Wireless Intrusion Detection System Research Articles

Optimized Ensemble Learning Models Based on Clustering and Hybrid Deep Learning for Wireless Intrusion Detection

Machine learning (ML) and deep learning (DL) are used in numerous fields, particularly to develop effective intrusion detection systems (IDS). Existing wireless network IDS, which rely on a single ML algorithm and have limitations. These include a high rate of false positives, difficulties in recognizing distinct attack patterns, and a high acquisition cost for annotated training datasets. However, hostile threats are always evolving, networks need a smart security solution. In comparison to other ML approaches, DL algorithms are more successful in intrusion detection. This paper presents a DL based ensemble model that combines Multi-verse through Chaotic Atom Search Optimization (MCA) for preprocessing, which eliminates unsolicited/recurrent information in the dataset. The process of optimized feature selection uses Principal Component Analysis (PCA), Chaotic Manta-ray Foraging Optimizations (CMFO), and a grounded grouping method to partition the optimized feature dataset into k-diverse clusters. The recommended model then stacks Support Vector Machine (SVM) as the ensemble model’s meta-learner classifier, pre-training the hybrid DL prototypes using the optimized feature dataset cluster. The CNN-LSTM and CNN-GRU models, which integrate Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), are the hybrid DL prototype’s key components. The suggested model’s performance has been enhanced and compared to six ML techniques: NB, SVM, J48, RF, MLP, and kNN models, utilizing measures such as accuracy, precision, recall, and F-measure. The public can access the Aegean Wi-Fi Intrusion Dataset (AWID) which is used for evaluating the recommended model and is outperformed the contemporary models in the literature.

Fed-Deep CTRL (Light GBM)-Scalable, Interpretable, and Privacy-Preserving Federated Learning Framework for Wireless Intrusion Detection System

Fed-Deep CTRL (Light GBM)-Scalable, Interpretable, and Privacy-Preserving Federated Learning Framework for Wireless Intrusion Detection System

A Signature-Based Wireless Intrusion Detection System Framework for Multi-Channel Man-in-the-Middle Attacks Against Protected Wi-Fi Networks

A Signature-Based Wireless Intrusion Detection System Framework for Multi-Channel Man-in-the-Middle Attacks Against Protected Wi-Fi Networks

Adversarial Sample Generation using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) and Classification for IEEE 802.11 using the Deep Deterministic Policy Gradient (DDPG)

One of today's most promising developments is wireless networking, as it enables people across the globe to stay connected. As the wireless networks' transmission medium is open, there are potential issues in safeguarding the privacy of the information. Though several security protocols exist in the literature for the preservation of information, most cases fail with a simple spoof attack. So, intrusion detection systems are vital in wireless networks as they help in the identification of harmful traffic. One of the challenges that exist in wireless intrusion detection systems (WIDS) is finding a balance between accuracy and false alarm rate. The purpose of this study is to provide a practical classification scheme for newer forms of attack. The AWID dataset is used in the experiment, which proposes a feature selection strategy using a combination of Elastic Net and recursive feature elimination. The best feature subset is obtained with 22 features, and a deep deterministic policy gradient learning algorithm is then used to classify attacks based on those features. Samples are generated using the Euclidean Jacobian-based Saliency Map Attack (EJSMA) to evaluate classification outcomes using adversarial samples. The meta-analysis reveals improved results in terms of feature production (22 features), classification accuracy (98.75% for testing samples and 85.24% for adversarial samples), and false alarm rates (0.35%).

Wireless Local Area Networks Threat Detection Using 1D-CNN.

Wireless Local Area Networks (WLANs) have revolutionized modern communication by providing a user-friendly and cost-efficient solution for Internet access and network resources. However, the increasing popularity of WLANs has also led to a rise in security threats, including jamming, flooding attacks, unfair radio channel access, user disconnection from access points, and injection attacks, among others. In this paper, we propose a machine learning algorithm to detect Layer 2 threats in WLANs through network traffic analysis. Our approach uses a deep neural network to identify malicious activity patterns. We detail the dataset used, including data preparation steps, such as preprocessing and division. We demonstrate the effectiveness of our solution through series of experiments and show that it outperforms other methods in terms of precision. The proposed algorithm can be successfully applied in Wireless Intrusion Detection Systems (WIDS) to enhance the security of WLANs and protect against potential attacks.

