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A distributed firewall is a security application that monitors and controls traffic on an organization's network. While centralized firewalls are used against attacks coming from outside a network, distributed firewalls are considered for inside attacks from internal networks such as wireless access and VPN tunnel. Distributed firewalls use policies, which are stated by rules, to find anomalous packets. However, such static rules may be incomplete. In this case, by monitoring firewall logs, the anomalies can be detected. Such logs become big when networks have high traffic, but their hidden knowledge contains valuable information about existing anomalies. In this paper, to detect the anomalies, we extract patterns from big data logs of distributed firewalls using data mining and machine learning. The proposed method is applied to big logs from distributed firewalls in a real security environment, and results are analyzed. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) refer to a group of battery-operated tiny sensor nodes having vast application areas in daily use. These are spatially dispersed and dedicated wireless sensor nodes for observing and recording the different parameter and physical conditions of the surroundings. There is a recent advancement in the field of network connectivity and computations in WSNs. The key functions of WSNs are a data extraction and to transmit the extracted data to the server placed at an isolated location. Various types of WSNs like underground underwater, terrestrial, and multimedia networks get applications domains such as in industrial automation, traffic monitoring and control medical device monitoring, and many other areas. Despite the thriving market, there are several challenges like energy efficiency, limited storage and computation, low bandwidth, high error rates, scalability, and survivability in harsh environment; hence, network lifespan expanding is a critical demanding issues. So many researchers have earlier focused towards finding the optimal path in between member node and sink node, so that energy depletion can be reduced to improve the network lifespan. There are different challenges in WSNs but one of the most challenging issues is how to minimize the energy consumption; numerous bio-inspired techniques have been proposed previously to obtain an optimal path between the member node and the sink node. In this manuscript, we are presenting a comprehensive survey on optimization technique-based routing and clustering. The study of this comprehensive survey offers in-depth summary of the past researches in the area of WSNs. [ABSTRACT FROM AUTHOR]

Wireless network sensors and their use in traffic monitoring, traffic density determination or vehicle speed detection and classification have recently been the focus of interest for researchers. This article describes how a new sensor circuit was designed to deliver instantaneous, real-time and novel solutions as a vehicle detection system, which is more powerful than the nodes used in other studies, and gives results with smaller error margins due to its serial communication qualification. With the proposed logic algorithm, it was possible to categorise the instantaneous traffic status of a road in four levels: no traffic, mild traffic, heavy traffic and very heavy traffic. Additionally, with the nodes placed at the beginning and the end of the road, the number of vehicles per hour for a day was determined and traffic was analysed. Then, vehicles passing by were classified with a proposed classification algorithm and magnetic signature length (MSL) parameter as cars, minibuses, buses and trucks, and an accuracy rate of 95% was obtained. As the last application, the direction of motion of the vehicle on the x-axis as well as left-to-right or right-to-left directions was determined, and the result was 94% accurate. The simplicity of the proposed algorithms, the absence of any complex mathematical calculations, the low cost of the sensor node and circuit and the low power consumption of the communication system demonstrate the superiority of this system in comparison with other studies. [ABSTRACT FROM AUTHOR]