Private companies and government have decided to provide Radio Frequency Identification (RFID) systems in places of free access to the population. However, as a result of such exposition, these systems become vulnerable to Denial of Service (DoS) attacks that can cause unavailability in the services. Furthermore, another complicating factor in that context regards the equipment or devices used on RFID systems, since they operate in frequency bands reserved for secondary communication systems, and consequently they have too few computational resources to deal with malicious electromagnetic interferences. Therefore, in this paper an Intrusion Detection System (IDS) is proposed to identify the occurrence of the malicious behavior and to notify the system operator about one specific type of electromagnetic interference in RFID systems -the Jamming attack. The obtained results of the system show the jamming attacks are detected prior of having a significant impact on the communication between the RFID reader and the tag -i.e., the jamming attack was detected even with insufficient power to impact on communication.
Distributed Denial of Service (DDoS) attacks impose a major challenge for today's security systems, given the variety of its implementations and the scale that the attacks can achieve. One approach for their early detection is the use of Machine Learning (ML) techniques, which create rules for classifying traffic from historical data. However, different types of data contribute unequally to the assertiveness of the trained model. The use of Feature Selection (FS) techniques as a pre-processing step allows identification of the most relevant features for the problem in question. This action reduces training time and can even improve performance when noisy variables are eliminated. The current work is based on a public dataset and the XGBoost algorithm to measure the impact of FS techniques on the DDoS attack classification problem. We consider both techniques independent of the sample labels, as well as methods that use this information to rank the variables in order of importance. We analyzed the problem from the point of view of Binary and Multiclass classification. We also created a benchmark of classification metrics and execution times. Our comparisons involved the Accuracy, Precision, Recall, and F1 Score metrics for different FS methods, in addition to training and execution time. In the results it is possible to verify for both the Binary (78% reduction of the features) and Multiclass classifiers (60% reduction of the features), that the ANOVA method proved to be the most beneficial.
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