Improved Intrusion Detection System Research Articles

Improving intrusion detection system accuracy using deep neural network

Improving intrusion detection system accuracy using deep neural network

Optimizing a Feature Selection Intrusion Detection Algorithm with Data Mining

Intrusion detection safeguards computer systems against unauthorized access and malicious activities. Feature selection plays a pivotal role in enhancing the efficiency and effectiveness of intrusion detection algorithms by identifying the most relevant features from vast datasets. In this study, we propose a novel approach to optimize feature selection in intrusion detection algorithms using data mining techniques. We explore various data mining algorithms, including decision trees, genetic algorithms, and particle swarm optimization, to identify the optimal feature subset that maximizes detection accuracy while minimizing computational overhead. Experimental results demonstrate our approach’s efficacy in improving intrusion detection systems’ performance across different datasets, achieving higher detection rates with reduced computational complexity. Our work advances state-of-the-art intrusion detection by leveraging data mining for efficient feature selection.

Three layer hybrid learning to improve intrusion detection system performance

In imbalanced network traffic, malicious cyberattacks can be hidden in a large amount of normal traffic, making it difficult for intrusion detection systems (IDS) to detect them. Therefore, anomaly-based IDS with machine learning is the solution. However, a single machine learning cannot accurately detect all types of attacks. Therefore, a hybrid model that combines long short-term memory (LSTM) and random forest (RF) in three layers is proposed. Building the hybrid model starts with Nearmiss-2 class balancing, which reduces normal samples without increasing minority samples. Then, feature selection is performed using chi-square and RF. Next, hyperparameter tuning is performed to obtain the optimal model. In the first and second layers, LSTM and RF are used for binary classification to detect normal data and attack data. While the third layer model uses RF for multiclass classification. The hybrid model verified using the CSE-CIC-IDS2018 dataset, showed better performance compared to the single algorithm. For multiclass classification, the hybrid model achieved 99.76% accuracy, 99.76% precision, 99.76% recall, and 99.75% F1-score.

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.

Improved Intrusion Detection System to Alleviate Attacks on DNS Service

Improved Intrusion Detection System to Alleviate Attacks on DNS Service

A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques

The study in this paper characterizes lightweight IoT networks as being established by devices with few computer resources, such as reduced battery life, processing power, memory, and, more critically, minimal security and protection, which are easily vulnerable to DDoS attacks and propagating malware. A DDoS attack detection model is crucial for attacks in various industries, ensuring the availability and reliability of their networks and systems. The model distinguishes between legitimate and malicious traffic by analyzing network traffic patterns and identifying anomalies. This safeguards critical infrastructure, preserves business continuity, and protects the user experience, minimizing the impact of DDoS attacks. Numerous scholars have studied the notion that protecting lightweight IoT networks essentially requires improving intrusion detection systems. This research is valuable, as it follows a tailored pre-processing methodology specific to IoT network challenges, addressing a pressing need in cybersecurity by focusing on a growing concern related to IoT devices and DDoS attacks, enhancing the security of essential network systems in various industries by effectively detecting DDoS attacks, and developing a lightweight intrusion detection system that aligns with the limited resources of IoT devices. This manuscript proposes a compact and lightweight intrusion detection system that blends machine learning classifiers with a fresh approach to data pre-processing. The handling of missing values, data standardization using Standard Scalar, feature selection using ExtraTreeClassifier wherein only the 15 best features are extracted, and anomaly detection using a classifier are performed. The network dataset of TON-IOT and BOT-IOT datasets is used for experiments, specifically binary classifications and multiple-class classification for the experiment with DDoS and all attacks, respectively. There is an imbalance between the TON-IOT and BOT-IOT attack classes. In trials using the TON-IOT and BOT-IOT datasets, the classes were balanced using several iterations of the SMOTE approach. This research provides a number of classifier types, namely logistic regression, random forest, naïve bayes, artificial neural network, and k nearest neighbor algorithms, which are used to build a lightweight intrusion detection system that is ideally suited for protecting against DDoS attacks in IoT networks. The time taken to train and predict the DDoS attacks is also implemented. Random forest performed well under TON-IOT and naïve bayes performed well under BOT-IOT under binary and multiple-class classification, achieving an accuracy of 100% with less training and prediction time.

