Wireless Network Intrusion Research Articles

FEDDBN-IDS: federated deep belief network-based wireless network intrusion detection system

Over the last 20 years, Wi-Fi technology has advanced to the point where most modern devices are small and rely on Wi-Fi to access the internet. Wi-Fi network security is severely questioned since there is no physical barrier separating a wireless network from a wired network, and the security procedures in place are defenseless against a wide range of threats. This study set out to assess federated learning, a new technique, as a possible remedy for privacy issues and the high expense of data collecting in network attack detection. To detect and identify cyber threats, especially in Wi-Fi networks, the research presents FEDDBN-IDS, a revolutionary intrusion detection system (IDS) that makes use of deep belief networks (DBNs) inside a federated deep learning (FDL) framework. Every device has a pre-trained DBN with stacking restricted Boltzmann machines (RBM) to learn low-dimensional characteristics from unlabelled local and private data. Later, these models are combined by a central server using federated learning (FL) to create a global model. The whole model is then enhanced by the central server with fully linked SoftMax layers to form a supervised neural network, which is then trained using publicly accessible labeled AWID datasets. Our federated technique produces a high degree of classification accuracy, ranging from 88% to 98%, according to the results of our studies.

An Effective Technique to Detect WIFI Unauthorized Access using Deep Belief Network

Network security has grown to be a major concern in recent years due to the popularity and development of Wi-Fi networks. However, the use of Wi-Fi networks is expanding quickly, and so is the number of attacks on Wi-Fi networks. In this paper, a novel WiFi Unauthorized Access Detection System (WUADS) technique has been proposed to detect unauthorized access in the WiFi network. Initially, the Wi-Fi frames are collected from the AWID dataset. The features of the Wi-Fi frame are extracted by using Principal Component Analysis (PCA). Finally, the Deep Belief Network (DBN) is employed for classification into authorized access and unauthorized access. The efficiency of the proposed WUADS technique was evaluated based on the parameters like accuracy, F1score, detection rate, precision, and recall. The performance analysis of the proposed WUADS technique achieves an overall accuracy range of 99.52%. The proposed WUADS method has a high success rate and the quickest attack detection time compared to deep learning techniques like CNN, RNN, and ANN. The proposed WUADS improves the overall accuracy better than 1.12%, 0.1%, and 14.22% comparative analysis of the SAE (Stacked AutoEncoder), WNIDS (wireless Network Intrusion Detection System), and 3D-ID (3 Dimensional-Identification) respectively.

Feature-Weighted Naive Bayesian Classifier for Wireless Network Intrusion Detection

Objective. Wireless sensor networks, crucial for various applications, face growing security challenges due to the escalating complexity and diversity of attack behaviours. This paper presents an advanced intrusion detection algorithm, leveraging feature-weighted Naive Bayes (NB), to enhance network attack detection accuracy. Methodology. Initially, a feature weighting algorithm is introduced to assign context-based weights to different feature terms. Subsequently, the NB algorithm is enhanced by incorporating Jensen–Shannon (JS) divergence, feature weighting, and inverse category frequency (ICF). Eventually, the improved NB algorithm is integrated into the intrusion detection model, and network event classification results are derived through a series of data processing steps applied to corresponding network traffic data. Results. The effectiveness of the proposed intrusion detection algorithm is evaluated through a comprehensive comparative analysis using the NSL-KDD dataset. Results demonstrate a significant enhancement in the detection accuracy of various attack types, including normal, denial of service (DoS), probe, remote-to-local (R2L), and user-to-root (U2R). Moreover, the proposed algorithm exhibits a lower false alarm rate compared to other algorithms. Conclusion. This paper introduces a wireless network intrusion algorithm that not only ensures improved detection accuracy and rate but also reduces the incidence of false detections. Addressing the evolving threat landscape faced by wireless sensor networks, this contribution represents a valuable advancement in intrusion detection technology.

