Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering

In intrusion detection, machine learning methods have proven to be rather requisite in the mainstays of all approaches. Some of the methods which have been implemented are decision trees, support vector machines, and k-nearest neighbors¹⁷. A further advantage mentioned is that models might be trained with new data to maintain their performance, which can counter changing threats¹⁸. For instance, decision tree based models are well applicable to the anomaly detection because they can easier recognize the differences between useful and destructive patterns of networks behavior. SVMs rely on the ability of separating the data space by a hyper plane to classify the occurrence and differentiate between safe and risky behaviors. As the K-nearest neighbors method defines it, comparing data is the way to find intruders. Strengthen the security of the network with the help of a large number of machine learning algorithms.

The capacity of deep learning approaches to automatically learn and extract complex patterns from vast datasets has propelled them to the forefront of machine learning. One popular application of deep learning architectures in intrusion detection is the use of Convolutional Neural Networks (CNNs), while Recurrent Neural Networks (RNNs) are also used¹⁶. CNNs excel at handling image-based attacks or multidimensional arrays of network traffic data. In contrast, RNNs excel at processing sequences and are hence well-suited to time-series network data. Improved precision in identifying complicated intrusion patterns is a direct result of their ability to capture temporal and geographical relationships in data.

In the evolution of swarm intelligence, some adaptations have been brought closer to a mix of exploitation and exploration models to achieve a much better correlation between as well as global and local research. The general PSO and ABC algorithms have been made adaptive and have been applied to actual life problems including scheduling and structural design. However, to date, new approaches are still being investigated continuously in an attempt at providing better algorithms especially for the constrained problem and for the multi-objective one. The Optimization Algorithm proposed in this paper¹⁹ is called the Greylag Goose Optimization, or GGO for short, and is a swarm-based heuristic model based on the motion of geese. GGO emulates the pattern of formation of geese to improve exploration as well as exploitation, with more effective searches realized. It has been verified on different benchmarks and engineering design problems and outperforms the existing optimization algorithms, especially in solving engineering design problems.

Meta-heuristic algorithms have become a favorite tool in solving optimization problems for their simplicity and ability to avoid convergence to local optima. Such algorithms, derived from natural analogues, differ in effectiveness due to the corresponding operators and heuristics used within a system. Among phenomena such as swarm intelligence and evolutionary algorithms, the most utilized in engineering, data mining, and machine learning. The Puma Optimizer, or PO for short, is a new algorithm that has just been created and is based on the life and intelligence of pumas with new exploration and exploitation approaches²⁰. In contrast, PO has some other unique characteristics, including a flexible phase-changing mechanism of the phase that can adapt to the specific problem of optimization for changing problems and increasing the efficiency for different optimization problems. The efficiency of the proposed PO algorithm has been tested on 23 standard benchmark functions, CEC2019 functions, and several machine learning tasks to perform clustering and feature selection tasks. Measurements indicate that the proposed PO is better than some current optimal solution search methods: in optimization tests, PO is better in 27 out of 33 problems and in clustering tests, PO provides better quality solutions in 7 out of 10 datasets. Also, PO has promising performance while addressing issues related to multi-layer perceptron (MLP) networks and community detection. These outcomes reveal that PO is a successful optimization tool for real-world applications for generating a stable solution with good opportunities to advance research work based on multi-objective optimization and civil engineering problems in the future.