Embedded Wids Kismet Sebagai Perangkat Deteksi Serangan Data Link Layer Wi-Fi Access Point

The rapid development of wireless network technology has an impact with a significant increase of users. Wi-Fi is one of the wireless technologies that is currently used widely by humans as a medium of transmission to exchange information. The increasing in the number of users is directly proportional to the increase the risk of crime occurring. In this case an attack on a Wi-Fi network aimed at taking certain advantage of the user or it’s victim was done by threatening aspects of information security. Wireless Intrusion Detection System (WIDS) is a tool used to detect intrusions that occur against wireless network technology, one of it is Wi-Fi into the form of logs. Kismet is one of the wids that is free and opensource with complete WiFi attack detection capability. The implementation of WIDS Kismet applied to Raspberry Pi 4 was shown as an alternative wids device that has a low cost for use in small Wi-Fi network environments such as home networks or small enterprise. The analysis is done by calculating the performance of accuracy, precision, recall, and f-measure values from the implementation results using confusion matrix method. The result of the calculation obtained by the highest value for each calculated performance with 99.83% accuracy, 97.96% precision, 100% recall, and 98.9% fmeasure. The calculation aims to provide information that can be utilized for users who want to do a similar implementation and use it to detect Wi-Fi networks.

Genetic programming support vector machine model for a wireless intrusion detection system

Objectives. The rapid penetration of wireless communication technologies into the activities of both humans and Internet of Things (IoT) devices along with their widespread use by information consumers represents an epochal phenomenon. However, this is accompanied by the growing intensity of successful information attacks, involving the use of bot attacks via IoT, which, along with network attacks, has reached a critical level. Under such circumstances, there is an increasing need for new technological approaches to developing intrusion detection systems based on the latest achievements of artificial intelligence. The most important requirement for such a system consists in its operation on various unbalanced sets of attack data, which use different intrusion techniques. The synthesis of such an intrusion detection system is a difficult task due to the lack of universal methods for detecting technologically different attacks; moreover, the consistent application of known methods is unacceptably long. The aim of the present work is to eliminate such a scientific gap.Methods. Using the achievements of artificial intelligence in the fight against attacks, the authors proposed a method based on a combination of the genetic programming support vector machine (GPSVM) model using an unbalanced CICIDS2017 dataset.Results. The presented technological intrusion detection system architecture offers the possibility to train a dataset for detecting attacks on CICIDS2017 and extracting detection objects. The architecture provides for the separation of the dataset into verifiable and not verifiable elements, with the latter being added to the training set by feedback. By training the model and improving GPSVM training set, better accuracy is ensured. The operability of the new flowchart of the GPSVM model is demonstrated in terms of the entry of input data and output of data after processing using the training set of the GPSVM model. Numerical analysis based on the results of model experiments on selected quality indicators showed an increase in the accuracy of the results as compared to the known SVM method.Conclusions. Computer experiments have confirmed the methodological correctness of choosing a combination of the GPSVM model using an unbalanced CICIDS2017 dataset to increase the effectiveness of intrusion detection. A procedure for forming a training dataset based on feedback is proposed. The procedure involving the separation of datasets is shown to create conditions for improving the training of the model. The combination of the GPSVM model with an unbalanced CICIDS2017 dataset to collect a sample increases theaccuracy of intrusion detection to provide improved intrusion detection performance as compared to the SVM method.

Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network.

