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.

Enhancement in wireless networks had given users the ability to use the Internet without a physical connection to the router. Almost every Internet of Things (IoT) devices such as smartphones, drones, and cameras use wireless technology (Infrared, Bluetooth, IrDA, IEEE 802.11, etc.) to establish multiple interdevice connections simultaneously. With the flexibility of the wireless network, one can set up numerous ad-hoc networks on-demand, connecting hundreds to thousands of users, increasing productivity and profitability significantly. However, the number of network attacks in wireless networks that exploit such flexibilities in setting and tearing down networks has become very alarming. Perpetrators can launch attacks since there is no first line of defense in an ad hoc network setup besides the standard IEEE802.11 WPA2 authentication. One feasible countermeasure is to deploy intrusion detection systems at the edge of these ad hoc networks (Network-based IDS) or at the node level (Host-based IDS). The challenge here is that there is no readily available benchmark data available for IoT network traffic. Creating this benchmark data is very tedious as IoT can work on multiple platforms and networks, and crafting and labelling such dataset is very labor-intensive. This research aims to study the characteristics of existing datasets available such as KDD-Cup and NSL-KDD, and their suitability for wireless IDS implementation. We hypothesize that network features are parametrically different depending on the types of network and assigning weight dynamically to these features can potentially improve the subsequent threat classifications. This paper analyses packet and flow features for the data packet captured on a wireless network rather than a wired network. Combining domain heuristcs and early classification results, the paper had identified 19 header fields exclusive to wireless network that contain high information gain to be used as ML features in Wireless IDS.

Software-Defined Networking (SDN) is an emerging architecture that enables flexible and easy management and communication of large-scale networks. It offers programmable and centralized interfaces for making complex network decisions dynamically and seamlessly. However, SDN provides opportunities for businesses and individuals to build network applications based on their demands and improve their services. In contrast, it started to face a new array of security and privacy challenges and simultaneously introduced the threats of a single point of failure. Usually, attackers launch malicious attacks such as botnets and Distributed Denial of Service (DDoS) to the controller through OpenFlow switches. Deep learning (DL)-based security applications are trending, effectively detecting and mitigating potential threats with fast response. In this article, we analyze and show the performance of the DL methods to detect botnet-based DDoS attacks in an SDN-supported environment. A newly self-generated dataset is used for the evaluation. We also used feature weighting and tuning methods to select the best subset of features. We verify the measurements and simulation outcomes over a self-generated dataset and real testbed settings. The main aim of this study is to find a lightweight DL method with baseline hyper-parameters to detect botnet-based DDoS attacks with features and data that can be easily acquired. We observed that the best subset of features influences the performance of the DL method, and the prediction accuracy of the same method could be variated with a different set of features. Finally, based on empirical results, we found that the CNN method outperforms the dataset and real testbed settings. The detection rate of CNN reaches 99% for normal flows and 97% for attack flows.

Global Positioning System (GPS) has been developed in outdoor environments in recent years. GPS offers a wide range of applications in outdoor areas, including military, weather forecasting, vehicle tracking, mapping, farming, and many more. In an outdoor environment, an exact location, velocity, and time can be determined by using GPS. Rather than emitting satellite signals, GPS receivers passively receive them. However, due to No Line-of-Sight (NLoS), low signal strength, and low accuracy, GPS is not suitable to be used indoors. As consequence, the indoor environment necessitates a different Indoor Positioning System (IPS) approach that is capable to locate the position within a structure. IPS systems provide a variety of location-based indoor tracking solutions, such as Real-Time Location Systems (RTLS), indoor navigation, inventory management, and first-responder location systems. Different technologies, algorithms, and techniques have been proposed in IPS to determine the position and accuracy of the system. This paper introduces a review article on indoor positioning technologies, algorithms, and techniques. This review paper is expected to deliver a better understanding to the reader and compared the better solutions for IPS by choosing the suitable technologies, algorithms, and techniques that need to be implemented according to their situation. Keywords-Global positioning system (GPS); indoor positioning system (IPS); real-time location system (RTLS)477 | P a g e www.ijacsa.thesai.org

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