Intrusion Detection System System Research Articles

Survey on Federated Learning for Intrusion Detection System: Concept, Architectures, Aggregation Strategies, Challenges, and Future Directions

Intrusion Detection Systems (IDS) are essential for securing computer networks by identifying and mitigating potential threats. However, traditional IDS face challenges related to scalability, privacy, and computational demands as network data complexity increases. Federated Learning (FL) has emerged as a promising solution, enabling collaborative model training on decentralized data sources while preserving data privacy. Each participant retains local data repositories, ensuring data sovereignty and precluding data sharing. Leveraging the FL framework, participants locally train machine learning models on their respective datasets, subsequently transmitting model updates to a central server for aggregation. The central server then disseminates the aggregated model updates to individual participants, collectively striving to bolster intrusion detection capabilities. This article presents a comprehensive survey of FL applications in IDS, covering core concepts, architectural approaches, and aggregation strategies. We evaluate the strengths and limitations of various FL methodologies for IDS, addressing privacy and security concerns and exploring privacy-preserving techniques and security protocols. Our examination of aggregation strategies within the FL framework for IDS aims to highlight their effectiveness, limitations, and potential enhancements.

Secure Cooperative Routing in Wireless Sensor Networks

In wireless sensor networks (WSNs), sensor nodes are randomly distributed to transmit sensed data packets to the base station periodically. These sensor nodes, because of constrained battery power and storage space, cannot utilize conventional security measures. The widely held challenging issues for the network layer of WSNs are the packet-dropping attacks, mainly sinkhole and wormhole attacks, which focus on the routing pattern of the protocol. This thesis presents an improved version of the second level of the guard to the system, intrusion detection systems (IDSs), to limit the hostile impact of these attacks in a Low Energy Adaptive Clustering Hierarchy (LEACH) environment. The proposed system named multipath intrusion detection system (MIDS) integrates an IDs with ad hoc on-demand Multipath Distance Vector (AOMDV) protocol. The IDS agent uses the number of packets transmitted and received to calculate intrusion ratio (IR), which helps to mitigate sinkhole attacks and from AOMDV protocol round trip time (RTT) is computed by taking the difference between route request and route reply time to mitigate wormhole attack. MATLAB simulation results show that this cooperative model is an effective technique due to the higher packet delivery ratio (PDR), throughput, and detection accuracy. The proposed MIDS algorithm is proven to be more efficient when compared with an existing LEACH-based IDS system and MS-LEACH in terms of overall energy consumption, lifetime, and throughput of the network.

Network intrusion detection system for IoT security using machine learning and statistical based hybrid feature selection

AbstractThe widespread adoption of Internet of Things (IoT) devices has revolutionized daily life, offering convenience and efficiency. However, this growth has also brought new security challenges. With the escalating use of Internet and network technology, the number of cyber‐attacks has increased, intensifying the focus on Intrusion Detection Systems (IDS) among researchers. Network intrusion detection (NID) plays a crucial role in securing IoT networks, becoming essential for modern security infrastructure. Recently, machine learning algorithms have shown promise in providing IDS solutions. Yet, IoT IDS systems face challenges due to their functional and physical diversity, making comprehensive feature utilization impractical. Therefore, effective feature selection becomes imperative. In this research, a novel feature selection methodology for anomaly‐based NIDS is proposed. The methodology commences by employing two filter‐based techniques, namely 1‐way ANOVA and the Pearson correlation coefficient, to meticulously identify and extract pertinent features from the dataset. These methods serve as initial filters to discern the most relevant attributes, ensuring that only the most informative features are retained for subsequent analysis. Subsequently, the most optimal features identified by both methodologies are extracted utilizing the principles of union and intersection in mathematical set theory. Using the NSL‐KDD and UNSW‐NB15 datasets, we exemplify how our model can outperform conventional ML classifiers in terms of detection rate, precision, recall. In our study, intrusion detection is carried out by SVM, kNN, Decision Tree, Logistic Regression and Random Forest using trained attack patterns. The demonstrated results highlight the exceptional performance of the proposed system, with an impressive accuracy rate of 99.6% on the NSL‐KDD dataset and a substantial 97.7% on the UNSW‐NB15 dataset, clearly surpassing the performance of contemporary methods.

