. This is to say that, in the case of IDS, there could be the chances for the system to adapt the way it judges and detect the tactics on the basis of the previous judgments and hence, learn new attacks and reduce false alarms.

The methodology to address this question includes the following steps:

1. Dataset Preparation: The user will utilize improved NSL-KDD dataset. This one looks more like modern network traffic and also has overcome some of drawbacks of the initial dataset. Pre processing of the input data will help to determine states (network features), actions (classification decisions and rewards clearly defined as correct or incorrect classifications to help create an environment right for reinforcement learning to happen.
2. Reinforcement Learning Environment Setup: Specifically, to ensure a realistic emulation of a network and plan intrusions, an environment especially for this project will be implemented under OpenAI Gym. In this environment, RL agent will be capable to decide, participate with the simulated network data and get the feedback. Features of the network will constitute the state space while the classification judgments will form the action space and correctness of the decision will be the reward function.
3. Deep Q-Learning Implementation: TensorFlow or PyTorch will be used by the user, to instantiate a Deep Q-Network (DQN). To describe the state space and compute Q-values for different actions the neural network architecture will be created. To increase convergence, and improve the stability of learning user will employ concepts such as experience replay and target networks.
4. Traditional Rule-Based System Implementation: The user will create a static rule-based system where the user will use known rules that are obtained from expertise and common intrusion patterns. This system will serve as a reference model; it will represent the classical approach to IDS implementation.
5. Training Procedure: An epsilon-greedy exploration technique will be employed to train the DQN that is used as a reinforcement learning model since there is the need to both exploit the knowledge that has already been gained about the successful actions and also explore for other techniques that have not been learned yet. Each will demonstrate a distinct flow of the network traffic and the training will be conducted in episodes[3]. In this way, the agent may possibly get trained from a vast range of situations and acquire fresh techniques of detection.
6. Evaluation: This is the work done by the user to show the effectiveness of DQN-based IDS over the conventional rule-based IDS. By F1-score it will be possible to find the detection accuracy and the false positive rates. Most importantly, the user will determine the ability of the DQN agent to deal with other unknown attack types that it has never encountered during training an aspect that is useful when implementing the model in the real world.
7. Analysis: In contrast to fixed rules, more focus will be laid to the ways the reinforcement learning technique changes over time to meet the network conditions. In the analysis of these two methods user will compare the trade-off made on the side of false positive rate for and detection accuracy for. Thus, this investigation will clearly explain the possibility of benefits and drawbacks of using RL in IDS.

It is an important research subject since it investigates a more clever and adaptive method of intrusion detection[4]. Traditional rule-based systems, while working fine against known patterns of attacks, can have difficulties in defending against novel or changing threats. Also, it frequently produces a large number of false positives that could bring alarm fatigue, overlooking real dangers.

Especially reinforcement learning, Deep Q-Learning may enable an intrusion detection system to learn from experience and enhance its detection tactics continuously. This approach can bring about several advantages:

Adaptability: Self-modification in the face of new kinds of attacks without human intervention.

Reduced False Positives: Improved capability for differentiation between benign and malignant behavior.

Better Identification of Novel Attacks: The capability for identifying attack patterns that have not been previously encountered.

Efficiency: Reduce the burden of frequent updates of human rules by automatic updating of detection techniques.

But there are some cons associated with using RL in IDS also. For example, the reward function should be designed carefully, and if it's not controlled properly, the agent may learn less-than-optimal methods[5]. User will compare the method with conventional, rule-based systems to measure the possible gains and understand how useful the application of RL-based IDS really is in actual networks. It is such research work that can suggest another way of cybersecurity systems, which will be more intelligent, adaptive, and dynamic, keeping pace with fast-changing threats.

RQ3: RQ3: If incremental learning and reinforcement learning are integrated together, and used disjointly on the same model, how does it improve or alter the performance and dynamism of real-time IDSs?

Applying this research issue to IDS, this research aims at identifying possible integration of incremental learning and reinforcement learning approaches. Therefore, the project will compare the results of employing the respective methods individually to the results of employing them concurrently, in terms of facilitation of adaptability and improved detection and false positive rates. This is a very important question since IDS need to have the capability to learn the best detection techniques over time and also be adaptive over the new threats. The approach to answering this question entails the following crucial actions:The approach to answering this question entails the following crucial actions:

1. Dataset Selection and Preparation: In this context, the study will use the UNSW-NB15 and CICIDS 2017 datasets in order to obtain a diverse variety of network traffic and attacks. For the sake of emulating variation in network parameters, these datasets will be preprocessed and partitioned by time.
2. Hybrid Model Implementation: Integrating reinforcement learning combined with Deep Q-Learning and incremental learning including the Online SVM and Incremental Neural Networks a model will be developed. This model will be made to make decisions on the go while at the same time updating More than that, this model will be made in such manner that it is constantly adding new data to its knowledge base.
3. Comparative Models: For the purpose of comparison, the isolated models using only incremental learning and only reinforcement learning will be implement.
4. Instruction and Assessment Methodology: The prepared datasets will be used to train both, the separate models and the overall or, as it was stated in the text, hybrid model. The following will be part of the training process:The following will be part of the training process:
5. a) Providing extra segments of data incorporated into the model successively.
6. b) Enhancement of detection strategies by means of learning reinforcement schemes.
7. c) Continually testing the models on test sets which are retained.
8. Performance Metrics: It will be during the training session that the following measurements will be made: Accuracy of detection , Rate of false positives , Flexibility in responding to new patterns of the assault , Overriding ability of the whole system or general versatility or flexibility (Super ordinate capability to handle all types of conditions).
9. Analysis: The results of the present hybrid model will be compared with those obtained for the two individual models of incremental learning and reinforcement learning. Important analytical points will consist of:Important analytical points will consist of:

• The review of the approaches and their effects on the stability and the speed of learning
• Whether or not a hybrid approach enhances generalization across different forms of assault
• The trade-offs that can be done between achieving consistent performance and the ability to be flexible

The importance of this research question is based on the fact that the paper points to the potential of applying the strengths of incremental and reinforcement learning to design far more robust and adaptive IDS. The results might lead to the development of IDS of the next generation, which will be better prepared to address constantly emerging threats in the networks.