An Improved Intrusion Detection System Using Machine Learning with Singular Value Decomposition and Principal Component Analysis

An Improved Intrusion Detection System Using Machine Learning with Singular Value Decomposition and Principal Component Analysis

Developing an Integrated Framework for Securing Internet of Things Traffic in Smart Cities Using Machine Learning Techniques

Internet of Things technology opens the horizon to a broader scope of intelligent applications in smart cities. However, the massive amount of traffic exchanged among devices may cause security risks, especially when devices are compromised or vulnerable to cyberattack. An intrusion detection system is the most powerful tool to detect unauthorized attempts to access smart systems. It identifies malicious and benign traffic by analyzing network traffic. In most cases, only a fraction of network traffic can be considered malicious. As a result, it is difficult for an intrusion detection system to detect attacks at high detection rates while maintaining a low false alarm rate. This work proposes an integrated framework to detect suspicious traffic to address secure data communication in smart cities. This paper presents an approach to developing an intrusion detection system to detect various attack types. It can be carried out by implementing a Principal Component Analysis method that eliminates redundancy and reduces system dimensionality. Furthermore, the proposed model shows how to improve intrusion detection system performance by implementing an ensemble model.

A balanced communication-avoiding support vector machine decision tree method for smart intrusion detection systems

The Internet of Things field has created many challenges for network architectures. Ensuring cyberspace security is the primary goal of intrusion detection systems (IDSs). Due to the increases in the number and types of attacks, researchers have sought to improve intrusion detection systems by efficiently protecting the data and devices connected in cyberspace. IDS performance is essentially tied to the amount of data, data dimensionality, and security features. This paper proposes a novel IDS model to improve computational complexity by providing accurate detection in less processing time than other related works. The Gini index method is used to compute the impurity of the security features and refine the selection process. A balanced communication-avoiding support vector machine decision tree method is performed to enhance intrusion detection accuracy. The evaluation is conducted using the UNSW-NB 15 dataset, which is a real dataset and is available publicly. The proposed model achieves high attack detection performance, with an accuracy of approximately 98.5%.

Optimized Deep Ensemble Technique for Malicious Behavior Classification in Cloud

We must find novel approaches to improve intrusion detection systems in cloud computing as network traffic increases and attacks become more pervasive and complex. This study offers Ensemble methods for Cloud-based Network Intrusion Detection and Classification. The main means through which Ensemble Learning enhances each Machine Learning result Using algorithms and a strong classifier. In starting, the redundant data is removed from the collected raw data and the data cleaning is performed as the pre-processing phase. From these pre-processed data, modified holoentropy, correntropy, information gain, and statistical features are extracted. An ensemble technique that uses models like the neural network (NN), support vector machine (SVM), random forest (RF), and optimized deep belief network (DBN) performs the final detection of the presence or absence of attacks. The process of fine-tuning is supervised, and in order to increase the training’s accuracy, the weights received from the RBM pre-training are modified. A new hybrid model called Moth Logarithmic Assisted Deer Hunting Optimization performs this fine-tuning (MLADHO). The conceptual fusion of the traditional Moth Flame Optimization (MFO) and the Deer Hunting Optimization Algorithm (DHO) yields the suggested algorithm. Finally, both the algorithmic and classifier evaluation is performed by comparing the suggested over conventional methods.

Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm.