With the increasing openness and development of network technology, the network based on the wireless sensor network system has increasingly become an important tool for human social life and production, but it also brings some network security problems. Among them, focus is on network privacy disclosure and foreign intrusion and the research of intrusion detection and privacy protection has increasingly become an important topic of network security. This paper deeply studies the wireless sensor network system based on neural network on the basis of traditional privacy protection and intrusion detection system. Firstly, it applies particle swarm optimization algorithm and constructs a wireless sensor network intrusion detection system based on particle swarm optimization algorithm. The system includes important modules such as data extraction, data analysis, data feedback, and auxiliary decision-making. Compared with other algorithms, particle swarm optimization algorithm does not rely on problem information. It mainly uses real numbers to solve, so the algorithm has strong universality. At the same time, its corresponding principle is simple and easy to implement and less parameters need to be adjusted. Compared with other algorithms, particle swarm optimization algorithm has fast convergence speed and little memory requirement for the computer. At the same time, this paper uses the leap of particle swarm optimization algorithm to make it easier to find the global optimal solution. At the corresponding level of wireless sensor network privacy protection, based on the original data aggregation privacy protection scheme, this paper proposes a privacy protection scheme based on polynomial regression and a user privacy protection scheme based on the same state encryption, which further improves the security of privacy protection and facilitates the management of information. To realize the integrity of user privacy information protection, this paper realizes the decryption of data based on the correlation between binary metadata and compares the corresponding decrypted data with aggregated data, so as to complete the integrity of privacy data protection. The experimental results show that the binary metadata correlation decryption method proposed in this paper and the introduction of the corresponding particle swarm optimization algorithm improve the stability of the system by about 10%, the corresponding system security by a positive proportion, and the integrity of private data by about 10%; therefore, the algorithm proposed in this paper has obvious advantages.

Wireless Network Security Design And Analysis Using Wireless Intrusion Detection System

Network security measures are critical in maintaining a network; attacks that destroy the connection system between linked devices are incredibly harmful. To achieve network security, we must occasionally be uncomfortable with its use; this is typically a factor to consider when establishing a network security system. DOS (Denial of Service) attacks can be detected using the WIDS (Wireless Intrusion Detection System) approach. Implementing the Linux operating system with Snort as a sensor engine and Iptables as an attack handler can help protect wireless networks from potential threats. A WAN (Wide Area Network) network is constructed within the system configuration to reflect the test. The findings of the analysis of each test show that every action made by the attacker on the network can be identified, allowing for a remedy before more damage occurs.

Quality of service (QoS) based wireless network for mobile Ad Hoc networks

Quality of Service (QoS) requirements are possible with actual transmission of updates for wireless applications typically included in mission critical applications such as military use or disaster recovery. This research paper proposes an efficient Secure Quality Oriented Distributed (S-QOD) Secure Enhanced Adaptive 3 Acknowledgment (S-EA3ACK) and implements a new on-demand wireless intrusion detection system. S-QOD can improve a best performance of output quality (QoS) to reduce transmission delay, transmission time and also increase network communication throughput for S-EA3ACK using Enhanced Adaptive Acknowledgement (EAACK) DSA (Digital Signature Algorithm) with MAJE4 symmetric cryptography specially designed for MANET through Network Simulator-2.34 (NS2) to implement it.

A Signature-Based Wireless Intrusion Detection System Framework for Multi-Channel Man-in-The-Middle Attacks Against Protected Wi-Fi Networks

A Signature-Based Wireless Intrusion Detection System Framework for Multi-Channel Man-in-The-Middle Attacks Against Protected Wi-Fi Networks

Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks

Internet of Things (IoT) devices work mainly in wireless mediums; requiring different Intrusion Detection System (IDS) kind of solutions to leverage 802.11 header information for intrusion detection. Wireless-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired networks. This survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods, IDS techniques, IDS placement strategies, and traffic data analysis techniques. This paper’s main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific intrusions. Specifically, the Knowledge Discovery in Databases (KDD) Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network (WN). The paper starts with a review of various intrusion detection techniques, data collection methods and placement methods. The main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless environment. Intrusion detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural complexities. So this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless environment. The main wireless environments to look into would be Wireless Sensor Networks (WSN), Mobile Ad Hoc Networks (MANET) and IoT as this are the future trends and a lot of attacks have been targeted into these networks. So it is very crucial to design an IDS specifically to target on the wireless networks.

