This article shows us the study results of a method for identifying the processor architecture of an executable code based on machine learning. In the first part of the article we see an overview of existing solutions for machine code identifying and we see how the author makes a new method assumption. The author considers features of the machine code instructions and build its frequency-byte model. There is a processor architecture identification scheme, which is based on this model. Apart from that we see the frequency signatures which are provided for the following Top 10 processor architectures: amd64, arm64, armel, armhf, i386, mips, mips64el, mipsel, ppc64el, s390x.
Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts’ work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of “Form and Content” data transformations.
One of the reasons for the implementation of information security threats in organizations is the insider activity of its employees. There is a big challenge to detect stego-insiders-employees who create stego-channels to secretly receive malicious information and transfer confidential information across the organization’s perimeter. Especially presently, with great popularity of wireless sensor networks (WSNs) and Internet of Things (IoT) devices, there is a big variety of information that could be gathered and processed by stego-insiders. Consequently, the problem arises of identifying such intruders and their transmission channels. The paper proposes an approach to solving this problem. The paper provides a review of the related works in terms of insider models and methods of their identification, including techniques for handling insider attacks in WSN, as well methods of embedding and detection of stego-embeddings. This allows singling out the basic features of stego-insiders, which could be determined by their behavior in the network. In the interests of storing these attributes of user behavior, as well as storing such attributes from large-scale WSN, a hybrid NoSQL database is created based on graph and document-oriented approaches. The algorithms for determining each of the features using the NoSQL database are specified. The general scheme of stego-insider detection is also provided. To confirm the efficiency of the approach, an experiment was carried out on a real network. During the experiment, a database of user behavior was collected. Then, user behavior features were retrieved from the database using special SQL queries. The analysis of the results of SQL queries is carried out, and their applicability for determining the attribute is justified. Weak points of the approach and ways to improve them are indicated.
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