One of the most important issues encountered in data preprocessing for building classification models is feature selection, which is aimed at dimensionality reduction. The “Apple Perfection” study used the Waterwheel Plant Algorithm (WWPA) to determine the key attributes affecting the quality of apples and succeeded in a considerable reduced computational load and an average error rate of 0.52153. This study showed what had been mentioned earlier that feature selection can bring about an improvement in the result by considering only the relevant attribute; Therefore, the logistic regression model that was used in the study had a classification accuracy of 88.63 %. In the same way, in intrusion detection, domain adapted feature selection can help improve the identification and categorization of the intrusion. By applying such methods as bWWPA or some other one, vital network features can be chosen, noise can be eliminated, and the machine learning and deep learning models’ performance can be maximized.²¹ Despite being a relatively new field, intrusion detection is now regarded as an essential component of network security that utilizes both classical and contemporary mathematical approaches. Intrusion Detection Systems (IDS) have been enhanced in the current world through new advancements within machine learning and deep learning methodologies. Self-organizing nature-inspired metaheuristic algorithms have shown some capabilities in the model optimization of IDS. This paper²² proposed the Greylag Goose Optimization (GGO) Algorithm based on the birds’ ‘V’ formation structure to improve exploration/exploitation. As a result, GGO was judged to be more effective than other methods in optimizing feature selection for IDS and solving complex engineering problems. It is important to use GGO to enhance ML models for IDS²². The synergism between dynamic strategies such as FbOA and the DL architectures represents the best practices profile. As a result, FbOA successfully applies high-dimensional and nonlinear problems, improving their convergence and making the whole process more robust as compared to the operations of football teams. In IDS contexts, such algorithms could help in adjusting deep learning models such as LSTM or CNN, applicable in analyzing sequential and complex data from network traffic.²³ Most of the reviewed nature-inspired optimization techniques can be incorporated into the proposed hybrid ML and DL models approach used in the current study. The usage of such optimization techniques can even advance model effectiveness, cut down the processing time, and increase the detection ratio. For example, incorporating GGO or PO during the feature selection step could enhance interpretability and part resistance against the zero-day attack.

AI and ML have huge possibilities of revolutionizing the education sector through the delivery of differentiated learning end products, identifying struggling learners, and designing content in response to student needs. Several prior works have investigated AI and ML approaches to education with most works using predictive modeling and feature selection for enhancing learning. Some of these algorithms include Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) wherein to make good predictions; the most relevant features are selected in Educational Data Mining. These techniques have shown promising results in such areas as students’ performance prediction and learning adapted models. In this study²⁴, Grand Canyon University examined the effects of AI on students’ learning with the aid of the following techniques: bPSO-Guided WOA for feature selection and Linear Regression for predicting. The results depict a comparatively small average error and an exceptionally small MSE underscoring the importance of these AI methods for forecasting and decision-making among educators. Examples include such advancements in AI as a way of creating more consistent and fuller education environments in the process of improving both teaching and learning.

Deep learning models are increasingly being adopted as key application tools in smart city development that enable traffic forecasting and streamlining of city frameworks. Among all these models such as VGG16Net, VGG19Net, GoogLeNet, ResNet-50, and AlexNet have been used for traffic data analysis where ResNet-50 and AlexNet exhibited higher accuracy sensitivity and specificity in traffic prediction²⁵. Further conclusions using valid statistical measures, ANOVA, and Wilcoxon Signed Rank Test are displayed and these show a significant difference between the performance of the specified groups of models while Alex Net yields an impressive accuracy of 0.93178. These outcomes signify that AI-powered solutions in the challenging field of traffic can improve the quality of urban mobility and support smart city projects; thus, organically feeding urbanists and policymakers with vital information on creating better and more efficient infrastructures.

Clustering and auto encoders are two examples of unsupervised learning techniques that might be useful for spotting anomalies.Anomalies depending on the level of network activity can be easily identified using a clustering algorithm like the K-means²⁶. Popularity of auto-encoder increase due to their capacity to reconstitute original data that can reconstruct “normal” network traffic with any discrepancies indicating a breach. These methods effective for autonomously identifying new attacks.

