## 2 The State-of-the-Art
Nevertheless, there are limitations--the inability to detect and respond to unknown attacks (zero-day attacks), the low efficiency of detection of multi-vector attacks; a high level of false positives, a significant response time that is unacceptable for real-time systems, and the need for significant amounts of computing resources. Another important aspect is the need to select a minimum and sufficient set of informative network traffic features that are able to indicate the presence of cyberattacks in the IoT infrastructure. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Goal** & **MLA** & **Data Set** & **Result** \\ \hline [PERSON]; [PERSON]; & malware detection, & Convolutional Neural & Real network & Accuracy of 91.32\%, \\ [PERSON]; [PERSON]; & zero-day malware & Network and binary & environments & Precision of 91.67\%, \\ [PERSON] [14] & classification & visualization & & Recall of 91.03\% \\ \hline  & & Logistic Regression, Decision & & \\  & & Tree, Adaptive boosting, KNN, & & \\  & & Random Forest, Naive Bayes, & & \\  & & Gated Recurrent Units, Simple & & \\ [PERSON]; & anomaly detection, & Recurrent Neural Network, & UNSW-NB15, & Performance about \\ [PERSON]; [PERSON]; & attack detection & Convolutional Neural & CICIDS-2017, ICS & 99.9\% using Random \\ [PERSON]. [15] & Network and Long short-Term Memory, Convolutional & Cyberattack & Forest (CICIDS-2017) \\  & & Neural Network, Long & & \\  & & short-Term Memory, Deep & & \\  & & Neural Network & & \\ \hline [PERSON]; & intrusion detection & Logistic regression, SVM, & UCI Machine & \\ [PERSON] [16] & random forest & Learning Repository & Accuracy of about 99\% \\ \hline [PERSON]; & cyber-attack detection & K-nearest neighbor, random & DS2 OS, NSL-KDD, & Accuracy up to 99\%, \\ [PERSON]; [PERSON] [17] & against IoT networks & forest, XGBoost & Bot-IoT & detection 90–100\% \\ \hline [PERSON]; & DDoS attacks & ELM, semi-supervised extreme & & \\ [PERSON]. [18] & attacks mitigation & learning machines & UNB-ISCX & Accuracy of \\  & & & & about 96.28\% \\ \hline [PERSON]; [PERSON]; & DoS, user-to-root & & & \\ [PERSON]. [19] & (U2R), & Stacked-deep & & \\  & remote-to-local (R2L) & polynomial network & NSL-KDD & & Accuracy up to 99.4\%, \\  & & detection, & & \\  & probe, intrusions & & & \\ \hline  & & AdaBoost, extremely & & \\ [PERSON]; & Survey on machine & randomized trees, multilayer & & \\ [PERSON]; & learning algorithms & perceptron, classification and & & \\ [PERSON] [20] & for DoS & regression trees, random forest, & & \\  & attacks detection & gradient boosted machine, & & \\  & & extreme gradient boosting & & \\ \hline [PERSON]; & & & & \\ [PERSON]; & & & \\ [PERSON]; & Detection of & & & \\ [PERSON]; & DDoS attacks & & & \\ [PERSON]; & & & & \\ [PERSON] [21] & & & & \\ \hline [PERSON]; & Detection of the & & & Accuracy up to 99.98\%, \\ [PERSON] [22] & DDoS attacks & Random Forest & NSL-KDD, NBaloT & precision up to 99.87\%, \\  & & DDoS attacks & & & recall up to 100\%, \\  & & & & \\  & & & & and F-score up \\  & & & & to 99.73\% \\ \hline [PERSON]; & Investigation of & & & \\ [PERSON]; & DoS/DDoS attacks & & & \\ [PERSON]; & detection for IoT & & & \\ [PERSON]; & based on ML & & & \\ [PERSON] [23] & algorithms & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Machine learning algorithm (MLA) efficiency for cyberattack detection in the Internet of Things infrastructure. To summarize, there is a strong need to evolve new methods for cyberattack detection in the IoT infrastructure. To do this, we are to eliminate technique drawbacks and increase the detection efficiency of detecting known and unknown cyberattacks in the IoT infrastructure. \begin{table}