A Collaborative Detection Method of Wireless Mobile Network Intrusion Based on Cloud Computing

In order to improve the communication security of wireless mobile network, a collaborative intrusion detection method based on cloud computing is studied. The mobile terminal and the cloud computing platform are connected by the wireless mobile network. The cloud computing platform authentication server adopts a dual server and multifactor authentication scheme for mobile cloud computing to provide authentication services for mobile terminal users. The web server of the cloud computing platform uses the intrusion node detection protocol of the neighbor classification mechanism to provide a communication security protocol for users; Using the HMM algorithm, the intrusion detection module of the computing platform realizes the intrusion detection of wireless mobile network. Finally, using authentication service, security protocol, and intrusion detection module completes the cooperative detection of mobile network intrusion. The experimental results show that this method can realize the cooperative detection of wireless mobile network intrusion, and the detection accuracy is as high as 98%, which ensures the communication security of wireless mobile network.

Trust in Intrusion Detection Systems: An Investigation of Performance Analysis for Machine Learning and Deep Learning Models

To design and develop AI-based cybersecurity systems (e.g., intrusion detection system (IDS)), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree (DT), K nearest neighbour (KNN), random forest (RF), and naïve Bayes (NB). The four deep learning techniques are LSTM (one and two layers) and GRU (one and two layers). Two datasets are used to classify the IDS attack type, including wireless sensor network detection system (WSN-DS) and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS.

An Improved K‐Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved k‐means clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each participant. Furthermore, the cosine distance of multiple perspectives is introduced in the algorithm to measure the similarity between network data objects in the improved k‐means clustering process, making the clustering results more reasonable and the judgment of network data behavior more accurate. The AWID, an open wireless network attack data set, is selected as the experimental data set. Its dimensionality reduces by the method of principal component analysis (PCA). Experimental results show that the improved k‐means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.

A Machine Learning Based Two-Stage Wi-Fi Network Intrusion Detection System

The growth of wireless networks has been remarkable in the last few years. One of the main reasons for this growth is the massive use of portable and stand-alone devices with wireless network connectivity. These devices have become essential on the daily basis in consumer electronics. As the dependency on wireless networks has increased, the attacks against them over time have increased as well. To detect these attacks, a network intrusion detection system (NIDS) with high accuracy and low detection time is needed. In this work, we propose a machine learning (ML) based wireless network intrusion detection system (WNIDS) for Wi-Fi networks to efficiently detect attacks against them. The proposed WNIDS consists of two stages that work together in a sequence. An ML model is developed for each stage to classify the network records into normal or one of the specific attack classes. We train and validate the ML model for WNIDS using the publicly available Aegean Wi-Fi Intrusion Dataset (AWID). Several feature selection techniques have been considered to identify the best features set for the WNIDS. Our two-stage WNIDS achieves an accuracy of 99.42% for multi-class classification with a reduced set of features. A module for eXplainable Artificial Intelligence (XAI) is implemented as well to understand the influence of features on each type of network traffic records.

Real-Time Intrusion Detection in Wireless Network: A Deep Learning-Based Intelligent Mechanism

With the development of the wireless network techniques, the number of cyber-attack increases significantly, which has seriously threat the security of Wireless Local Area Network (WLAN). The traditional intrusion detection technology is a prevalent area of study for numerous years, but it may not have a good detection performance in a real-time way. Therefore, it is urgent to design a detection mechanism to detect the attacks timely. In this paper, we exploit a CDBN (Conditional Deep Belief Network)-based intrusion detection mechanism to recognize the attack features and perform the wireless network intrusion detection in real time. To avoid the impact of the imbalanced dataset and the data redundancy on the detection accuracy, a window-based instance selection algorithm “SamSelect” is adopted to undersample the majority class data samples, and a Stacked Contractive Auto-Encoder (SCAE) algorithm is proposed to reduce the dimension of the data samples. By doing so, our proposed mechanism can effectively detect the potential attack and achieve high accuracy. The experiment results show that CDBN can be effectively combined with “SamSelect” and SCAE, and the proposed mechanism has a high detection speed and accuracy, with the average detection time 1.14 ms and the detection accuracy 0.974.