This study proposes a novel framework to improve intrusion detection system (IDS) performance based on the data collected from the Internet of things (IoT) environments. The developed framework relies on deep learning and metaheuristic (MH) optimization algorithms to perform feature extraction and selection. A simple yet effective convolutional neural network (CNN) is implemented as the core feature extractor of the framework to learn better and more relevant representations of the input data in a lower-dimensional space. A new feature selection mechanism is proposed based on a recently developed MH method, called Reptile Search Algorithm (RSA), which is inspired by the hunting behaviors of the crocodiles. The RSA boosts the IDS system performance by selecting only the most important features (an optimal subset of features) from the extracted features using the CNN model. Several datasets, including KDDCup-99, NSL-KDD, CICIDS-2017, and BoT-IoT, were used to assess the IDS system performance. The proposed framework achieved competitive performance in classification metrics compared to other well-known optimization methods applied for feature selection problems.

Improved Intrusion Detection System using Quantal Response Equilibrium-based Game Model and Rule-based Classification

Wireless sensor network has large number of low-cost tiny nodes with sensing capability. These provide low cost solutions to many real world problems such as such as defence, Internet of things, healthcare, environment monitoring and so on. The sensor nodes of these networks are placed in vulnerable environment. Hence, the security of these networks is very important. Intrusion Detection System (IDS) plays an important role in providing a security to such type of networks. The sensor nodes of the network have limited power and, traditional security mechanisms such as key-management, encryption decryption and authentication techniques cannot be installed on the nodes. Hence, there is a need of special security mechanism to handle the intrusions. In this paper, intrusion detection system is designed and implemented using game theory and machine learning to identify multiple attacks. Game theory is designed and used to apply the IDS optimally in WSN. The game model is designed by defining the players and the corresponding strategies. Quantal Response Equilibrium (QRE) concept of game theory is used to select the strategies in optimal way for the intrusion’s detection. Further, these intrusions are classified as denial of service attack, rank attack or selective forwarding attacks using supervised machine learning technique based on different parameters and rules. Results show that all the attacks are detected with good detection rate and the proposed approach provides optimal usage of IDS.

A comparative study of Machine learning Algorithms on the UNSW-NB 15 Dataset

The world has experienced a radical change due to the internet. Internet became a crucial element in our daily life, therefore, the security of our DATA could be threatened at any time. This safety is handled using systems to detect network intrusion called Intrusion Detection Systems (IDS). Machine learning techniques are being implemented to improve these systems. In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a good one for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. Nevertheless, most researchers have focused on the confusion matrix as measurements of classification performance. Therefore, many papers use this matrix to present a detailed comparison with the dataset, data preprocessing, feature selection technique, algorithms classification and performance evaluation. The goal of this paper is to present a comparison of application of different Machine Learning algorithms used to build and improve intrusion detection systems in terms of confusion matrix, accuracy, recall, precision, FAR, specificity and sensitivity using the UNSW-NB15 Dataset. Furthermore, we introduce some lesson learnt to shoot more researchers in their future works.

Comparison of Artificial Intelligence Algorithms for IoT Botnet Detection on Apache Spark Platform

The amount of network traffic generated from the Internet of Things (IoT) devices is massive which leads to Big data. In present scenario, IoT devices has taken a leap ahead in terms of growing technology and insecure network traffic flow. The sensitivity of the data in the IoT network is becoming very high, which concern the security in legal and privacy issues. Traditional Intrusion Detection System (IDS) is used as a primary line security to differentiate between the attack and benign network traffic flow. As the size of the network traffic capture increases, it becomes more important to handle such big data. Similarly, when the number of classes and instances increases, the complexity of the data also increases. In this paper, we propose a big data platform based intrusion detection system which can differentiate between the types of network traffic flow generated from the IoT devices. We compared the performance of deep learning algorithms with machine learning algorithms on Apache Spark platform and found that machine learning algorithms outperform deep learning algorithms with greater accuracy and less training time for the model. We evaluated our research based on real world network traffic dataset BoT-IoT [1] for an improved intrusion detection system in IoT network traffic.

Application of cascading classification algorithms to improve in-trusion detection systems

Application of cascading classification algorithms to improve in-trusion detection systems

A New Ensemble-Based Intrusion Detection System for Internet of Things

The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.

