Bilen Abdulkadir, Özer Ahmet Bedri
Criminal Department, General Directorate of Security, Ankara, Turkey.
Department of Computer Engineering, Firat University, Elazığ, Turkey.
PeerJ Comput Sci. 2021 Apr 9;7:e475. doi: 10.7717/peerj-cs.475. eCollection 2021.
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.
网络攻击已成为全球最大的问题之一。它们每天都给国家和人民造成严重的经济损失。网络攻击的增加还带来了网络犯罪。打击犯罪和罪犯的关键因素是识别网络犯罪的实施者并了解攻击方法。检测和避免网络攻击是艰巨的任务。然而，研究人员最近通过开发安全模型和利用人工智能方法进行预测来解决这些问题。文献中存在大量犯罪预测方法。另一方面，它们在预测网络犯罪和网络攻击方法方面存在不足。通过使用实际数据识别攻击及其实施者，可以解决这个问题。这些数据包括犯罪类型、犯罪者性别、损失和攻击方法。这些数据可以从遭受网络攻击的人员向法医单位提交的申请中获取。在本文中，我们使用机器学习方法在两种不同模型中分析网络犯罪，并预测所定义特征对检测网络攻击方法和犯罪者的影响。我们在方法中使用了八种机器学习方法，得出它们的准确率相近。发现支持向量机线性模型在网络攻击方法方面最为成功，准确率为95.02%。在第一个模型中，我们能够高精度地预测受害者可能遭受的攻击类型。逻辑回归在检测攻击者方面是领先方法，准确率为65.42%。在第二个模型中，我们通过比较犯罪者的特征来预测是否能够识别他们。我们的结果表明，随着受害者教育水平和收入水平的提高，网络攻击的可能性会降低。我们相信网络犯罪单位将使用所提出的模型。它还将便于检测网络攻击，并使打击这些攻击的工作更加轻松有效。
