Akıllı evlerde ıot tabanlı akıllı ölçüm sistemi

Even while new technologies are being developed to aid in the growth of humanity, their security systems still have weaknesses that might be exploited by those who want to steal or damage the data of others. An example of a typical security issue is the usage of passwords for devices that are either too easy to crack or too simple for users to remember. Botnets are a tactic used by malicious actors; they are networks of compromised devices that may be used to execute commands. Botnets are networks of hacked computing devices. As a direct result, issues about the security of the Internet of Things (IoT) must be addressed. According to, there might be as many as 26 billion Internet of Things (IoT) devices linked to the internet by the year 2020. Therefore, it is crucial to develop solutions for safeguarding the Internet of Things' security (IoT). In many network settings, intrusion detection systems serve as the first line of defense for network security. Several studies have been undertaken in this field of study, including [8] and [9], which use supervised machine learning to identify instances of hazardous behavior. [8] and [9] In these sorts of systems, the process of tagging databases demands a large investment of both computational and human resources. During the dataset preparation phase, also known as step 1, this technique's algorithm picks features based on the qualities of the data sets in order to organize them in decreasing order of their degree of similarity. This procedure is known as feature selection. DoS, R2L, U2R, and probing are among the four types of attacks that are included in the KDD CUP 99(KDD) dataset, which was created for testing IDS. There are 41 KDD features that are unique to every TCP connection. These characteristics may be categorized into three groups: traffic features, fundamental features, and content features. KDD has been used often in situations requiring intrusion detection data mining and machine learning investigations.