جستجوی مقالات مرتبط با کلیدواژه "support vector machines" در نشریات گروه "پدافند غیرعامل"

Intrusion detection is a classification problem in which various machine learning (ML) and data mining (DM) techniques are used to classify network data in normal traffic and attack. In addition, the types of network attacks have changed over the years. This paper tries to compare two models of intrusion detection systems, which include adaptive neuro-fuzzy inference systems (ANFIS) and support vector machines (SVM). In addition, it examines and evaluates several instances of data sets related to intrusion detection systems. In the following, a new hybrid method is proposed that uses Particle Swarm Optimization (PSO) to create a classifier combination to provide better accuracy for intrusion detection. Experimental results show that the new method can produce a better performance based on different evaluation criteria. This paper lists the different datasets for evaluating the IDS model and discusses the performance of the proposed hybrid method on the IDS datasets that can be used to efficiently and effectively use the datasets to develop IDS based on ML and DM.

Given the inefficiency of static analysis methods due to malware techniques such as code polymorphism, metamorphism, and obfuscation, and self-modifying code, leveraging dynamic and heuristic analysis   methods that are based on the analysis of runtime behavior of malwares, have become particularly important. Environment-aware malware that attempts to conceal its malicious behavior through dynamic anti-analysis methods has caused problems for dynamic analysis detection methods in practice. The purpose of this study is to present an effective method for environment-aware malware detection. Regarding to split–personality of such malware behaviors, this research has proposed an effective way to detect environment-aware malware. This method is based on system call monitoring of malicious and benign  samples under the two NtTrace and drstrace softwares with different monitoring techniques and calculating behavioral distances as training data to create a Support Vector Machine model. Finally, the resulted    support vector machine classifier is used to detect this type of malware with an average precision, recall and accuracy up to 100%, whereas the evaluation of previous related work shows an average precision, recall and accuracy 96.85%, 95.68% and 96.12%, respectively.

This article proposes a method to classify digital modulation type from ASK, PSK and FSK groups, without prior information of received signal. This method has two steps and is called "clustering method". In the first step, the modulation group, and in the second step, modulation order is determined. Using clustering method, the least pre-processing, the most effective separation characteristics and appropriate classifiers was applied to each group and genetic algorithm has been used to improve system performance. The advantages of this method are high accuracy of identification, simplicity, blind identification and more modulation candidates compared to other similar methods.