Recent techniques used to detect botnet activities within the network using netflow data and botnet detection in virtual environments
Tespit etmek için kullanılan son teknikler ağ içindeki botnet aktiviteleri netflow verilerinin ve botnetin kullanılmasısanal ortamlarda tespit
- Tez No: 672686
- Danışmanlar: PROF. DR. OSMAN NURİ UÇAN
- Tez Türü: Yüksek Lisans
- Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2020
- Dil: İngilizce
- Üniversite: Altınbaş Üniversitesi
- Enstitü: Lisansüstü Eğitim Enstitüsü
- Ana Bilim Dalı: Elektrik ve Bilgisayar Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
Botnets, DDoS, Spam, Adware, Clickjacking, Phishing etc. Security experts, at various levels of the botnet network. It works on most Signature-based policies. detection, some anomaly-based detection and some are based on Data Mining techniques. Whereas bot writers are largely encrypted communication. Due to this phenomenon, it is difficult for any traditional. Signature-based detection technique to detect bot activity by applying Deep Pack only Examination of loads (DPI). For this reason, there is a requirement to have the technique. Observe the rather typical network behavior independent of the load analysis. botnets. In this article, I will focus on the latest techniques used to detect botnet activity. In the network using NetFlow data. For this, open both live bots and malicious Datasets (with non-hazardous traffic) to check the strength and limitation of these detections. techniques. This will help us formulate optimal botnet detection techniques. About streaming data.
Özet (Çeviri)
Botnets are popular to carry out malicious activities like DDoS, Spamming, Adware, Clickjacking, Phishing etc. Security experts have developed layered security measures to counter botnet at various levels of the network. Most of them work on the principle of Signature based detection, some Anomaly based detection and some based on Data Mining techniques. Whereas, bot writers are constantly changing their mode of operation by majorly going towards encrypted communication. Because of this phenomenon, it is difficult for any traditional Signature-based detection technique to detect bot activities by applying only Deep Packet Inspection (DPI) of payloads. Hence there is a requirement to have the technique which is independent of payload analysis but rather observe the network behavior that is typical to botnets. In this study, I will be focusing on the recent techniques used to detect botnet activities within the network using NetFlow data. For this, both live bots and also Open malicious Datasets (along with benign traffic) will be used to check the strength and limitation of these detection techniques. This will help us in the formulation of optimum botnet detection techniques based on Flow data.
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