Analysis of Machine Learning Methods for Intrusion Detection Systems in Wireless Networks

Wireless networks have become integral to modern communication systems, making them vulnerable to various security threats. Intrusion detection systems (IDSs) are essential for detecting and mitigating these threats and can also powerfully screen network traffic for pernicious activities that are planned to abuse the classification, honesty, realness, and accessibility of the network. Machine learning (ML) and Deep learning (DL) techniques are effective in identifying and classifying network attacks. This study proposes a novel intrusion detection system that employs ML and DL models to classify and distinguish network attacks in wireless networks. The proposed system enhanced detection accuracy and efficiency in IDS, the scalability of IDS systems, and estimated the performance of IDS in wireless networks. It also investigates IDS techniques using machine learning, designs and implements IDS in wireless networks using machine learning, and trains several IDS models regarding wireless networks that are fitted. It contrasts the exhibition of proposed models and existing procedures. The suggested system can therefore be utilized as an effective IDS for wireless networks, providing real-time detection and classification of network attacks.

Empirical Enhancement of Intrusion Detection Systems: A Comprehensive Approach with Genetic Algorithm-based Hyperparameter Tuning and Hybrid Feature Selection

Machine learning-based IDSs have demonstrated promising outcomes in identifying and mitigating security threats within IoT networks. However, the efficacy of such systems is contingent on various hyperparameters, necessitating optimization to elevate their performance. This paper introduces a comprehensive empirical and quantitative exploration aimed at enhancing intrusion detection systems (IDSs). The study capitalizes on a genetic algorithm-based hyperparameter tuning mechanism and a pioneering hybrid feature selection approach to systematically investigate incremental performance improvements in IDS. Specifically, our work proposes a machine learning-based IDS approach tailored for detecting attacks in IoT environments. To achieve this, we introduce a hybrid feature selection method designed to identify the most salient features for the task. Additionally, we employed the genetic algorithm (GA) to fine-tune hyperparameters of multiple machine learning models, ensuring their accuracy in detecting attacks. We commence by evaluating the default hyperparameters of these models on the CICIDS2017 dataset, followed by rigorous testing of the same algorithms post-optimization through GA. Through a series of experiments, we scrutinize the impact of combining feature selection methods with hyperparameter tuning approaches. The outcomes unequivocally demonstrate the potential of hyperparameter optimization in enhancing the accuracy and efficiency of machine learning-based IDS systems for IoT networks. The empirical nature of our research method provides a meticulous analysis of the efficacy of the proposed techniques through systematic experimentation and quantitative evaluation. Consolidated in a unified manner, the results underscore the step-by-step enhancement of IDS performance, especially in terms of detection time, substantiating the efficacy of our approach in real-world scenarios.

CFS-AE: Correlation-based Feature Selection and Autoencoder for Improved Intrusion Detection System Performance

The major problem computer network users face concerning data – whether in storage, in transit, or being processed - is unauthorized access. This unauthorized access typically leads to the loss of confidentiality, integrity, and availability of data. Consequently, it is essential to implement an accurate Intrusion Detection System (IDS) for every information system. Many researchers have proposed machine learning and deep learning models, such as autoencoders, to enhance existing IDS. However, the accuracy of these models remains a significant research challenge. This paper proposes a Correlation-Based Feature Selection and Autoencoder (CFS-AE) to enhance detection accuracy and reduce the false alarms associated with the current anomaly-based IDS. The first step involves feature selection for the NSL-KDD and CIC-IDS2017 datasets which are used to train and test our model. Subsequently, an autoencoder is employed as a classifier to categorize data traffic into attack and normal categories. The results from our experimental study revealed an accuracy of 94.32% and 97.71% for the NSL-KDD and CIC-IDS2017 datasets, respectively. These results demonstrate improved performance over existing IDS systems.

Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis

The rapid proliferation of new technologies such as Internet of Things (IoT), cloud computing, virtualization, and smart devices has led to a massive annual production of over 400 zettabytes of network traffic data. As a result, it is crucial for companies to implement robust cybersecurity measures to safeguard sensitive data from intrusion, which can lead to significant financial losses. Existing intrusion detection systems (IDS) require further enhancements to reduce false positives as well as enhance overall accuracy. To minimize security risks, data analytics and machine learning can be utilized to create data-driven recommendations and decisions based on the input data. This study focuses on developing machine learning models that can identify cyber-attacks and enhance IDS system performance. This paper employed logistic regression, support vector machine, decision tree, and random forest algorithms on the UNSW-NB15 network traffic dataset, utilizing in-depth exploratory data analysis, and feature selection using correlation analysis and random sampling to compare model accuracy and effectiveness. The performance and confusion matrix results indicate that the Random Forest model is the best option for identifying cyber-attacks, with a remarkable F1 score of 97.80%, accuracy of 98.63%, and low false alarm rate of 1.36%, and thus should be considered to improve IDS system security.

Федеративное обучение (FL) – Обзор

Explores the fundamental aspects of federated learning (FL) in the context of intrusion detection systems (IDS) within Internet of Things (IoT) networks. Federated learning presents an innovative approach to training machine learning models on distributed devices, thereby minimizing the need to transmit sensitive data to central servers. We classify FL into horizontal, vertical, and federated transfer learning and examine their application in IDS systems. Additionally, we analyze the network structure of FL, encompassing centralized and decentralized FL. Based on the conducted review, it can be concluded that FL holds promise for enhancing data privacy and anomaly detection efficiency in IoT networks.

A Blockchain-based Intrusion Detection/Prevention Systems in IoT Network: A Systematic Review

The Internet of Things (IoT) is an innovative technology used in manufacturing, environmental monitoring, home automation, human health applications, and other fields. IoT devices, however, are susceptible to various security risks. Cyber-attacks on IoT systems can halt the entire system, resulting in data loss. Intrusion Detection Systems (IDS) can quickly identify malicious activity that could have compromised the network's performance. Effective IDS and Intrusion Prevention System (IPS) are necessary to protect IoT systems. Blockchain technology presents another innovative method to strengthen security systems against the latest attacks. This paper focuses on conducting a Systematic Literature Review (SLR) on Blockchain-based Intrusion Detection/Prevention Systems in IoT Networks. We reviewed several relevant blockchain-based IDS and IPS proposed for IoT networks and their mechanisms. The most recent research articles, published between 2017 and 2022, were selected from several databases. Consequently, a comprehensive review of these selected papers sheds light on their techniques, advantages, and drawbacks.

ANALYZING THE CAN PROTOCOL: VULNERABILITIES, PROTECTIVE MEASURES AND IMPROVEMENTS

The Controller Area Network (CAN) bus, originally designed for internal communication among a limited number of Electronic Control Units (ECUs) within vehicles, faces significant security challenges due to the rapid expansion of ECUs in modern automobiles. This expansion necessitates accessibility for diagnostic purposes, yet the CAN protocol lacks fundamental security features such as encryption, authentication, and integrity checks, rendering it vulnerable to various attacks including message injection, Denial of Service (DoS), and masquerading ECUs. This paper surveys existing literature and investigates potential intrusions on the CAN bus, highlighting attacks ranging from GPS spoofing to remote sensor tampering. Various solutions are discussed, including network subdivision, encryption, authentication techniques, and intrusion detection systems (IDS). Recent research proposes IDS solutions utilizing machine learning algorithms such as Random Forest, Support Vector Machine, and Deep Neural Networks (DNN), demonstrating effectiveness in detecting known attacks. However, challenges persist in identifying unknown attacks and enhancing overall system performance. Innovative approaches, such as event-triggered detection and bloom filtering techniques, show promise in mitigating specific attack vectors but may introduce overhead or limitations in real-time response. Deep learning-based IDS systems exhibit high performance but struggle with the detection of novel attacks. Furthermore, while some solutions address specific vulnerabilities, others propose more comprehensive security measures, such as preventing remote manipulation of critical vehicle functions like steering and braking. Overall, the research emphasizes the critical need for robust security measures in automotive systems to safeguard against evolving threats to vehicle safety and integrity.

