Biblio

Wireless sensor networks (WSN) hold numerous battery operated, compact sized, and inexpensive sensor nodes, which are commonly employed to observe the physical parameters in the target environment. As the sensor nodes undergo arbitrary placement in the open areas, there is a higher possibility of affected by distinct kinds of attacks. For resolving the issue, intrusion detection system (IDS) is developed. This paper presents a new optimal Support Vector Machine (OSVM) based IDS in WSN. The presented OSVM model involves the proficient selection of optimal kernels in the SVM model using whale optimization algorithm (WOA) for intrusion detection. Since the SVM kernel gets altered using WOA, the application of OSVM model can be used for the detection of intrusions with proficient results. The performance of the OSVM model has been investigated on the benchmark NSL KDDCup 99 dataset. The resultant simulation values portrayed the effectual results of the OSVM model by obtaining a superior accuracy of 94.09% and detection rate of 95.02%.

In machine learning, feature engineering has been a pivotal stage in building a high-quality predictor. Particularly, this work explores the multiple Kernel Discriminant Component Analysis (mKDCA) feature-map and its variants. However, seeking the right subset of kernels for mKDCA feature-map can be challenging. Therefore, we consider the problem of kernel selection, and propose an algorithm based on Differential Mutual Information (DMI) and incremental forward search. DMI serves as an effective metric for selecting kernels, as is theoretically supported by mutual information and Fisher's discriminant analysis. On the other hand, incremental forward search plays a role in removing redundancy among kernels. Finally, we illustrate the potential of the method via an application in privacy-aware classification, and show on three mobile-sensing datasets that selecting an effective set of kernels for mKDCA feature-maps can enhance the utility classification performance, while successfully preserve the data privacy. Specifically, the results show that the proposed DMI forward search method can perform better than the state-of-the-art, and, with much smaller computational cost, can perform as well as the optimal, yet computationally expensive, exhaustive search.