Similar to how people learn and adapt, reinforcement learning (RL) honors the constantly changing embodiment of network security. An agent in the RL-based intrusion detection works in a way that it tries to select a safety action that will lead to a highest long-term reward. Such decisions²⁷ include the ability to block or allow network traffic, turn on or off security policies, or response to a particular threat. The next information is acquired through ’learning from experience,’ where the agent modifies the strategy with reference to information, which in this case is provided by the network. This is the reason why, flexibility of RL is found very much useful in the field of intrusion detection.The traditional rule-based systems face difficulties to keep up with the ever-changing nature of cyber threats. RL agents have the capacity to learn optimal security rules and adapt new attack techniques in real-time through interaction with the network environment.Agent’s job in RL-based intrusion detection make safety choices optimize long-term rewards. Blocking allowing network traffic, modifying security policies, responding specific threats are examples of such decisions. The agent acquires knowledge by trial and error, constantly modifying its approach in light of the network’s input. The flexibility of RL is what makes it so effective in the field of intrusion detection. Traditional rule-based systems have difficulty keeping up with the ever-changing nature of cyber threats²⁸.

The use of ensemble learning techniques for intrusion detection in networks has shown promising results²⁹. Random Forests and Gradient Boosting are two examples of ensemble approaches that use the combined power of numerous models to boost detection precision. In essence, they pool the results of various models in order to make more informed decisions with less chance of error. Using a random sampling of characteristics and data from the training set, Random Forests construct numerous decision trees. The combined results from these trees form the basis for the ultimate choice³⁰. This method strengthens the model’s resistance to noise and outliers while enhancing its capacity for generalization. In contrast, the Gradient Boosting method builds a robust predictive model by iteratively constructing weak models that correct the shortcomings of their predecessors. This repeated technique yields an effective ensemble model that is particularly good at representing subtle interconnections in data. Ensemble methods have a reputation for being able to manage datasets in which malicious events are vastly outnumbered by benign ones. In the field of network security, they are used to examine heatmaps and other traffic data visualizations. Since attacks tend to look different from regular network traffic, CNNs are incredibly useful for spotting these abnormalities and identifying patterns within these heatmaps.

When it comes to dealing with sequential data, RNNs provide an alternative to LSTMs. In contrast to LSTMs’ strength in processing long-range relationships, RNNs’ strength lies in their ability to process and remember short-term dependencies and recent occurrences³¹. When it comes to intrusion detection, RNNs shine when they’re being put to use to identify multiple simultaneous threats. For spotting attacks that other models might overlook, their capacity to assess short time frames is crucial. RNNs improve intrusion detection systems’ real-time capabilities by taking recent network behavior into account.

When it comes to learning and reconstruction, auto encoders are the best type of neural network to use. Auto encoders are used to reconstruct regular network data for use in intrusion detection³². Input data is encoded into a lower-dimensional representation before being decoded back into its original form. When abnormal network traffic is introduced, the resulting reconstruction will also be abnormal. Since auto encoders may spot outliers in patterns without relying on preset attack signatures, they are ideally suited for detecting unknown or unique attacks.

Protecting user anonymity and data is essential for any secure network. With federated learning, models may be trained using data from numerous distributed sources without compromising on data’s locality³³. It enables companies to work together on threat detection and intrusion detection without disclosing private network information. Each region has its own dataset for training models, and only the most recent modifications to these models are shared between regions. In settings where regulatory compliance and data protection are of the utmost importance, such as healthcare and finance, this privacy-preserving method is vital, all the while reaping the benefits of the collective intelligence to improve network security.

Graph Neural Networks (GNNs) have become an indispensable resource for ensuring the safety of computer networks. Modern network topologies are notoriously complicated, with many devices and systems interconnected in complex ways that make intrusion detection particularly difficult. Graph-based data representation is harnessed by GNNs to solve this problem. In a network graph, nodes represent devices and edges represent relationships between them³⁴. For this purpose, GNNs can learn and propagate information across this graph structure to detect abnormalities that could be hard to identify using more traditional techniques. One of GNNs’ main advantages is that they can perform the analysis from two aspects at the same time, that is, the global features of the network data and the local features. As an ability to bring much-needed additional information into the decision-making process, XAI has become essential for intrusion detection systems. High levels of accuracy presented by a number of machine and deep learning techniques used for intrusion detection are often associated with the lack of explicit understanding of how and why specific decisions were made. But in real-world security systems where security professionals are to rely on and understand why the current warning is being given, this obscuration pose a real challenge. To address this issue, the main XAI methods have been created, LIME and SHAP³⁵. These methods generate justifications for model predictions, providing insight into the considerations that went into a given choice. XAI techniques, for instance, can shed light on the traits and patterns that lead to the detection of an intrusion. After gathering this data, security analysts will be better equipped to make choices, conduct investigations, and fine-tune security plans.