Implementation of IDS Using Snort with Barnyard2 Visualization for Network Monitoring in The Informatics Engineering Computer Lab at Muhammadiyah University Surakarta

The recent surge in cyberattacks should not be taken lightly, especially by large enterprises with sensitive data. Intrusion Detection Systems (IDS) are becoming a critical component for detecting network anomalies. One such network anomaly detection tool is SNORT, with a BASE (Basic Analysis and Security Engine) frontend for efficient data processing. Acting as a bridge between SNORT and BASE, the author uses barnyard2 as a backend to store logs obtained from SNORT into the database. The implementation methodology used in this research is an experimental approach, where the authors conduct experiments through trial and error to achieve the desired results. This IDS system was tested using two types of attacks, namely DDoS and SQL-Injection. The DDoS attack trial uses tools found in Kali Linux, namely Hping3 with 6 scenarios namely FIN, ACK, RST, UDP, SYN, and ICMP with the results detected in the snort database. SQL-Injection attack test using the DVWA vulnerable website with the result detected in the snort database when the attack is carried out. This proves that the accuracy level of the system reaches close to 100% with the rules given and the penetration testing given.

Two-Stage Intrusion Detection System in Intelligent Transportation Systems Using Rule Extraction Methods From Deep Neural Networks

In recent years, intrusion detection systems (IDSs) are offering effective solutions to protect various types of cyber-attacks in different networks such as Internet of Vehicles (IoVs) network in Intelligent Transportation Systems (ITS). Deep learning models have largely been leveraged by these intrusion detection systems to achieve better effectiveness results. However, deep learning models are black boxes, which limits their acceptability in decision systems. Also, they require powerful processing capabilities such as GPU, which limit their deployments in resource-constrained devices in IoV environment. To deal with these issues, we propose a two-stage IDS in ITS to discover suspicious network activity of In-Vehicles Networks (IVN) and vehicles to everything (V2X) networks. Our proposed IDS system uses rule extraction methods from deep learning models, i.e., deep neural networks in two stages. In the first stage, we analyze network traffic to distinguish between normal and attack traffic. If the traffic is found malicious, the second stage is invoked to identify the type of attack. To this end, we propose three variants of rule extraction. The first and the second variants are homogeneous, and they apply <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$DeepRed$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$HypInv$</tex-math> </inline-formula> rule extraction methods in both stages respectively. The third variant is heterogeneous, and it applies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$HypInv$</tex-math> </inline-formula> in the first stage to perform binary classification, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$DeepRed$</tex-math> </inline-formula> in the second stage to perform attack classification. The key idea is to combine the advantages of rule extraction technique and two-stage IDS architecture to resource consumption and improve classification accuracy. The proposed IDS model was tested using four benchmark datasets, i.e. ISCXIDS2012, CIC-IDS2017, and CSE-CIC-IDS2018 datasets are used for external network communications and the car hacking dataset are used for in-vehicle communications. The evaluation results show that the homogeneous <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$DeepRed$</tex-math> </inline-formula> is the optimal one in all cases of IDS system with an accuracy scores ranging between 92.43%-98.32% under CIC-IDS2017 dataset, between 91.32%-99.46% under CSE-CIC-IDS2018 dataset, and between 96.05%-99.21% under Car-hacking dataset.

Personalized federated learning-based intrusion detection system: Poisoning attack and defense

To deal with the increasing number of cyber-attacks, intrusion detection system (IDS) plays an important role in monitoring and ensuring the security of the computer network. With the power of machine learning and deep learning, intelligent IDS systems have gained increasing attention due to their efficiency and high classification accuracy. However, the premise of machine learning/deep learning is that the data must be in one central entity (e.g., server) to train the model. This causes additional concerns, such as data transmission costs and privacy leakage. Federated learning complements this shortcoming with a privacy-preserving decentralized learning technique. In federated learning, the data are not shared with the server, local model training is performed where the data reside and only the model parameters are exchanged with the server. This work investigates the federated learning-based IDS approach in the context of IoT data to study the main challenges imposed by federated learning. Two main issues, such as data heterogeneity and poisoning attacks launched by malicious clients, are the main focus of this study. As real-world IoT datasets are heterogeneous, we propose a personalized federated learning-based IDS approach to handle imbalanced data distributions. Moreover, a curious yet malicious client can poison the local data or model to corrupt the global intrusion detection model due to the distributed nature of federated learning, where the central server has no control over the client’s local training process. This study demonstrates that the existence of a malicious client can degrade the performance of the federated learning-based IDS model. Accordingly, we propose a robust approach called pFL-IDS to combat poisoning attacks against the federated learning-enabled IDS on heterogeneous IoT data. Our approach introduces mini-batch logit adjustment loss to local model training to obtain a personalized model tailored to each local data distribution. Moreover, we design a detection mechanism at the server to identify malicious agents by considering the cosine similarity of local models from the non-poisoned client’s centroid. The non-poisoned centroid is determined from the similarity between the pre-computed global model and the local models. If the poisoning attack is successful, poisoned clients will be closer to the pre-computed global model; any models further from the pre-computed model are taken as the non-poisoned clients. With this two-phase client similarity alignment, we identify poisoned clients and restrict their aggregation on the global intrusion detection model. In comparison with the baseline methods, we demonstrate that our pFL-IDS can detect poisoning attacks without compromising performance.