Network Abnormal Data Detection Based on Deep Learning Model

To improve intrusion detection system performance,many algorithms are used to improve the performance of IDS systems, especially deep learning models. This paper presents an algorithm based on the model MLP, the training data set is the KDD99 data set, and the original data of the data set is vectorized by one-hot encoding, and the feature data is processed by Z-Score, and then the feature vector is encoded, and then the multi-layer perception is used The machine network performs feature learning, and finally trains the classifier model for detection. Traditional network anomaly detection algorithm models mainly use manual selection methods, and the accuracy and efficiency of classification problems are not high. This article first proposed the role of multilayer perceptron in Adam optimizer. The test of the KDD99 data set has been completed. The algorithm accuracy rate can reach 99%. For future network abnormal data detection work, an algorithm model that can realize real-time online detection is provided, which will have higher accuracy and better real-time performance.

Modified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection

The ability of wireless sensor networks (WSN) and their functions are degraded or eliminated by means of intrusion. To overcome this issue, this paper presents a combination of machine learning and modified grey wolf optimization (MLGWO) algorithm for developing an improved intrusion detection system (IDS). The best number of wolves are found by running tests with multiple wolves in the model. In the WSN environment, the false alarm rates are reduced along with the reduction in processing time while improving the rate of detection and the accuracy of intrusion detection with a decrease in the number of resultant features. In order to evaluate the performance of the proposed model and to compare it with the existing techniques, the NSL KDD’99 dataset is used. In terms of detection rate, false alarm rate, execution time, total features and accuracy the evaluation and comparison is performed. From the evaluation results, it is evident that higher the number of wolves, the performance of the MLGWO model is enhanced.

Improved Intrusion Detection System using Quantal Response Equilibrium-based Game Model and Rule-based Classification

Wireless sensor network has large number of low-cost tiny nodes with sensing capability. These provide low cost solutions to many real world problems such as such as defence, Internet of things, healthcare, environment monitoring and so on. The sensor nodes of these networks are placed in vulnerable environment. Hence, the security of these networks is very important. Intrusion Detection System (IDS) plays an important role in providing a security to such type of networks. The sensor nodes of the network have limited power and, traditional security mechanisms such as key-management, encryption decryption and authentication techniques cannot be installed on the nodes. Hence, there is a need of special security mechanism to handle the intrusions. In this paper, intrusion detection system is designed and implemented using game theory and machine learning to identify multiple attacks. Game theory is designed and used to apply the IDS optimally in WSN. The game model is designed by defining the players and the corresponding strategies. Quantal Response Equilibrium (QRE) concept of game theory is used to select the strategies in optimal way for the intrusion’s detection. Further, these intrusions are classified as denial of service attack, rank attack or selective forwarding attacks using supervised machine learning technique based on different parameters and rules. Results show that all the attacks are detected with good detection rate and the proposed approach provides optimal usage of IDS

An Improved Intrusion Detection System Based on KNN Hyperparameter Tuning and Cross-Validation

As security violations increase, cybersecurity is a critical issue for any area of cyberspace. A large number of zero-day attacks are taking place on a continuous basis due to the inclusion of several protocols. Most of these attacks are minor variations of cyberattacks that have been carried out previously. This shows that problems are faced by even the most sophisticated methods, such as conventional machine learning systems, when identifying these minor variations in attacks over time. By considering a few challenges in the existing intrusion detection methods, an effective semisupervised technique is presented in this paper to decrease the false alarm rate and enhance the detection rate for intrusion detection systems (IDSs). The proposed approach proposes an IDS utilizing k-nearest neighbor hyperparameter tuning with fivefold cross-validation on semisupervised learning. For each unlabeled data point, its k-nearest neighbors in the training set are first identified. After that, based on statistical information gained from hyperparameter tuning of these neighboring data, namely the number of neighboring data points belonging to each possible class, distance metric, and distance weight, the new data are classified as normal or attack class. A widely used dataset NSL-KDD is employed to determine the robustness of the model. In comparison with IDS-based KNN algorithms, the simulation findings demonstrate that the proposed approach performs better.