By assisting with the analysis of log files and textual data provided by network devices, Natural Language Processing (NLP) has expanded its reach into network security³⁶ System logs, firewall logs, and event logs are all examples of logs that offer useful information for intrusion detection but are also typically large and poorly organized. Natural language processing methods save the day by extracting and organizing this textual material, rendering it analyzable. The ability to recognize abnormalities or patterns in log data is an important NLP application for intrusion detection. Natural language processing (NLP) can analyze log entries and spot suspicious behavior like attempted intrusions or configuration changes. In the case of early intrusion detection, where the identification of security problems in a timely manner might reduce possible damage, this study is of paramount importance Detecting threats and associated hazards in a network is facilitated by their ability to incorporate past information and probabilistic reasoning to evaluate the likelihood of various network events. The capacity to model uncertainty is one of Bayesian Networks’ main capabilities. Managing uncertainty is particularly important in the context of intrusion detection, when both false positives and negatives can have severe repercussions³⁷.

Homomorphic encryption is a revolutionary method for protecting private information while still processing calculations on it. Homomorphic encryption provides a novel approach to protecting the privacy of network information in the context of intrusion detection. The adaptability of Markov models is one of its main selling points. Since circumstances in the network and the means of attacks vary they will be able to find themselves new opportunities and carry on with their malicious activity.

Ensemble clustering works as follows: in an attempt to improve the analysis of network traffic and hence the recognition of intrusions, the pieces unite various clustering algorithms. Algorithms for cluster analysis attempt to classify data into groups with comparable characteristics. Effective segmentation of network traffic data into meaningful groups, each representing a particular network behavior, is possible through the use of an ensemble of clustering algorithms such as K-means, DBSCAN, and hierarchical clustering. Ensemble clustering is a more general method of intrusion detection as it takes into consideration numerous different patterns in the network. Because different phases of an intrusion may display distinct behaviors, this is especially helpful in spotting multi-stage intrusions³⁸. Security professionals can better detect breaches and identify anomalies in network behavior by aggregating the results of various clustering exercises. Overfitting can be avoided, and the problem of datasets where benign activity is outnumbered by instances of malice can be overcome, thanks to ensemble clustering. This method improves the accuracy of intrusion detection systems by drawing on the features of several clustering methods, making it a flexible and effective tool for network security.

When dynamic security policies are required, reinforcement learning (RL) with a focus on Q-learning provides an attractive approach to intrusion detection. Since cybercriminals are always coming up with new ways to circumvent security measures, the landscape of network protection is anything from stable. Q-learning excels in this setting. Using this method of reinforcement learning, security experts may teach agents to take precautions that will have the greatest long-term impact³⁹. The optimal responses to network threats can be defined and rewarded in this way. Q-learning stands out because of its flexibility to adjust to new dangers. Q-learning allows businesses to implement dynamic security policies in response to new threats immediately. Q-learning agents successfully learn to adjust defensive strategies depending on their perception of the surrounding network environment in order to achieve the maximum level of certain benefits and risks. This flexibility is important in dynamic network situations where the strict application of rules of the static rule-based systems may not work. In order to assist businesses to enhance an improved level of security to safeguard their networks from current and emerging potent cyber threats that pose fatal consequences from security breaches, reinforcement learning with Q-learning is proven to be a proactive strategy for intrusion detection.

Intrusion detection depends mostly on the Principal Component Analysis because it can minimize the numbers of independent variables in a network. In a network setting, data could be very high dimensional in such a way that there are many irrelevant or redundant features. This is particularly so, especially when working with big data or big sets of data or when working in an environment with limited resources of time. Intrusion detection models benefit from data simplification since it allows them to run more quickly and with less computational cost.