A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network

The diversity of network attacks poses severe challenges to intrusion detection systems (IDSs). Traditional attack recognition methods usually adopt mining data associations to identify anomalies, which has the disadvantages of a high false alarm rate (FAR), low recognition accuracy (ACC) and poor generalization ability. To ameliorate the comprehensive capabilities of IDS and strengthen network security, we propose a novel intrusion detection method based on the adaptive synthetic sampling (ADASYN) algorithm and an improved convolutional neural network (CNN). First, we use the ADASYN method to balance the sample distribution, which can effectively prevent the model from being sensitive to large samples and ignore small samples. Second, the improved CNN is based on the split convolution module (SPC-CNN), which can increase the diversity of features and eliminate the impact of interchannel information redundancy on model training. Then, an AS-CNN model mixed with ADASYN and SPC-CNN is used for intrusion detection tasks. Finally, the standard NSL-KDD dataset is selected to test AS-CNN. The simulation illustrates that the accuracy is 4.60% and 2.79% higher than that of the traditional CNN and RNN models, and the detection rate (DR) increased by 11.34% and 10.27%, respectively. Additionally, the FAR decreased by 15.58% and 14.57%, respectively, compared with the two models.

Combined Wireless Network Intrusion Detection Model Based on Deep Learning

In order to effectively detect wireless network intrusion behavior, a combined wireless network intrusion detection model based on deep learning was proposed. First, a feature database was generated by feature mapping, one-hot encoding, and normalization processing. Then, we built a deep belief network (DBN) with the multi-restricted Boltzmann machine (RBM) and the back propagation (BP) network. The BP network layer was connected as an auxiliary layer to the end of the RBM. The back-propagation algorithm was used to fine-tune the weight of the multi-restricted Boltzmann machine. Finally, the support vector machine (SVM) was used to train the detection method. After training, the intrusion detection model, which had the DBN-SVM detection method, was determined. The experimental results show that the detection model has good intrusion detection performance.

Wireless Network Intrusion Detection Based on Improved Convolutional Neural Network

The diversification of wireless network traffic attack characteristics has led to the problems what traditional intrusion detection technology with high false positive rate, low detection efficiency, and poor generalization ability. In order to enhance the security and improve the detection ability of malicious intrusion behavior in a wireless network, this paper proposes a wireless network intrusion detection method based on improved convolutional neural network (ICNN). First, the network traffic data is characterized and preprocessed, then modeled the network intrusion traffic data by ICNN. The low-level intrusion traffic data is abstractly represented as advanced features by CNN, which extracted autonomously the sample features, and optimizing network parameters by stochastic gradient descent algorithm to converge the model. Finally, we conducted a sample test to detect the intrusion behavior of the network. The simulation results show that the method proposed in our paper has higher detection accuracy and true positive rate together with a lower false positive rate. The test results on the test set KDDTest + in our paper show that compared with the traditional models, the detection accuracy is 8.82% and 0.51% higher than that of LeNet-5 and DBN, respectively, and the recall rate is 4.24% and 1.16% higher than that of LeNet-5 and RNN, respectively, while the false positive rate is lower than the other three types of models. It also has a big advantage compared to the IDABCNN and NIDMBCNN methods.

스마트 홈의 위험수준별 침입 트래픽 분석을 사용한 침입대응 기법에 대한 연구

스마트 홈 시스템은 주거 생활의 편리함을 위해 새롭게 신축되는 건물에 대부분 설치되고 있다. 그러나, 스마트 홈 시스템이 보편화되고, 확산 속도가 빨라짐에 따라 해커들의 홈 네트워크 시스템 공격이 증가할 것으로 예상된다. 본 논문에서는, 스마트 홈의 위험수준별 침입 대응을 하기 위해 유선 네트워크와 무선 네트워크에서 발생한 침입 사례와 공격이 발생할 수 있는 가상 상황을 시나리오로 만들어 데이터베이스로 구축하였다. 이것을 기반으로 보안에 취약한 스마트 홈 사용자들에게 실시간으로 불법 침입 트래픽을 찾아내 침입 사실을 알려주고 공격을 차단하는 침입대응 알고리즘을 설계하였다. Smart home system are being installed in the most new construction of building for the convenience of living life. As smart home systems are becoming more common and their diffusion rates are faster, hacker's attack for the smart home system will be increased. In this paper, Risk level of smart home's to do respond to intrusion that occurred from the wired network and wireless network intrusion cases and attacks can occur in a virtual situation created scenarios to build a database. This is based on the smart home users vulnerable to security to know finding illegal intrusion traffic in real-time and attack prevent was designed the intrusion detection algorithm.