Siber Saldırılara Karşı Kullanılan Makine Öğrenme Yöntemlerinin Web Uygulamalarında Güvenlik Etkinliğinin Ölçümü

Küresel dünyadaki teknolojik gelişmelerin son zamanlarda hızlı ilerlemesi, kitlelerin hızlı bir şekilde bu gelişmeleri yakından takip etmesi ve paylaşımlarda bulunması siber suçluların odak noktası haline gelmiştir. İnsanlar temel ihtiyaçlarını, isteklerini, paylaşımlarını veya çalışmalarını akıllı cihazlar üzerinden internet alt yapısını kullanarak gerçekleştirmektedirler. Bu eylemleri kullanıcılar gerçekleştirirken web uygulamalar üzerinden ister istemez bir açık kapı bırakabilmektedir. Neticesinde kullanıcıya özel tanımlanmış bilgiler başkalarının eline kolayca geçebilmektedir. Son zamanlarda web siteleri üzerinden gerçekleştirilen faaliyetlerde ciddi artış olmuştur. Bu artışın sebeplerinden biri ve en önemlisi ise dünya genelinde etkisini göstermiş olan pandemi sürecidir. Siber suçlular bu gibi durumları fırsata çevirmek ve maddi kazanç sağlamak isterler. İnsanların yoğun talepte bulunduğu web sitelerine yönelik açıklar ararlar ve onların kullanıcı bilgilerine, kart bilgilerine erişmek isterler. Bu çalışma çeşitli web sitelerinin güvenlik açıklarına karşı makine öğrenme yöntemlerinin performansını ölçen bir yaklaşım önermektedir. Çalışmada kullanılan veri kümesi 1000 adet web sitesinin parametre özelliklerinden oluşmaktadır. Çalışmanın deneysel analizlerinde; Çok Katmanlı Algılayıcı, Destek Vektör Makineleri, Karar Ağaçları, Naif Bayes, Rastgele Orman yöntemleri kullanıldı. Makine öğrenme yöntemlerinden elde edilen genel doğruluk başarıları sırasıyla; %74, %74, %100, %69,5 ve %100'dü. Deneysel analizler siber saldırılarının tespitinde makine öğrenme yöntemlerinin etkin olduğunu göstermiştir.

Anahtar Kelimeler:

Web güvenliği, makine öğrenme, siber saldırı, siber güvenlik, yapay zekâ

Measuring the Security Effectiveness of Machine Learning Methods Used Against Cyber Attacks in Web Applications

The rapid progress of technological developments in the global world, the people to closely follow these developments and share them have become the focus of cybercriminals. People realize their basic needs, requests, shares or works via smart devices using the internet infrastructure. While performing these actions, users can inevitably leave an open door through web applications. As a result, user-defined information can easily be passed on to others. Recently, there has been a serious increase in activities carried out on websites. One of the reasons for this increase, and the most important one, is the pandemic that has had an impact worldwide. Cybercriminals want to turn such situations into opportunities and gain financial gain. They look for vulnerabilities in the websites that people demand heavily and they want to access their user information and card information. This study proposes an approach that measures the performance of machine learning methods against the vulnerabilities of various websites. The data set used in the study consists of parameter properties of 1000 websites. In the experimental analysis of the study; Multilayer Perceptron, Support Vector Machines, Decision Trees, Naive Bayesian, Random Forest methods were used. The general accuracy achievements obtained from machine learning methods are; it was 74%, 73.7%, 100%, 69.5% and 100%, respectively. Experimental analysis has shown that machine learning methods are effective in detecting cyber attacks.

Keywords:

Web security, machine learning, cyber attack, cyber security, artificial intelligence,

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