## 1 Introduction
### Assessing the Security of IoT Software
The authors note that non-ML statements will precede and succeed the ML ones, since apart from the intelligent component, each step comprises strictly defined rules (e.g., unpacking archives with files, ranking documents by their size, etc.). The matrix's validity was confirmed through expert analysis. For the second part, an analysis of research papers relevant to analyzing IoT systems is conducted. Each study is categorized and assigned based on its attributes to one or more SA stages and one or more ML tasks. The second matrix has the same types of rows and columns, but the cells are the research papers that were categorized under the corresponding stage-task couple. The two models make it possible to create methodological solutions that are theoretically and practically sound in order to provide information security in the IoT systems domain. This, of course, necessitates the development of a suitable framework that can ensure the execution of all phases utilizing the wide range of ML methods available for big data and heterogeneous data. The novelty of this work resides in the fact that it takes into consideration the phases of data collection and preparation that precede the code analysis. Thus, this work refers from previous ones in that it covers the entirety of the SA process. Additionally, this review is the first to consider the full range of ML solutions, both theoretically and practically, for each stage of SA. Finally, not only is SA divided into stages, but it is also suggested to represent the actions of these stages in a formalizedmanner, which involves transforming the form and content of the data being studied in the IoT system. He et al. [33] propose a homology detection method based on a clonal selection algorithm for detecting vulnerabilities in IoT firmwares. The proposed problem indicates that methods utilizing machine learning for detecting vulnerabilities in IoT firmwares need sample data of firmware vulnerabilities. This sample data is very scarce for some types of vulnerabilities. The authors characterize the results achieved by machine learning algorithms on this matter as \"not ideal\" and attribute this to their need for a large set of sample data. To that end, this work proposes a firmware vulnerability homology detection method based on the clonal selection algorithm combined with the simulated annealing algorithm. Unlike preexisting machine learning methods, this method relies solely on the affinity between the objective function and the detector, eliminating the need for extensive sample data sets. In the clonal selection algorithm of biology, the immune system identifies antigens and creates a variety of plasma cells to produce antibodies that are customized based on the specific characteristics of each antigen. Antibodies that have a high affinity to their respective antigens are retained, while those with low affinities are discarded. When applied to the method in question, the optimization problem is the antigen, the feasible solution to the optimization problem is the antibody, and the quality of the feasible solution is represented by affinity. Essentially, the antigen is a vulnerability function, and affinity represents the similarity between the sample function and the original one. The simulated annealing algorithm is a general optimization algorithm. It begins from a high initial temperature and randomly explores the solution space to find the global optimal solution of the objective function. The main idea is to use the probability of a local optimal solution for obtaining the global optimal solution. This work combines the efficient local search capability of simulated annealing with the rapid convergence of the clonal selection algorithm. The proposed method is tested against a neural network algorithm that attempts to tackle the same issue. The evaluating indicators are recall rate and accuracy. The recall rate is the ratio of the number of actual vulnerabilities in the algorithm's vulnerability set and the total number of vulnerabilities that should be detected.