Biblio

Botnets are one of the major threats on the Internet. They are used for malicious activities to compromise the basic network security goals, namely Confidentiality, Integrity, and Availability. For reliable botnet detection and defense, deep learning-based approaches were recently proposed. In this paper, four different deep learning models, namely Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), hybrid CNN-LSTM, and Multi-layer Perception (MLP) are applied for botnet detection and simulation studies are carried out using the CTU-13 botnet traffic dataset. We use several performance metrics such as accuracy, sensitivity, specificity, precision, and F1 score to evaluate the performance of each model on classifying both known and unknown (zero-day) botnet traffic patterns. The results show that our deep learning models can accurately and reliably detect both known and unknown botnet traffic, and show better performance than other deep learning models.

The legacy security defense mechanisms cannot resist where emerging sophisticated threats such as zero-day and malware campaigns have profoundly changed the dimensions of cyber-attacks. Recent studies indicate that cyber threat intelligence plays a crucial role in implementing proactive defense operations. It provides a knowledge-sharing platform that not only increases security awareness and readiness but also enables the collaborative defense to diminish the effectiveness of potential attacks. In this paper, we propose a secure distributed model to facilitate cyber threat intelligence sharing among diverse participants. The proposed model uses blockchain technology to assure tamper-proof record-keeping and smart contracts to guarantee immutable logic. We use an open-source permissioned blockchain platform, Hyperledger Fabric, to implement the blockchain application. We also utilize the flexibility and management capabilities of Software-Defined Networking to be integrated with the proposed sharing platform to enhance defense perspectives against threats in the system. In the end, collaborative DDoS attack mitigation is taken as a case study to demonstrate our approach.

Wikipedia is one of the most popular information platforms on the Internet. The user access pattern to Wikipedia pages depends on their relevance in the current worldwide social discourse. We use publically available statistics about the top-1000 most popular pages on each day to estimate the efficiency of caches for support of the platform. While the data volumes are moderate, the main goal of Wikipedia caches is to reduce access times for page views and edits. We study the impact of most popular pages on the achievable cache hit rate in comparison to Zipf request distributions and we include daily dynamics in popularity.