A fully scalable big data framework for Botnet detection based on network traffic analysis

Real-time botnet detection on large network bandwidths using machine learning

Botnets are one of the most harmful cyberthreats, that can perform many types of cyberattacks and cause billionaire losses to the global economy. Nowadays, vast amounts of network traffic are generated every second, hence manual analysis is impossible. To be effective, automatic botnet detection should be done as fast as possible, but carrying this out is difficult in large bandwidths. To handle this problem, we propose an approach that is capable of carrying out an ultra-fast network analysis (i.e. on windows of one second), without a significant loss in the F1-score. We compared our model with other three literature proposals, and achieved the best performance: an F1 score of 0.926 with a processing time of 0.007 ms per sample. We also assessed the robustness of our model on saturated networks and on large bandwidths. In particular, our model is capable of working on networks with a saturation of 10% of packet loss, and we estimated the number of CPU cores needed to analyze traffic on three bandwidth sizes. Our results suggest that using commercial-grade cores of 2.4 GHz, our approach would only need four cores for bandwidths of 100 Mbps and 1 Gbps, and 19 cores on 10 Gbps networks.

OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure

Network Intrusion Detection Systems (NIDS) and firewalls are the de facto solutions in the modern cloud to detect cyberattacks and minimize potential hazards for tenant networks. Most of the existing firewalls, perimeter security, and middlebox solutions are built on static rules/signatures or simple rule matching, making them inflexible, susceptible to bugs, and difficult to introduce new services. This paper aims to improve network management in OpenStack Clouds by taking advantage of the combination of software-defined networking (SDN), Network Function Virtualization (NFV), and machine learning/artificial intelligence (ML/AI) and for making networks more predictable, reliable, and secure. Artificial intelligence is being used to monitor the behavior of the virtual machines and applications running in the OpenStack SDN cloud so that when any issues or degradations are noticed, the decision can be quickly made on how to handle that issue, being able to analyze data in motion, starting at the edge. The OpenStackDP framework comprises lightweight monitoring, anomaly-detecting intelligent sensors embedded in the data plane, a threat analytics engine based on ML/AI algorithms running inside switch hardware/network co-processor, and defensive actions deployed as virtual network functions (VNFs). This network data plane-based architecture makes high-speed threat detection and rapid response possible and enables a much higher degree of security. We have built the framework with advanced streaming analytics technologies, algorithms, and machine learning to draw knowledge from this data that is in motion before the malicious traffic goes to the tenant compute nodes or long-term data store. Cloud providers and users will benefit from improved Quality-of-Services (QoS) and faster recovery from cyber-attacks and compromised switches. The multi-phase collaborative anomaly detection scheme demonstrates an accuracy of 99.81%, average latencies of 0.27 ms, and response speed within 9 s. The simulations and analysis show that the OpenStackDP network analytics framework substantially secures and outperforms prior SDN-based OpenStack solutions for Cloud architectures.

Artificial intelligence-based network traffic analysis and automatic optimization technology

Network operation and maintenance (O & M) activities of data centers focus mainly on checking the operating states of devices. O & M engineers determine how services are running and the bearing capacity of a data center by checking the operating states of devices. However, this method cannot reflect the real transmission status of business data; therefore, engineers cannot fully comprehensively perceive the overall running conditions of businesses. In this paper, ERSPAN (Encapsulated Remote Switch Port Analyzer) technology is applied to deliver stream matching rules in the forwarding path of TCP packets and mirror the TCP packets into the network O & M AI collector, which is used to conduct an in-depth analysis on the TCP packets, collect traffic statistics, recapture the forwarding path, carry out delayed computing, and identify applications. This enables O & M engineers to comprehensively perceive the service bearing status in a data center, and form a tightly coupled correlation model between networks and services through end-to-end visualized modeling, providing comprehensive technical support for data center optimization and early warning of network risks.

A Novel Video Steganography-Based Botnet Communication Model in Telegram SNS Messenger

In botnets, a bot master regularly sends command and control messages (C & C messages) to bots for various purposes, such as ordering its commands to bots and collecting critical data from bots. Although such C & C messages can be encrypted by cryptographic methods to hide them, existing botnet detection mechanisms could detect the existence of botnets by capturing suspicious network traffics between the bot master (or the C & C server) and numerous bots. Recently, steganography-based botnets (stego-botnets) have emerged to make C & C communication traffics look normal to botnet detection systems. In stego-botnets, every C & C message is embedded in a multimedia file, such as an image file by using steganography techniques and shared in Social Network Service (SNS) websites (such as Facebook) or online messengers (such as WeChat or KakaoTalk). Consequently, traditional botnet detection systems without steganography detection methods cannot detect them. Meanwhile, according to our survey, we observed that existing studies on the steganography botnet are limited to use only image steganography techniques, although the video steganography method has some obvious advantages over the image steganography method. By this motivation, in this paper, we study a video steganography-based botnet in Social Network Service (SNS) platforms. We first propose a video steganography botnet model based on SNS messengers. In addition, we design a new payload approach-based video steganography method (DECM: Divide-Embed-Component Method) that can embed much more secret data than existing tools by using two open tools VirtualDub and Stegano. We show that our proposed model can be implemented in the Telegram SNS messenger and conduct extensive experiments by comparing our proposed model with DECM with an existing image steganography-based botnet in terms of C & C communication efficiency and undetectability.