A pay-and-stay model for tackling intruders in hybrid wireless mesh networks

Intrusion handling in Wireless Mesh Networks (WMNs) is a relatively less addressed topic. The difficulty may lie in the fact that there are other wireless networks for which some intrusion detection or prevention schemes (IDS or IPS) are proposed that could also be applied in some way in a WMN setting. As those schemes are contributing, researchers may not find it necessary to specifically focus on this field. Another critical reason may be the difficulty in developing an effective scheme for WMN. In fact, the structural differences among various wireless ad hoc networking technologies make it imperative to devise the mechanisms in subtle but critically different ways. For WMN, there is a proper network backbone that is called mesh backbone (which is not present in many other wireless network counterparts), which supports the fringe part or the mesh clients. Hence, it is often possible to install the intrusion handling mechanisms or agents in the stable part and allowing some flexibility in the client or fringe parts. Nonetheless, instead of thinking in this pattern, we take a different approach of tackling intrusion by allowing an intruder to stay in the network as long as it proves to be worthy of staying in the network by supporting the network’s regular activities. The idea is that; not always direct purging out of rogue entities is useful but rather exploiting the intruder’s resources, the network could get benefited. We call our approach an intrusion tackling mechanism and term it as Pay-and-Stay model. Alongside presenting the details and analysis of our model, in this paper, we also present the basics of various forms of intrusion handling in such types of networks. By our evaluation results, we found that the model could be very effective in handling intruders and defending the network against a broad range of security attacks

Towards an effective intrusion detection model using focal loss variational autoencoder for internet of things (IoT)

As the range of security attacks increases across diverse network applications, intrusion detection systems are of central interest. Such detection systems are more crucial for the Internet of Things (IoT) due to the voluminous and sensitive data it produces. However, the real-world network produces imbalanced traffic including different and unknown attack types. Due to this imbalanced nature of network traffic, the traditional learning-based detection techniques suffer from lower overall detection performance, higher false-positive rate, and lower minority-class attack detection rates. To address the issue, we propose a novel deep generative-based model called Class-wise Focal Loss Variational AutoEncoder (CFLVAE) which overcomes the data imbalance problem by generating new samples for minority attack classes. Furthermore, we design an effective and cost-sensitive objective function called Class-wise Focal Loss (CFL) to train the traditional Variational AutoEncoder (VAE). The CFL objective function focuses on different minority class samples and scrutinizes high-level feature representation of observed data. This leads the VAE to generate more realistic, diverse, and quality intrusion data to create a well-balanced intrusion dataset. The balanced dataset results in improving the intrusion detection accuracy of learning-based classifiers. Therefore, a Deep Neural Network (DNN) classifier with a unique architecture is then trained using the balanced intrusion dataset to enhance the detection performance. Moreover, we utilize a challenging and highly imbalanced intrusion dataset called NSL-KDD to conduct an extensive experiment with the proposed model. The results demonstrate that the proposed CFLVAE with DNN (CFLVAE-DNN) model obtains promising performance in generating realistic new intrusion data samples and achieves superior intrusion detection performance. Additionally, the proposed CFLVAE-DNN model outperforms several state-of-the-art data generation and traditional intrusion detection methods. Specifically, the CFLVAE-DNN achieves 88.08% overall intrusion detection accuracy and 3.77% false positive rate. More significantly, it obtains the highest low-frequency attack detection rates for U2R (79.25%) and R2L (67.5%) against all the state-of-the-art algorithms

Tackling Intruders in Wireless Mesh Networks

This chapter presents a different approach of tackling intruders in Wireless Mesh Networks (WMN). Traditional approach of intruder detection and prevention suggests purging out intruders immediately after their detection. In our work, we take a different approach to tackle intruder rather than purging it out of the network unless it is marked as a direct threat to the network’s operation. Our intrusion tackling model is termed ‘Pay-and-Stay’ (PaS) model which allows a rogue node to stay in the network only with the expense of doing some traffic forwarding tasks in the network. Failing to carry out the required tasks of packet forwarding disqualifies the node permanently and eventually that rogue entity is purged out. Alongside presenting our approach, we briefly talk about other available literature, essential knowledge on wireless network intrusion detection and prevention, and status of intrusion related works for WMN

Exploring Machine Learning for Predicting Cerebral Stroke: A Study in Discovery

Cerebral strokes, the abrupt cessation of blood flow to the brain, lead to a cascade of events, resulting in cellular damage due to oxygen and nutrient deprivation. Contemporary lifestyle factors, including high glucose levels, heart disease, obesity, and diabetes, heighten the risk of stroke. This research investigates the application of robust machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), and K-nearest neighbor (KNN), to the prediction of cerebral strokes. Stroke data is collected from Harvard Dataverse Repository. The data includes—clinical, physiological, behavioral, demographic, and historical data. The Synthetic Minority Oversampling Technique (SMOTE), adaptive synthetic sampling (ADASYN), and the Random Oversampling Technique (ROSE) are used to address class imbalances to improve the accuracy of minority classes. To address the challenge of forecasting strokes from partial and imbalanced physiological data, this study introduces a novel hybrid ML approach by combining a machine learning method with an oversampling technique called ADASYN_RF. ADASYN is an oversampling technique used to resample the imbalanced dataset then RF is implemented on the resampled dataset. Also, other oversampling techniques and ML models are implemented to compare the results. Notably, the RF algorithm paired with ADASYN achieves an exceptional performance of 99% detection accuracy, exhibiting its dominance in stroke prediction. The proposed approach enables cost-effective, precise stroke prediction, providing a valuable tool for clinical diagnosis