Computational intelligence techniques for automatic detection of Wi-Fi attacks in wireless IoT networks

These days, number of smart products based on Internet-of-Things (IoT) has been increased. These products are unified via various wireless technologies like, Bluetooth, Z-wave, Wi-Fi, Zigbee, etc. While the need on the wireless networks has improved, the assaults against them throughout the time have expanded on top. In order to identify these assaults, an intrusion detection system (IDS) with a prominent precision and low identification time is required. In this work, a machine learning (ML) based wireless intrusion detection system (WIDS) for wireless networks to effectively identify assaults against them has been proposed. A ML prototype has been implemented to categorize the wireless network records into ordinary or one of the particular assault categories. The operation of an IDS is extensively enhanced when the attributes are more discriminative and delegate. Different attribute selection methods have been investigated to identify the best set of attributes for the WIDS. The proposed model is evaluated on aegean wireless intrusion dataset using various parameters like attack detection rate, detection time, precision, F-measure, etc. The experimental evaluation is carried out in the tools like, Weka, Rstudio and Anaconda Navigator Python. Finally, the experimental result shows the best performing ML algorithm with best set of reduced attributes.

Naive Bayes and deep learning model for wireless intrusion detection systems

In recent years, online data is exponentially increasing on day basis and accessing that data un-authentically is a major issue. The intruder or attacker is an individual or team who violates the rules of governess by accessing or modifying the data. To protect the online data, there must be a proper mechanism called intrusion detection system (IDS) to be implemented and followed in a right manner. This paper presents a hybrid model for IDS to detect the various attacks in NSL-KDD dataset. The hybrid model is built with a combination of naive Bayes and deep learning (NB-DL) approaches. The NB-DL is tested with benchmarked datasets, and it has scored high accuracy in detecting the attacks. Nowadays, combining two or more different machine learning techniques is popular as multiple methods performance is averaged or selected the best performance between and so on.

Naive Bayes and deep learning model for wireless intrusion detection systems

Naive Bayes and deep learning model for wireless intrusion detection systems

WIDS: An Anomaly Based Intrusion Detection System for Wi-Fi (IEEE 802.11) Protocol

Over the last few decades, the Internet has seen unprecedented growth, with over 4.57 billion active users as of July 2022, encompassing 59% of the global population. In recent years, we have seen an increase in mobile computing and the Internet of Things (IoT), allowing more users to communicate through the Internet using wireless devices. Modern Internet users use their wireless IoT devices for a wide variety of services that include cloud computing and storage, social networking, content services, online banking, shopping, to name a few. Moreover, with the omnipresence of IoT devices, wireless networks are used for services like device control, user authentication, etc. Wi-Fi is the network of choice for most of these wireless communications. Although Wi-Fi networks have improved over recent years, little has been done to secure Wi-Fi networks against attacks. In this article, we present a Wireless Intrusion Detection System (WIDS); an anomaly behavior analysis approach to detect attacks on Wi-Fi networks with high accuracy and low false alarms. In this approach, we model the normal behavior of the Wi-Fi protocol, using n-grams, and use machine learning models to classify Wi-Fi traffic flows as normal or malicious. We have extensively tested our approach on multiple datasets collected locally at the University of Arizona and AWID family of datasets. Our approach can successfully detect all attacks on Wi-Fi protocols with low false positives (0.0174) and a varying low rate of false negatives for different attacks.

DESIGN OF WIRELESS INTRUSION DETECTION SYSTEM FOR DOS ATTACK AND MALICIOUS ACCESS POINT IN WI-FI NETWORKS

The IEEE 802.11 wireless networks are organized either in infrastructure mode or ad-hoc mode. These wireless networks are very common, because of their portability and ease of access. In infrastructure mode of wireless networks, wireless clients have to be aware of the environments due to vulnerability and various types of attacks that can made by wireless intruders. Wired Equivalent Privacy (WEP) and Wi-Fi Protected Access (WPA) security protocols are used to protect any wireless infrastructure against wireless intruders. The main problem of wireless network is to maintain the network security. Firewalls are not much capable to secure network from attacks because firewall can only detect the attacks which come from outside of the network. Therefore, Intrusion Detection System (IDS) plays an important role in network security. In general Intrusion Detection System (IDS) is used to detect unauthorized access, malicious access point and various types of wireless attacks in IEEE 802.11 infrastructure mode of wireless networks. Network based Wireless Intrusion Detection system (WIDS) is designed to detect Denial of Service (DOS) attack and Malicious Access Point (Evil Twin Attack) in Wi-Fi networks. Because DOS attack and Evil Twin Attack are the most significant attacks in IEEE 802.11 networks. In this work, a diverse detection algorithm is proposed to detect DOS attack and Evil Twin Attack which is based on number of deauthentication frames and beacon frames and some other fields. In this project, an experimental setup for wireless network infrastructure with one access point (AP) and three or four number of wireless clients are used to design Wireless Intrusion Detection System (WIDS). In that design, one client act as an attacker and another one client act as detector. In this work, Kali Linux based attack software tool is used to register wireless clients to launch attacks on the network. The set of commends are used to configure on the designed infrastructure wireless networks for testing the attacks and solutions

