With the burgeoning development of IoT technology, the frequency of network attacks targeting IoT devices is escalating, amplifying the prominence of security concerns. This study proposes a traffic identification methodology based on Long Short-Term Memory (LSTM), adept at discerning diverse attack patterns such as flood attacks, intrusion attacks, and deception attacks. Leveraging the CIC IoT dataset 2023 from the University of New Brunswick (UNB), this dataset comprehensively encompasses attack categories, effectively mirroring the spectrum of attacks faced by IoT devices in real-world scenarios. The model proposed in this study yielded exceptional results, achieving an accuracy rate of 96%. Experimental findings substantiate the efficacy of the proposed approach in detecting and identifying various attacks on IoT devices. This study presents a viable strategy for recognizing diverse attack patterns encountered by IoT devices, thereby fortifying the security of IoT ecosystems.