Federated learning is a novel approach that improves the state of collaborative intrusion detection without compromising the safety of confidential information. In this approach, no data is centralized at any point, allowing participant businesses, endpoints, or edge devices to train machine learning models on their own data. The privacy of the network is protected because only model changes are shared instead of the original data. When it comes to protecting sensitive network information and staying in compliance with severe data privacy requirements, this cooperative technique shines⁴⁰. As the old saying goes, “united we stand, divided we fall.” Through federated learning, businesses can increase the swarm intelligence of their intrusion detection systems by combining knowledge and insights from a variety of sources. This federated method takes advantage of the unique qualities of each dataset to produce a flexible and powerful intrusion detection system. When information from several sources is combined, the ability to identify and counteract security threats is strengthened. Data remains decentralized and secure, while collective knowledge equips businesses to protect against sophisticated cyber threats.

When dealing with time-series data, Hidden Markov Models (HMMs) are very useful in the intrusion detection field. HMMs are a good fit for capturing temporal relationships in the context of network security monitoring, where activity patterns can change over time. Whether it’s the gradual appearance of anomaly or sequential acts of a multi-stage attack, HMMs excel at predicting such complex behavior. They offer a framework for figuring out and picking up on patterns of network behavior that could otherwise go undiscovered. HMMs are exceptional because their flexibility⁴¹. From more conventional corporate networks more complex industrial control systems, they are applicable across a wide range of network contexts. The flexibility of HMMs makes them useful in intrusion detection in a wide variety of settings. They are crucial the protection of networks because they help security professionals spot novel and sophisticated threats. HMMs guarantees that security measures are always in step with developing threat landscape as cyber adversaries continuously enhance their strategies.

XGBoost, short for Extreme Gradient Boosting, is a flexible machine learning algorithm that performs particularly ensemble learning for intrusion detection. pooling together model predictions, ensemble learning improves detection precision. XGBoost excels because it can handle large datasets and scale well. These features make it an excellent tool for ensuring safety of computer networks. XGBoost reduces the possibility of false positives and false negatives by combining predictions from many models⁴². It’s an effective intrusion detection system that can adjust new types of attacks and boost network safety. When dealing with complex, ever-changing threats in the real world, XGBoost shines. Due to high flexibility together with the ability to process massive data in a short time, it offers businesses a strong line of defense against the ever-emerging threat in the field of cyber security. XGBoost is not just an addition to the security arsenal in the field of Intrusion Detection System but also a key that is instrumental in enhancing the accuracy and dependability of such systems.

However, when it comes to intrusions detection, the highlight feature of Isolation Forests is that it employs the method of ensemble based anomaly detection. Isolation Forests are classified from standard ensemble methods that are directed on outliers compared to usual cases. The concept of them is such that while outliers are reasonably rare and therefore noticeable when set apart from the rest. The method utilizes individual decision trees where the complexity at which an individual tree is developed enables fast determination of anomalous points⁴³. This guarantees the efficient detection of intrusions by IDS while minimizing on false alarms normally associated with networks.

Intelligence for intrusion detection can greatly benefit from the organized representation of information offered by studies. Knowledge graphs make it easier to store, link retrieve information about dangers in this age of interconnected threats and adversaries. They give a complete picture of the dangers that exist by linking together things like vulnerabilities, exploits, malware, and attack methods. Informed decisions, the ability to spot new risks, and efficient responses network incursions are all made possible by this organized body of knowledge available to security analysts⁴⁴. Ability of intrusion detection systems to detect and prevent advanced threats in real time is greatly improved by the incorporation of knowledge graphs into the fusion of threat intelligence. Each of these methods exemplifies a different strategy for intrusion detection, revealing the many options that can be used. An adaptable and flexible security approach is necessary since best method to use is determined by factors such as the nature of the data being protected, the nature of the threats being faced, and the requirements at hand.