A Deep Gated Recurrent Unit based model for wireless intrusion detection system

Abstract With the advances and growth of various wireless technologies, it is imperative to implement robust Intrusion Detection Systems (IDS). This paper proposes the implementation of Deep Gated Recurrent Unit (DGRU) Based classifier as well as a wrapper-based feature extraction algorithm for Wireless IDS. We assess the performance of the DRGU IDS using the NSL-KDD benchmark dataset. Furthermore, we compare our framework to several popular algorithms including Artificial Neural Networks, Deep Long–Short Term Memory, Random Forest, Naive Bayes and Feed Forward Deep Neural Networks. The experimental outcomes demonstrate that the DGRU IDS displays a significant increase in performance over existing methods.

A deep learning method with wrapper based feature extraction for wireless intrusion detection system

In the past decade, wired and wireless computer networks have substantially evolved because of the rapid development of technologies such as the Internet of Things (IoT), wireless handled devices, vehicular networks, 4G and 5G, cyber-physical systems, etc. These technologies exchange large amount of data, and as a result, they are prone to several malicious actions, attacks and security threats that can compromise the availability and integrity of information or services. Therefore, the security and protection of the various communication infrastructures using an intrusion detection system (IDS) is of critical importance. In this research, we propose a Feed-Forward Deep Neural Network (FFDNN) wireless IDS system using a Wrapper Based Feature Extraction Unit (WFEU). The extraction method of the WFEU uses the Extra Trees algorithm in order to generate a reduced optimal feature vector. The effectiveness and efficiency of the WFEU-FFDNN is studied based on the UNSW-NB15 and the AWID intrusion detection datasets. Furthermore, the WFEU-FFDNN is compared to standard machine learning (ML) algorithms that include Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT) and k-Nearest Neighbor (kNN). The experimental studies include binary and multiclass types of attacks. The results suggested that the proposed WFEU-FFDNN has greater detection accuracy than other approaches. In the instance of the UNSW-NB15, the WFEU generated an optimal feature vector consisting of 22 attributes. Using this input vector; our approach achieved overall accuracies of 87.10% and 77.16% for the binary and multiclass classification schemes, respectively. In the instance of the AWID, a reduced input vector of 26 attributes was generated by the WFEU, and the experiments demonstrated that our method obtained overall accuracies of 99.66% and 99.77% for the binary and the multiclass classification configurations, respectively.

Efficient pattern matching algorithm for security and Binary Search Tree (BST) based memory system in Wireless Intrusion Detection System (WIDS)

Wireless Intrusion Detection System (WIDS) has been introduced for providing enhanced security level in WLANs, owing to the numerous and potentially devastating threats against it. WIDS are software- or hardware-based. The hardware-based approaches focus on memory efficiency in pattern matching. This paper proposes a pattern matching algorithm called All-Ready State Traversal pattern matching algorithm. This algorithm constructs the state traversal machine with 1280 bytes size, and enables users to store large sized string patterns in the pattern database. The state traversal machine facilitates the easy retrieval of these patterns through the path vector. Further, the proposed work also follows a number of basic ASCII characters with 128 bytes size; and designs the memory architecture using Binary Search Tree (BST) structure. The hardware generates the addresses of input strings. State traversal machine and bits split algorithms are used to merge together the common addresses of input strings. The merged addresses are encrypted and decrypted by Blowfish algorithm to allow the valid packets and to discard the invalid packets using WIDS. Thus, the proposed algorithm provides a significant reduction in memory usage than that of Aho–Corasick algorithm.