تشخیص نفوذ و ناهنجاری¬ها با استفاده از داده کاوی و انتخاب ویژگیها بوسیله الگوریتم PSO
محورهای موضوعی : فناوری اطلاعات و ارتباطاتفریدون رضائی 1 , محمدعلی افشار کاظمی 2 * , محمد علی کرامتی 3
1 - گروه مدیریت فناوری اطلاعات، دانشگاه آزاد اسلامی واحد تهران مرکز
2 - دانشگاه آزاد اسلامی واحد علوم و تحقیقات، گروه مدیریت صنعتی
3 - دانشگاه آزاد اسلامی واحد علوم و تحقیقات، گروه مدیریت صنعتی
کلید واژه: PSO, J48, داده کاوی, حملات سایبری, NLC-KDD,
چکیده مقاله :
امروزه با توجه به پیشرفت فناوری و توسعه استفاده از اینترنت در کسب و کارها و تغییر نوع کسب و کارها از حالت فیزیکی به مجازی و اینترنت، باعث شده است که نوع حملات و ناهنجاری های مرتبط نیز از حالت فیزیکی به حالت مجازی تغییر کند. یعنی بجای دستبرد به یک فروشگاه یا مغازه، افراد با استفاده از حملات سایبری به سایت ها و فروشگاه های مجازی نفوذ کرده و در آنها اخلال ایجاد می کند. آشکارسازی حملات و ناهنجاریها یکی از چالشهای جدید در مسیر پیشبرد تکنولوژی تجارت الکترونیک می باشد. تشخیص ناهنجاری های یک شبکه و فرآیند شناسایی فعالیتهای مخرب در کسب و کارهای تجارت الکترونیک با تجزیه و تحلیل رفتار ترافیک شبکه امکانپذیر است. سیستمهای داده کاوی بطور گسترده ای در سیستمهای تشخیص نفوذ (IDS) برای تشخیص ناهنجاری ها استفاده می شود. کاهش ابعاد ویژگیها نقش بسیار مهمی در تشخیص نفوذ ایفا می کند، زیرا تشخیص ناهنجاری ها از ویژگیهای ترافیک شبکه با ابعاد بالا فرآیندی زمان بری است. انتخاب ویژگیهای درست و مناسب بر سرعت تجزیه و تحلیل و کار پیشنهادی تاثیر می-گذارد و می تواند سرعت تشخیص را بهبود بخشد. در این مقاله با استفاده از الگوریتم های داده کاوی مانند J48 و PSO توانستیم میزان دقت تشخیص ناهنجاری ها و حملات به میزان قابل توجه ای بهبود ببخشیم.
Today, considering technology development, increased use of Internet in businesses, and movement of business types from physical to virtual and internet, attacks and anomalies have also changed from physical to virtual. That is, instead of thieving a store or market, the individuals intrude the websites and virtual markets through cyberattacks and disrupt them. Detection of attacks and anomalies is one of the new challenges in promoting e-commerce technologies. Detecting anomalies of a network and the process of detecting destructive activities in e-commerce can be executed by analyzing the behavior of network traffic. Data mining systems/techniques are used extensively in intrusion detection systems (IDS) in order to detect anomalies. Reducing the size/dimensions of features plays an important role in intrusion detection since detecting anomalies, which are features of network traffic with high dimensions, is a time-consuming process. Choosing suitable and accurate features influences the speed of the proposed task/work analysis, resulting in an improved speed of detection. In this article, by using data mining algorithms such as J48 and PSO, we were able to significantly improve the accuracy of detecting anomalies and attacks.
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