Search

Search Results

• CASCADE CLASSIFICATION ALGORITHM FOR DETECTING MALICIOUS SOFTWARE BY STATIC ANALYSIS

  А.V. Kozachok , А. V. Kozachok , S.S. Matovykh

  18-35

  2025-11-10

  Abstract ▼

  A study is presented on the development and experimental validation of a two-level cascading architecture for static classification of Portable Executable (PE) format executable files. The aim of the work is to reduce computing costs without compromising the quality of malware detection. At the first level of the cascade, a decision tree model is used, trained on the ten most informative features, providing a high completeness of Recall 0.990 detection with an acceptable error of 1 kind. The second level is implemented by the random forest model on forty features and is intended for clarifying classification, reaching the metrics Precision 0.988 and Recall 0.987 with an F1 measure of 0.988. The classification threshold at the first level was established empirically, taking into account the minimization of errors of the second kind, while at the second level the optimal threshold value was determined by the Juden index, which provides a balanced ratio of sensitivity and specificity. Experiments on a representative sample have shown that with a malicious traffic fraction of < 20%, the proposed cascade reduces the average analysis time of one object by 5-12% compared to the 40-feature model while maintaining comparable classification quality.
  The time limit of the cascade,  = 20.6%, is analytically derived, confirmed by empirical data. The practical significance of the work lies in the possibility of integrating the proposed algorithm into antivirus gateways and endpoint protection tools, where fast response and high completeness of detection are required during mass scanning of mostly legitimate code.

• OPTIMIZATION OF PID PARAMETERS OF SERVO SYSTEMS USING A GENETIC ALGORITHM AND A NEURAL NETWORK CLASSIFIER

  Ahmad Zoualfikar , Y.А. Kravchenko , А.М. Mansour

  237-250

  2025-10-01

  Abstract ▼

  Machine learning algorithms play a vital role in enhancing the performance of industrial systems, providing high precision and operational efficiency in real time. In servo motor control systems, these algorithms help reduce noise and vibration, improving efficiency and extending equipment lifespan. This article examines various types of noise that occur and their negative impact on industrial processes. The primary research objective is to optimize PID controller parameters in servo systems using a combined algorithm that combines neural networks and genetic algorithms. Unlike traditional methods such as genetic algorithms (GA) and particle swarm optimization (PSO), which suffer from slow convergence and risk of motor damage, the proposed solution is based on a control software platform. This platform ensures safe real-time interaction with the servo motor. A CAN Bus-based control system has been developed that enables developers to: read all servo motor parameters (speed, current, voltage, encoder position); modify PID coefficients with a single click, eliminating the need for manual tuning as in MOTO-MASTER. The implementation of the developed control system allowed the use of a trained neural classifier to constrain PID parameters within safe limits, reducing search space and accelerating the optimization process. Experimental results on SPH-S servo motors demonstrated significant reduction in noise and mechanical vibrations during real-time operation while maintaining stability across a wide speed range (0-1500 rpm).

• CLASSIFICATION OF RADAR IMAGES OF MULTI-ROTOR UNMANNED AERIAL VEHICLES USING THE YOLO11 ALGORITHM

  V.А. Derkachev

  171-180

  2025-07-24

  Abstract ▼

  This article discusses a classifier of radar images of unmanned aerial vehicles based on a neural network built on the YOLO algorithm version 11. Solving the problem of detecting and classifying unmanned aerial vehicles has become one of the priority tasks at present. The increase in the number of modifications of unmanned aerial vehicles greatly complicates the use of statistical classification methods, which requires the use of new approaches to solving the classification problem. The development of neural network methods, simultaneously with an increase in the performance of computers for training, on the one hand, and embedded solutions, on the other, allows for the classification of aircraft using radar images in real time. The use of the YOLO11 algorithm allows, in addition to determining the class of the target, to estimate the range to the observed object. The use of radar images is justified due to the fact that visual observation is not always possible due to difficult weather conditions and darkness. To train the neural network, it is proposed to use a set of radar images obtained using the author's model of data generation with an arbitrary configuration of unmanned aerial vehicles. The neural network of the Detection YOLO11s class (9.4 million parameters) was trained on a sample of radar images of two classes, a total of 8192. As a result of training, an accuracy of 0.99 was obtained for classification in 2 classes of objects (on test model data). Tests were conducted using natural data taken using the TI IWR1642 millimeter-range radar system, as a result of which error-free classification of objects on a small sample was achieved