When it comes to finding security flaws in a system, quantum machine learning (QML) is a huge step forward. The theoretical foundations of quantum computing hold the promise of unprecedented processing power and information security. The quantum bits (qubits) that define a quantum computer enable it to perform calculations at speeds that are inconceivable on a classical computer⁴⁵. In terms of intrusion detection it means that whatever threats the network poses can be assessed and addressed on the spot. Another promising area where quantum computing can be applied to detect intrusions is by using methods of quantum encryption. These methods are prepared to change the experience of data encryption through concept of quantum mechanics. This means that no hacker no matter the sophistication level in hacking will be able to intercept or decrypt your data. In the context of network security, quantum encryption may lead to new level of reliability and safety of the sensitive data, that was previously thought, to be unattainable.

The dependency of nodes and events in today’s networks is broader and is developing in terms of space and time. In such complex network configurations, the application of a contemporary technique for intrusion detection is offered by the ST-GCN or Spatial-Temporal Graph Convolutional Networks. Due to the fact that these networks depict such complex relationships in a very accurate manner, these kinds of networks excel in architectures where aspects such as layout and time play a role in the traffic of the network. ST-GCNs are constructed on top of graph convolutional networks, making them capable of handling the complexity of actual networks. Accurate intrusion detection in highly dynamic situations is made possible by modeling the spatial and temporal elements of network data, which allows for the identification of anomalies that evolve over time. Where spatial-temporal dynamics play a crucial role in guaranteeing network security, such as in smart cities, industrial control systems, or cloud settings, traffic patterns can be analyzed. ST-GCNs are distinguished by their flexibility⁴⁶. In this way, they can be adapted to the distinctive spatial-temporal properties of different network environments. This adaptability makes them a useful resource for IT departments and other businesses concerned with network security, especially in complex and ever-changing environments.

Since VAEs are adept at both anomaly detection and data creation, they provide a holistic approach to intrusion detection. Generative models, of which VAEs are a subset, are used to discover the structure of raw data. When used for intrusion detection, they serve a dual purpose: identifying out-of-the-ordinary activity providing artificial data for use in training models. In the context of anomaly detection, VAEs are particularly effective at identifying outliers relative to the learned data distribution. They can detect abnormalities in real time by creating a model of typical network activity⁴⁷. This is especially helpful in cases where new threats don’t yet have well-defined signatures but can be recognized due to their departure from the usual. When businesses need synthetic data supplement their intrusion detection models, VAEs’ generative features come into play. VAEs help businesses strengthen their intrusion detection systems by creating a wide variety of datasets that simulate both benign malicious network activity.

In the face of advanced persistent threats (APTs) and other complicated, multi-stage attacks, application of game theory is important. Because enemies in these situations are constantly changing their approach, it is crucial be able analyze and anticipate their actions. Organizations can model and simulate these interactions with the use of game theory, which sheds light on potential attack routes enables proactive protection. Organizations improve their intrusion detection preventative measures by using a game-theoretic approach¹⁰. This long-term outlook guarantees that network defenders do more than simply respond to threats; they also anticipate and neutralize them. The incorporation of game theory into intrusion detection provides a preventative technique of securing networks from increasingly sophisticated adversaries, which is especially important as the threat landscape continues to grow.

Although hyperparameter tuning isn’t technically a machine learning method, it is crucial to the success of intrusion detection models. The hyperparameters of a machine learning model are the variables and configurations that determine its behavior. The effectiveness of an intrusion detection system relies heavily on the careful selection of models and tweaking of hyperparameters. Optimizing a model’s hyperparameters involves a methodical search for the optimal values for those variables⁴⁸. Many intrusion detection models include a number of settings and characteristics that must be calibrated before they can function at peak efficiency. By following these steps, you can rest assured that your intrusion detection system is functioning at peak efficiency, with the optimal configuration for reliable threat detection. Some of the hyperparameter optimization methods include Grid Search, Random Search and Bayesian optimization among others.

