Our world today relies heavily on informatics and the internet, as computers and communications networks have increased day by day. In fact, the increase is not limited to portable devices such as smartphones and tablets, but also to home appliances such as: televisions, refrigerators, and controllers. It has made them more vulnerable to electronic attacks. The denial of service (DoS) attack is one of the most common attacks that affect the provision of services and commercial sites over the internet. As a result, we decided in this paper to create a smart model that depends on the swarm algorithms to detect the attack of denial of service in internet networks, because the intelligence algorithms have flexibility, elegance and adaptation to different situations. The particle swarm algorithm and the bee colony algorithm were used to detect the packets that had been exposed to the DoS attack, and a comparison was made between the two algorithms to see which of them can accurately characterize the DoS attack.

The spread of the internet of things (IoT) greatly are to its targeting by other parties that are considered suspicious or malicious, such as the attacks that are exposed to various networks to endanger their security. For this reason, it was necessary to take strict measures to protect the security and stability of networks in general and the internet of things in particular. It is worth noting that the current study presented a model and chose a long shorts term memory (LSTM) for attack detection through the use of deep learning technology via Keywords: the internet of things as well as the detection of bots in IoT systems.