Robust genetic machine learning ensemble model for intrusion detection in network traffic

Network security has developed as a critical research subject as a result of the Rapid advancements in the development of Internet and communication technologies over the previous decades. The expansion of networks and data has caused cyber-attacks on the systems, making it difficult for network security to detect breaches effectively. Current Intrusion Detection Systems (IDS) have several flaws, including their inability to prevent attacks on their own, the requirement for a professional engineer to administer them, and the occurrence of false alerts. As a result, a plethora of new attacks are being created, making it harder for network security to properly detect breaches. Despite the best efforts, IDS continues to struggle with increasing detection accuracy while lowering false alarm rates and detecting new intrusions. Therefore, network intrusion detection enhancement by preprocessing and generation of highly reliable algorithms is the main focus nowadays. Machine learning (ML) based IDS systems have recently been implemented as viable solutions for quickly detecting intrusions across the network. In this study, we use a combined data analysis technique with four Robust Machine learning ensemble algorithms, including the Voting Classifier, Bagging Classifier, Gradient Boosting Classifier, and Random Forest-based Bagging algorithm along with the proposed Robust genetic ensemble classifier. For each algorithm, a model is created and tested using a Network Dataset. To assess the performance of both algorithms in terms of their ability to anticipate the anomaly occurrence, graphs of performance rates have been evaluated. The suggested algorithm outperformed other methods as it shows the lowest values of mean square error (MSE) and mean absolute error (MAE). The experiments were conducted on the Network traffic dataset available on Kaggle, on the Python platform, which has limited samples. The proposed method can be applied in the future with more machine learning ensemble classifiers and deep learning techniques.

Moth–Flame Optimization based ensemble classification for intrusion detection in intelligent transport system for smart cities

The number of vehicles travelling on the road has increased tremendously over the past few decades throughout the globe. The advent of Intelligent Transport System (ITS) enabled the vehicles to be managed intelligently. However, the ITS network can be intruded by malicious users to access the sensitive information. So a strong Intrusion Detection System (IDS) is required to safeguard the ITS network. Due to the huge volume of data generated by the sensors in the vehicles in ITS, there is a dire need to extract the most important features from the ITS data for classification phase in IDS. Even though, many researchers have worked on building an IDS system in ITS, not much work is done on addressing the aforementioned issue. To address this problem, we have utilized the Moth–Flame Optimization (MFO) in this work. In the proposed work, a MFO based ensemble machine learning model is proposed to classify the IDS dataset in ITS. Firstly the IDS dataset is normalized using the standard-scaler method. Then optimal features from the IDS dataset are chosen by MFO. These optimal features are trained by an ensemble classifier comprising of linear regression, random forest and XGBoost classification algorithms for intelligent decision making. The performance of the proposed model is compared against several state of the art meta-heuristic algorithms based on ensemble models which achieved a superior accuracy and recall of 100%, and precision of 99.5% on shellcode attacks.

A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection.

The Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system's overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.

Optimization of Deep Generative Intrusion Detection System for Cloud Computing: Challenges and Scope for Improvements

The large amount of data and its exponential increase result in security problems which subsequently cause damage to cloud computing and its environments. The Intrusion detection system (IDS) is among the systems that monitor and analyse data for malicious attacks in the cloud environment. High volume, high redundancy, and high dimensionality of network traffic in cloud computing make it difficult to detect attacks by contemporary techniques. To improve the performance of IDS features selection and data imbalance issues need to be resolved. This paper includes techniques and surveys of cloud-based IDS with ML techniques and IDS performance on the different types of cloud-based datasets. It also analyses the gaps and scope for enhancement of evaluation parameters of IDS. It provides a cloud-based IDS system which will produce a good performance result as compared to the other contemporary system. Moreover, this paper offers a current overview of cloud-based IDS, Data imbalance technique, Dataset and proposed cloud IDS system architecture.

