بهبود عملکرد سیستم‌های تشخیص حملات فیشینگ مبتنی بر هم‌افزایی شبکه عصبی و الگوریتم علی بابا و چهل دزد
محورهای موضوعی : فناوری اطلاعات
رضا عصاره
1
(استادیار، گروه مهندسی کامپیوتر، واحد یادگار امام خمینی (ره) شهر ری، دانشگاه آزاد اسلامی، شهر ری، ایران.)
یونس مباشری
2
()
کلید واژه: حملات سایبری, حملات فیشینگ, یادگیری ماشین, شبکه عصبی پرسپترون چند لایه, الگوریتم علی بابا و چهل دزد,
چکیده مقاله :
یکی از حملات سایبری، حملات فیشینگ است که در سال‌های اخیر به‌سرعت افزایش یافته‌اند. استفاده از یادگیری ماشین برای آموزش سیستمی که پیام‌های فیشینگ را تشخیص می‌دهد، برای افزایش سطح امنیت در برابر حملات سایبری ضروری است. با استفاده از یافتن وزن و بایاس‌های شبکه عصبی از طریق الگوریتم علی بابا و چهل دزد می‌توان صفحات فیشینگ را با دقت بالایی شناسایی کرد. در روش پیشنهادی برای دسته‌بندی و تشخیص حملات فیشینگ از شبکه عصبی پرسپترون چند لایه استفاده می‌شود. وزن‌های شبکه عصبی پرسپترون چند لایه از طریق الگوریتم علی بابا و چهل دزد پیدا می‌شوند. نکته مهم، انتخاب روشی است که تابع هزینه با آن محاسبه شود که شامل 'MSE'، 'RMSE' و 'Accuracy' هستند. شبیه‌سازی روش پیشنهادی از طریق نرم‌افزار متلب انجام شده است. در مجموعه داده، ویژگی‌های مختلف مربوط به وب‌سایت‌های قانونی و فیشی را شناسایی کرده و ۱۳۵۳ وب‌سایت مختلف از منابع مختلف جمع‌آوری شده است. نتایج روش پیشنهادی با طرح پایه از نظر دقت، صحت، F1_Score و منحنی AUC-ROC مقایسه می‌شوند. مطابق با نتایج به دست آمده، دقت روش پیشنهادی نسبت به روش LR به میزان 4.91 درصد، نسبت به روش ماشین بردار پشتیبان به میزان 5.7 درصد، نسبت به روش K نزدیک‌ترین همسایه به میزان 3.72 درصد، نسبت به روش AdaBoost به میزان 9.03 درصد، نسبت به روش پرسپترون چند لایه به میزان 3.53 درصد، نسبت به روش J48 به میزان 2.46 درصد و نسبت به روش جنگل تصادفی به میزان 0.74 درصد بهبود داشته است. همچنین روش پیشنهادی نسبت به روش‌های ترکیبی الگوریتم‌های فراابتکاری و شبکه عصبی نیز بهبود داشته است. دقت روش پیشنهادی نسبت به روش EPO – ANN به میزان 1.3 درصد و همچنین نسبت به روش SSA – ANN به میزان 1.41 درصد بهبود داشته است.
One of the cyber attacks is phishing attacks, which have increased rapidly in recent years. Defining robust, efficient and up-to-date methods for phishing detection is essential. Using machine learning to train a system that recognizes phishing messages is essential to increase the level of security against cyber attacks. By finding the weights and biases of the neural network through the algorithm of ali baba and the forty thieves, it is possible to identify phishing pages with high accuracy. Multi-layer perceptron neural network is used in the proposed method to classify and detect phishing attacks. The weights of the multi-layer perceptron neural network are found through the algorithm of ali baba and the forty thieves. The important thing is to choose the method with which the cost function is calculated, which includes 'mse', 'rmse' and 'accuracy'. The simulation of the proposed method has been done through matlab software. In the dataset, different features related to legal and phishing websites have been identified and 1353 different websites have been collected from different sources. The results of the proposed method are compared with the basic design in terms of precision, accuracy, F1_Score and AUC-ROC curve. According to the obtained results, the accuracy of the proposed method is 4.91% compared to the LR method, 5.7% compared to the support vector machine method, 3.72% compared to the K-nearest neighbor method, and 9.03% compared to the adaboost method. Compared to the multilayer perceptron method, it has improved by 3.53%, compared to the J48 method by 2.46%, and compared to the random forest method by 0.74%. Also, the proposed method has improved compared to the combined methods of meta-heuristic algorithms and neural network. The accuracy of the proposed method has improved by 1.3% compared to the EPO-ANN method and by 1.41% compared to the SSA-ANN method.
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