Recent advances in Explainable Artificial Intelligence or XAI are aimed at making Intrusion Detection Systems more transparent and interpretable. The sub-discipline of machine learning and deep learning produces models, which delivers high accuracy but it is challenging to comprehend and trust the output due to the black-box nature of these models¹⁰. Interpretations of the model’s predictions can then be made using XAI methods such as LIME or SHAP, and therein explaining the thought-process. In this way, due to the ability of observing the intrusion warning thoughts, security analysts are able to consider occurrences and set various security plans. Besides, it supports the concern for responsible and ethical use of AI in security, without which people cannot be assured of trusting their security and wellbeing in the hands of the subsequently automated security systems.

The encryption methods utilized by intrusion detection systems stand to benefit greatly from the implementation of Quantum Key Distribution (QKD). The mathematical algorithms used in conventional encryption systems are theoretically vulnerable to powerful computers. In contrast, QKD employs quantum mechanical principles to ensure the absolute safety of data transmission⁴⁸. It allows two people generate a secret key that can’t be hacked or intercepted by anyone, regardless of how powerful their computers are. In the context of intrusion detection, where protecting sensitive information from malevolent actors is of fundamental importance, this level of protection is essential.

It is a common problem in the field of intrusion detection to use models trained on data from one domain detect intrusions in another domain. Intrusion detection models can benefit from domain adaptation strategies like adversarial domain adaptation and transfer learning when being deployed in novel, previously unexplored network settings. These methods guarantee the accuracy of models in new settings by bringing the source and target domains into alignment⁴⁹. Because of this, intrusion detection systems may be easily adapted to the changing network landscape without sacrificing effectiveness, which is especially helpful when companies increase their network infrastructure or acquire new subsidiaries.

An efficient method for anomaly detection in IDSs is semi-supervised learning, especially when combined with One-Class Support Vector Machines (SVM). One-Class SVMs are able to establish a border that captures normal network behavior since they are trained on a labeled dataset consisting of only normal data. When presented with fresh information, they are able to spot outliers that don’t fit inside the norm. Because it makes use of a small bit of labeled data in conjunction with a large number of unlabeled data, this method shines in situations when labeled incursion data is sparse⁵⁰. In this way, intrusion detection systems may keep their false positive rate low while yet successfully detecting new and developing threats.

Edge AI, or the execution of machine learning models locally on edge devices, is reshaping intrusion detection by allowing for the detection of threats in real time without the need for centralized servers. Edge AI is crucial for securing the network at the device level in the age of IoT (Internet of Things) and edge computing, when billions of devices are interconnected. Deployed intrusion detection models on edge devices may rapidly assess network traffic and device behavior, enabling swift action against security threats⁵¹. When low-latency intrusion detection is crucial and centralized systems are at risk of being attacked, this method becomes indispensable.

Homomorphic encryption is a game-changer for intrusion detection since it allows for secure data analysis without the risk of sensitive information disclosure. Data is often exposed to breaches during the decryption phase of traditional techniques to data analysis since the data must first be read in plaintext before it can be processed. When using homomorphic encryption, however, it is possible to perform computations on encrypted data without first decrypting it. This allows businesses to do intrusion detection on encrypted data while keeping the data as secure as possible on the network. There are two main advantages to using homomorphic encryption. First, it protects individuals’ identities and personal data. Data stored in a network can be of volatile nature and leakage of such information may result in undesirable impacts⁵². The risks of unauthorized data breaches or access are reduced by homomorphic encryption because the data remains encrypted the entire analytical process. Secondly, it maintains the procedures for intrusion detection safe and secure in order to prevent external threat from reaching them. To decipher the encrypted data and spot dangers, businesses might use machine learning and deep learning methods. The encrypted data serves as the basis for any detections, keeping the data secure.