Hybrid extreme learning machine-based approach for IDS in smart Ad Hoc networks

In recent years, intrusion detection systems (IDSs) have increasingly come to be regarded as a significant method due to their potential to develop into a key component that is necessary for the safety of computer networks. This work focuses on the usage of extreme learning machines, which are also known as ELMs, with the purpose of spotting prospective intrusions and assaults. The proposed method combines the self-adaptive differential evolution method for optimising network input weights and hidden node biases and multi-node probabilistic approach with the extreme learning machine for deriving network output weights. This body of work presents an innovative method of learning that can be put into practice in order to determine whether or not an incursion has taken place in the system that is the focus of the investigation that is being carried out by this body of work. A hybrid extreme learning machine is used in the execution of this strategy. When there is one thousand times more traffic on a network, the ability of regular IDS systems to detect malicious network intrusions is lowered by a factor of one hundred. This is because there are less opportunities to detect the intrusions. This is due to the fact that there are less probabilities to identify potential dangers. This paper lays the groundwork for a novel methodology for identifying malicious network breaches. The findings of the simulation demonstrated that putting into practice the approach that was proposed resulted in an improvement in the accuracy of the scenario's classification while it was being investigated. The implementation of the method seems to have produced the desired results.

English

The latest wireless sensor network (WSN) developments in critical applications have introduced security risks, like jamming. Intrusion Detection System (IDS) in WSN is the method of recognizing malevolent or unauthorized activities in the network. The intruder's presence to launch different attacks within the network cannot be disregarded. Despite a great deal of effort by the researcher workers, IDS still experienced difficulties enhancing recognition performance while minimizing the false alarm rate and identifying novel intrusions. Recently, Deep Learning (DL) and Machine Learning (ML) based IDS system has been deployed as promising solution to effectively identify intrusion across the network. Therefore, the study presents a Moth Search Optimization with DL-based Intrusion Detection (MSODL-ID) method in the WSN. The MSODL-ID technique aims to effectually identify the occurrence of malicious activities or intrusions in the network. To accomplish this, the MSODL-ID technique undergoes two stages of preprocessing: data conversion and data scaling. In addition, the MSODL-ID technique employs Convolutional Recurrent Neural Network (CRNN) model with a Hopfield layer for intrusion detection purposes. For optimal hyperparameter selection of the CRNN model, the MSO algorithm is used and thereby enhances the classification performance of the CRNN model. The stimulation analysis of the MSODL-ID system is tested by means of Kaggle datasets, and the outcomes exhibit the promising performance of the MSODL-ID system over other current DL approaches.

LM-GA: A Novel IDS with AES and Machine Learning Architecture for Enhanced Cloud Storage Security

Cloud Computing (CC) is a relatively new technology that allows for widespread access and storage on the internet. Despite its low cost and numerous benefits, cloud technology still confronts several obstacles, including data loss, quality concerns, and data security like recurring hacking. The security of data stored in the cloud has become a major worry for both Cloud Service Providers (CSPs) and users. As a result, a powerful Intrusion Detection System (IDS) must be set up to detect and prevent possible cloud threats at an early stage. Intending to develop a novel IDS system, this paper introduces a new optimization concept named Lion Mutated-Genetic Algorithm (LM-GA) with the hybridization of Machine Learning (ML) algorithms such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Initially, the input text data is preprocessed and balanced to avoid redundancy and vague data. The preprocessed data is then subjected to the hybrid Deep Learning (DL) models namely the CNN-LSTM model to get the IDS output. Now, the intruded are discarded and non-intruded data are secured using Advanced Encryption Standard (AES) encryption model. Besides, the optimal key selection is done by the proposed LM-GA model and the cipher text is further secured via the steganography approach. NSL-KDD and UNSW-NB15 are the datasets used to verify the performance of the proposed LM-GA-based IDS in terms of average intrusion detection rate, accuracy, precision, recall, and F-Score.