Information management collects data from several online systems. They analyze the information. They issue reports about information for supporting decision-making management. Utilizing current modern innovations try to controlling many obstacles such as, high cost, high battery power, and speed system, safety System without building a full system to solve all these problems together, we created a new internet of things ( IoT) system that provides attention to safety, and Security with low cost, low battery power, and high-speed System. As for the information management system. This paper aims at developing an active system for managing most of the smart farm and home obstacles, such issues to deal with the security system for the farm's and house and animal hanger, raining, irrigation and watering system, food supplement system, Also, a network was established to connect all those systems. Connected database storage was used, infra-red, The system is used for monitoring. They send all the collected information back to be maintained. Arduino will be used for programming this system
A botnet, a set of compromised machines controlled distantly by an attacker, is the basis of numerous security threats around the world. Command and Control servers are the backbones of botnet communications, where the bots and botmasters send report and attack orders to each other. Botnets are also categorized according to their C&C protocols. A Domain Name System method known as Fast-Flux Service Network (FFSN)-a special type of botnet-has been engaged by bot herders to cover malicious botnet activities and increase the lifetime of malicious servers by quickly changing the IP addresses of the domain name over time. Although several methods have been suggested for detecting FFSNs, they have low detection accuracy especially with zero-day domain. In this research, we propose a new system called Fast Flux Killer System (FFKS) that has the ability to detect FF-Domains in online mode with an implementation constructed on Adaptive Dynamic evolving Spiking Neural Network (ADeSNN). The proposed system proved its ability to detect FF domains in online mode with high detection accuracy (98.77%) compare with other algorithms, with low false positive and negative rates respectively. It is also proved a high level of performance. Additionally, the proposed adaptation of the algorithm enhanced and helped in the parameters customization process.
A botnet refers to a set of compromised machines controlled distantly by an attacker. Botnets are considered the basis of numerous security threats around the world. Command and control (C&C) servers are the backbone of botnet communications, in which bots send a report to the botmaster, and the latter sends attack orders to those bots. Botnets are also categorized according to their C&C protocols, such as internet relay chat (IRC) and peer-to-peer (P2P) botnets. A domain name system (DNS) method known as fast-flux is used by bot herders to cover malicious botnet activities and increase the lifetime of malicious servers by quickly changing the IP addresses of the domain names over time. Several methods have been suggested to detect fast-flux domains. However, these methods achieve low detection accuracy, especially for zero-day domains. They also entail a significantly long detection time and consume high memory storage. In this survey, we present an overview of the various techniques used to detect fast-flux domains according to solution scopes, namely, host-based, router-based, DNS-based, and cloud computing techniques. This survey provides an understanding of the problem, its current solution space, and the future research directions expected.
Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts.
Cloud computing is a very large storage space, can be accessed via an internet connection, this concept has appeared to facilitate the preservation of personal and corporate data and the easily of sharing, and this data can also be accessed from anywhere in the world as long as it is on the Internet, large gaps have emerged around data theft and viewing. Accordingly, researchers have developed algorithms and methods to protect this data, but the attempts to penetrate the data did not stop. In this research, we developed a method that combines XOR and Genetic algorithm to protect the data on the cloud through encryption operations and keep the key from being lost or stolen. The data that is uploaded to cloud computing may be important and we should not allow any party to see it or steal it. Therefore, it became imperative to protect this data and encrypt it. We have developed an algorithm that uses XOR and genetic algorithms in the encryption process.
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