Cyber-attack method and perpetrator prediction using machine learning algorithms

Abstract

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.

Classification and Prediction of Significant Cyber Incidents (SCI) Using Data Mining and Machine Learning (DM-ML)

“…The authors surveyed different DM-ML approaches for malware detection [30], [31]. In another paper, the authors used a Deep learning methodology is used for forecasting cyber-attacks based on the captured data from network traffic [32], [33]. In another paper [34], cyber-attack methods and committers have been predicted using Support Vector Machine (SVM), an ML algorithm.…”

Regresyon Yöntemlerine Dayalı Suç Tespit Analizi Karşılaştırması Elazığ İli Örneği

“…Daha önce yapılan çalışma [18] ile karşılaştırıldığında Lineer, Polinom, Lasso ve Ridge Regresyon modellerinin daha başarısız olduğu yeni modelin yeterince başarı elde edemediği görülmüştür.…”

“…Uygulanan modelde lineer, ridge ve lasso regresyon doğruluk oranlarının çok düşük olduğundan başarısız olduğu tespit edilmiştir. Polinom regresyon yönteminde ise 0.79 R Square doğruluk oranıyla 4 yöntem arasında en başarılı yöntem olduğu görülmüş olsa da daha önce benzer yapılan çalışmaya [18] göre başarı oranı düşük kalmıştır. Yapılan çalışmada polinom regresyon modelinin geliştirilmesi halinde suç analizi ve tahminlerde kullanılabileceği değerlendirilmektedir.…”

“…Yapılan çalışmalarda özellikle suç analizinde ve diğer çalışmalarda regresyon yöntemlerinin başarısı görülmüştür. Suçu analiz ederken diğer makine öğrenmesi yöntemleri de ciddi başarı gösterdiğinden daha önce aynı verilerle yapılan çalışma ile [18] karşılaştırılmıştır. Çalışmada özellikle regresyon yöntemlerinin analizdeki başarısının önceki çalışma ile karşılaştırılması amaçlanmaktadır.…”

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

“…An additional risk regarding a breach of patient confidentiality could derive from so-called "membership inference attacks, " i.e., malicious attacks toward AI algorithms which are aimed at detecting the confidential data used to build the algorithm (Shokri et al, 2017). Actually, the implementation of AI systems means access to sensitive health data, which intrinsically always carries the risk of cyberattacks, posing a substantial risk on the privacy of patients (especially those with lower education and financial income; Bilen and Özer, 2021) and requiring a guaranteed level of robustness against such attacks (Catak et al, 2021;Zhou et al, 2021). Attacks on AI systems can undermine diagnostic accuracy, administer lethal drug doses, or sabotage critical moves in an operation, and in the area of diagnostic imaging, they can manipulate data entering AI systems (so-called "input attacks"), leading to false diagnosis and altered patient care and/or reimbursement (Finlayson et al, 2019;Kiener, 2020;Myers et al, 2020).…”