In the case of intrusion detection, transfer learning is a more versatile technique that relies on deep learning models. It can take a lot of time and resources to train a deep learning model from scratch⁵³. However, most of the pretrained models have already been trained several times on large dataset and have become very much familiar with many types of patterns and characteristics. It may be utilized in improving more factors concerning the intrusion detection models such as accuracy of the detection and the reliability of the results. A convolutional neural network (CNN) that has been trained to recognize characteristics in images is an example of a pretrained model that can be used for transfer learning and thus network threat detection. To transform such a pretrained model to be more suitable for the current task, we retrain it using data from intrusion detection⁵⁴. AFine-tuning means that some features of the model have to be adjusted as to its architecture, weights that were used and the chosen hyperparameters should now match the specifics of the given network traffic data. The first advantage of transfer learning, therefore, is when developing models, the amount of time it takes. A fresh model can be created by firms with less difficulty and work due to the ability of building new models on the pretrain models.

This study⁵⁵ proposed HDLNIDS model based on deep learning model used local features and temporal features for an intrusion detection. The malicious threats are emerged and constantly evolving therefore we need advanced security system for it. Because of new forms of text-based threats, intrusion detection is experiencing a need for using Natural Language Processing (NLP) techniques. The textual components that may be identified by NLP correspond to a wide range of assaults involving the use of text, from the phishing of messages and network invasions that control textual interfaces. By applying NLP, the intrusion detection systems are capable of understanding, analyzing and comprehending the language of these texts to factors threats. Phishing emails, for instance, frequently use misleading wording or URLs natural language processing models can identify. linguistic patterns used by malicious code in network communication may also be identifiable by NLP algorithms.

Optimizing intrusion detection models with the help of Neural Architecture Search (NAS) is a novel approach. By automatically probing a wide range of design options and hyperparameters, it can quickly zero on the optimal neural network architecture. Organizations that want strengthen their security posture can benefit from NAS since it will let them spend less time manually designing effective intrusion detection models⁵⁶. Different neural network designs, layer combinations, activation functions, and optimization methods are all part of the wide design space that must be explored during the NAS process. During this investigation, NAS compares the efficacy of several architectural frameworks using information from intrusion detection systems. More precise and productive models are given greater weight. Among the many advantages of NAS is the ease with which unique intrusion detection models may be developed. The search process considers the specific characteristics and requirements of the network data, resulting in models that are finely tuned to the organization’s needs.

Intrusion detection often grapples with uncertain and imprecise data, which can be challenging to classify as purely normal or malicious. Fuzzy logic systems offer a robust solution for handling uncertainty and providing nuanced decision-making in intrusion detection⁵⁷. Fuzzy logic is based on the concept of “fuzzy sets,” which allow gradual transitions between different membership grades. Unlike traditional binary classifications, where data is either entirely in one category or another, fuzzy logic accommodates the idea data can belong to multiple categories simultaneously. This is particularly advantageous in intrusion detection, where network behavior may exhibit subtle deviations or uncertainty. Fuzzy logic systems use linguistic variables rules and make decision making in way that is quite easier to understand by human beings⁵⁸. This is most helpful where there is a risk of getting high false positives in a simple and cleaner binary classification.

One of a major concern is the relative absence of the single, thorough and adaptive approach in dealing with the constant evolution of the threats. At first, new and innovative approaches to attack paths are discovered at once, and in such cases old intrusion detection systems may often fail. Many of these systems utilize labeled data and this can be limiting since it means a certain level of know-how of typical attacks. Reliance on them may mean that one misses other attacks such as the zero-day or those that are completely unknown. Still, flexibility and scalability are the matters that should be paid attention to. It is concerning therefore that while there have been high achievements in the achievement of machine learning as well as deep learning there is the need to come up with models as well as algorithms that are capable of being scaled to support numerous geometries. This is especially important in the context of the IoT and industrial networks, where limited resources are a typical occurrence. To guarantee the safety of these increasingly networked systems, intrusion detection technologies that function well in such situations are essential. Furthermore, there is a need to bridge the gap between theoretical improvements in intrusion detection systems and their implementation in real-world network security.