Various machine learning algorithms have been applied to network intrusion classification problems, including both binary and multi-class classifications. Despite the existence of numerous studies involving unbalanced network intrusion datasets, such as CIC-IDS2017, a prevalent approach is to address the issue by either merging the classes to optimize their numbers or retaining only the most dominant ones. However, there is no consistent trend showing that accuracy always decreases as the number of classes increases. Furthermore, it is essential for cybersecurity practitioners to recognize the specific type of attack and comprehend the causal factors that contribute to the resulting outcomes. This study focuses on tackling the challenges associated with evaluating the performance of multi-class classification for network intrusions using highly imbalanced raw data that encompasses the CIC-IDS2017 and CSE-CIC-IDS2018 datasets. The research concentrates on investigating diverse machine learning (ML) models, including Logistic Regression, Random Forest, Decision Trees, CNNs, and Artificial Neural Networks. Additionally, it explores the utilization of explainable AI (XAI) methods to interpret the obtained results. The results obtained indicated that decision trees using the CART algorithm performed best on the 28-class classification task, with an average macro F1-score of 0.96878.
Spartus išmaniųjų įrenginių skaičiaus ir juose saugomų duomenų kiekio ir jautrumo augimas lemia taip pat augančias ir duomenų saugumo rizikas. Šias rizikas siekia sumažinti operacinių sistemų kūrėjai, periodiškai išleisdami saugos atnaujinimus, tačiau yra nustatyta, kad pavojingiausios aplikacijos gali būti įdiegiamos kartu su šiais operacinės sistemos ar saugos OTA (angl. over-theair) atnaujinimais – apie 5% įrenginių gamintojų įdiegtų aplikacijų yra kenkėjiškos. Taip pat apsaugos priemonės yra taikomos ir šiems mobiliems įrenginiams pritaikytoms infrastruktūroms – elektroninėms aplikacijų parduotuvėms, tačiau 67% kenkėjiškų aplikacijų vartotojus pasiekia būtent per jas. Siekiant atliepti saugumo rizikų mažinimo poreikį, šiame darbe yra pasiūlytas naujas IDS/IPS technologijomis grįsto mobiliųjų įrenginių atakų prevencijos metodas, paruošta eksperimentinė metodo realizacija ir atliktas šio metodo tyrimas.
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