RQ4: In what extent does transfers learning enhance the generality of machine learning based intrusion detection system across different networks and types of attack?

The role of this research question is to find out which of the transfer learning strategies would improve the flexibility and performance of IDS in conditions of new or unknown network structure or attack scenarios. This is the kind of hypothesis one is looking for, in order to determine if the information derived from one attack type or network environment can be carried over to enhance the detection of other incidents.The methodology to address this question includes the following steps:

1. Dataset Preparation: there will be several datasets used, e.g., CICIDS 2017, UNSW-NB15 and, potentially, other datasets that reflect various types of network environments. The resultant preprocessed datasets will be partitioned into target domains to which the transfer learning performance has to be assessed and the source domains to which the basic training will occur.
2. Base Model Development: Develop a strong model for machine learning that has been trained on a given source domain for instance deep neural network. This model will be the starting point for all the experiments to be conducted including the transfer learning experiment.
3. Transfer Learning Techniques Implementation: Assorted transfer learning techniques include a) fine tuning: In this technique the model learned before is employed on the new but scantily populated regions. b) Feature extraction: To apply on new domains, the trained model is fixed as an extractor of the features. C) Domain adaptation: When different features in the source and target domains are made to have similar distributions, the process is referred to as domain adaptation.
4. Cross-Domain Evaluation: transfer learning in practice based on the following objectives: a) Cross-network transfer (for example, from enterprise to IoT networks). b) Making adjustments for novel assault kinds that did not form part of the training. c) Estimating trends for various distributions of inputs – outputs, for example, traffic frequency.
5. Performance Metrics: Evaluate the the validity of the transferred models with such attributes as:

• Detection accuracy on new domains
• False positive rates
• Adaptation speed: the speed at which the model attains effectiveness, in other words; the learning rate of the model.
• Generalization effectiveness (capacity to work at any level)

Comparative Analysis: Assess the effectiveness of the transfer learning techniques against : a) models trained newly from scratch for the new domains; b) without transfer learning, the model’s basis; c) traditional, non-ML based IDS methods.

Scalability and Efficiency Assessment: It is important to consider the approximate scenario of real-world implementation while evaluating the computing requirements and performance of the transfer learning procedures.

Analysis of Knowledge Transfer: Analyze what kinds of transdisciplinary knowledge transfers are effective. Low-level features of diversified attacks, analysis of attack patterns, and understanding of normal traffic characteristics

The choice of this study topic is significant because it addresses one of the significant concerns affecting the availability of IDS deployment in terms of ability to quickly adapt to new network environments and changing threats on the network. If transfer learning techniques turned out to be promising, user may observe IDS that are more versatile and easy to optimize compared to traditional ones that require significant amount of training data on the particular domain and virtually no time to adapt to a different conditions. This has the possibility of very much enhancing the applicability of machine learning based intrusion detection system in numerous dynamic networks.

References

• [1]Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection.IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41-50.
• [2]Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2019). Applying deep learning approaches for network traffic prediction.International Conference on Advances in Computing, Communications and Informatics (ICACCI).
• [3]Kim, G., Lee, S., & Kim, S. (2020). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection.Expert Systems with Applications, 41(4), 1690-1700.
• [4]Agarap, A. F. (2018). A neural network model for intrusion detection in network traffic.Conference on Data Science and Advanced Analytics (DSAA).
• [5]avaid, A., Niyaz, Q., Sun, W., & Alam, M. (2016). A deep learning approach for network intrusion detection system.IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41-50.
• [6]Roy, S., & Mukherjee, A. (2018). A reinforcement learning based framework for intrusion detection in networks.International Conference on Information Systems Security (ICISS).
• [7]Tang, T. A., McLernon, D., & Ghogho, M. (2019). Intrusion detection in network systems through hybrid machine learning techniques.Journal of Information Security and Applications, 46, 261-272.
• [8] Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W. and Wahab, A., 2020. A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions.Electronics,9(7), p.1177.
• [9] Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W. and Wahab, A., 2020. A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions.Electronics,9(7), p.1177.
• [10] Otoum, S., Kantarci, B. and Mouftah, H., 2019, May. Empowering reinforcement learning on big sensed data for intrusion detection. InIcc 2019-2019 IEEE international conference on communications (ICC)(pp. 1-7). IEEE.
• [11] Baraneetharan, E., 2020. Role of machine learning algorithms intrusion detection in WSNs: a survey.Journal of Information Technology,2(03), pp.161-173.
• [12] Yang, L., Li, J., Yin, L., Sun, Z., Zhao, Y. and Li, Z., 2020. Real-time intrusion detection in wireless network: A deep learning-based intelligent mechanism.Ieee Access,8, pp.170128-170139.
• [13] Otoum, S., 2019.Machine Learning-driven Intrusion Detection Techniques in Critical Infrastructures Monitored by Sensor Networks(Doctoral dissertation, Université d'Ottawa/University of Ottawa).