Wireless Networks facilitate the ease of communication for sharing the crucial information. Recently, most of the small and large-scale companies, educational institutions, government organizations, medical sectors, military and banking sectors are using the wireless networks. Security threats, a common term found both in wired as well as in wireless networks. However, it holds lot of importance in wireless networks because of its susceptible nature to threats. Security concerns in WLAN are studied and many organizations concluded that Wireless Intrusion Detection Systems (WIDS) is an essential element in network security infrastructure to monitor wireless activity for signs of attacks. However, it is an indisputable fact that the art of detecting attacks remains in its infancy. WIDS generally collect the activities within the protected network and analyze them to detect intrusions and generates an intrusion alarm. Irrespective of the different types of Intrusion Detection Systems, the major problems arising with WIDS is its inability to handle large volumes of alarms and more prone to false alarm attacks. Reducing the false alarms can improve the overall efficiency of the WIDS. Many techniques have been proposed in the literature to reduce the false alarm rates. However, most of the existing techniques are failed to provide desirable result and the high complexity to achieve high detection rate with less false alarm rates. This is the right time to propose a new technique for providing high detection accuracy with less false alarm rate. This paper made an extensive survey about the role of machine learning techniques to reduce the false alarm rate in WLAN IEEE 802.11. This survey proved that the substantial improvement has been achieved by reducing false alarm rate through machine learning algorithms. In addition to that, advancements specific to machine learning approaches is studied meticulously and a filtration technique is proposed.

This paper presents a system model of fuel cell based distributed generation systems (DGS) in a standalone AC power supply system. Non-isolated high step-up DC-DC converters are required widely in the industrial applications. Many of these conventional DC-DC converters have the disadvantages of operating at high duty-cycle, high switch voltage stress and high diode peak current. A three-level step-up converter is implemented to boost the fuel cell stack voltage of 90V to 340V. This high DC link voltage is fed to the voltage source inverter and interfaced with the load. Two feedback control loops are designed to make the system operate in stable conditions. This system configuration is suitable for low-power applications. The Performance of the developed system is analyzed in MATLAB/Simulink environment under steady state and transient conditions.

To maintain the integrity and protection of networks, intrusion detection systems play a vital role. Growth of wireless networks turned the globe to perform all pecuniary tasks online resulting a lot of security breaches in the network. One of the common breaches happening in network is the intruders who eventually tries to bypass the adopted security framework. Every day new intrusions arises and new solutions as well, however the research in making the intrusion detection system intelligent holds energetic. Today most of the systems are becoming intelligent by adopting machine learning and artificial intelligence algorithms. Success of building an efficient machine learning model to make intelligent intrusion detection system is relied on the effective features considered for classification and prediction. Thus, feature reduction is an integral part for discarding irrelevant and redundant features to produce a computationally decisive system that can identify defects with high accuracy. This implementation is an attempt to identify the smaller feature set possible for the well adopted wireless intrusion detection dataset AWID. Here, we proposed a LASSO based implementation to produce a smaller decisive set of features. Incorporation of Lasso on feature reduction not only provides a smaller set of features, but also allow to adopt prediction algorithms inside Lasso resulting lesser number of false alarms as well.

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%).