RESUMO
Cyber-attacks have become one of the biggest problems of the world. They cause serious financial damages to countries and people every day. The increase in cyber-attacks also brings along cyber-crime. The key factors in the fight against crime and criminals are identifying the perpetrators of cyber-crime and understanding the methods of attack. Detecting and avoiding cyber-attacks are difficult tasks. However, researchers have recently been solving these problems by developing security models and making predictions through artificial intelligence methods. A high number of methods of crime prediction are available in the literature. On the other hand, they suffer from a deficiency in predicting cyber-crime and cyber-attack methods. This problem can be tackled by identifying an attack and the perpetrator of such attack, using actual data. The data include the type of crime, gender of perpetrator, damage and methods of attack. The data can be acquired from the applications of the persons who were exposed to cyber-attacks to the forensic units. In this paper, we analyze cyber-crimes in two different models with machine-learning methods and predict the effect of the defined features on the detection of the cyber-attack method and the perpetrator. We used eight machine-learning methods in our approach and concluded that their accuracy ratios were close. The Support Vector Machine Linear was found out to be the most successful in the cyber-attack method, with an accuracy rate of 95.02%. In the first model, we could predict the types of attacks that the victims were likely to be exposed to with a high accuracy. The Logistic Regression was the leading method in detecting attackers with an accuracy rate of 65.42%. In the second model, we predicted whether the perpetrators could be identified by comparing their characteristics. Our results have revealed that the probability of cyber-attack decreases as the education and income level of victim increases. We believe that cyber-crime units will use the proposed model. It will also facilitate the detection of cyber-attacks and make the fight against these attacks easier and more effective.
RESUMO
INTRODUCTION: It is known that hyperlipidemia reduces hearing functions. In this study, we aimed to study the effect of antihyperlipidemic drugs on hearing functions and tinnitus. METHODS: Eighty-four patients aged 18 to 84, who were diagnosed with hyperlipidemia and started treatment with the statin group (atorvastatin 20 mg and 40 mg, rosuvastatin 10 mg and 20 mg, and simvastatin 20 mg) of antihyperlipidemic drugs, were included in this study. All patients underwent pure-tone audiometry before starting treatment with antihyperlipidemic drugs. Patients with tinnitus were evaluated by Tinnitus Severity Index and Visual Analogue Scale. In the 6th month of therapy, otologic examination, pure-tone audiometry and tinnitus evaluation of the patients were repeated. RESULTS: No significant difference was found in the pure-tone averages of the patients before and after statin use (p > 0.05). However, it was found in the audiometry that, after statin use, all drugs caused to statistically significant decrease in the hearing thresholds at 6000 Hertz (p < 0.05). Also, a strong increase was found in the Speech Discrimination percentages after treatment in patients using rosuvastatin 10 mg (p = 0.022). A significant decrease was found in the tinnitus frequency, duration, severity and degree of annoyance in patients using rosuvastatin 10 mg and 20 mg (p < 0.05). CONCLUSION: Statin group of drugs can have a positive effect on the hearing functions and subjective tinnitus. In particular, it is seen that rosuvastatin group of statins has a more notable effect on tinnitus. It was considered that further studies with larger patient groups are needed.
