Botnets for scalable management

International audienceWith an increasing number of devices that must be managed, the scalability of network and service management is a real challenge. A similar challenge seems to be solved by botnets which are the major security threats in today's Internet where a botmaster can control several thousands of computers around the world. This is done although many hindernesses like firewalls, intrusion detection systems and other deployed security appliances to protect current networks. From a technical point of view, such an efficiency can be a benefit for network and service management. This paper describes a new management middleware based on botnets, evaluates its performances and shows its potential impact based on a parametric analytical model

PeerHunter: Detecting Peer-to-Peer Botnets through Community Behavior Analysis

Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few existing work claimed to detect traditional botnets effectively, the problem of detecting P2P botnets involves more challenges. In this paper, we present PeerHunter, a community behavior analysis based method, which is capable of detecting botnets that communicate via a P2P structure. PeerHunter starts from a P2P hosts detection component. Then, it uses mutual contacts as the main feature to cluster bots into communities. Finally, it uses community behavior analysis to detect potential botnet communities and further identify bot candidates. Through extensive experiments with real and simulated network traces, PeerHunter can achieve very high detection rate and low false positives.Comment: 8 pages, 2 figures, 11 tables, 2017 IEEE Conference on Dependable and Secure Computin

Management and Security of IoT systems using Microservices

Devices that assist the user with some task or help them to make an informed decision are called smart devices. A network of such devices connected to internet are collectively called as Internet of Things (IoT). The applications of IoT are expanding exponentially and are becoming a part of our day to day lives. The rise of IoT led to new security and management issues. In this project, we propose a solution for some major problems faced by the IoT devices, including the problem of complexity due to heterogeneous platforms and the lack of IoT device monitoring for security and fault tolerance. We aim to solve the above issues in a microservice architecture. We build a data pipeline for IoT devices to send data through a messaging platform Kafka and monitor the devices using the collected data by making real time dashboards and a machine learning model to give better insights of the data. For proof of concept, we test the proposed solution on a heterogeneous cluster, including Raspberry Pi’s and IoT devices from different vendors. We validate our design by presenting some simple experimental results

Scalable Detection and Isolation of Phishing

This paper presents a proposal for scalable detection and isolation of phishing. The main ideas are to move the protection from end users towards the network provider and to employ the novel bad neighborhood concept, in order to detect and isolate both phishing e-mail senders and phishing web servers. In addition, we propose to develop a self-management architecture that enables ISPs to protect their users against phishing attacks, and explain how this architecture could be evaluated. This proposal is the result of half a year of research work at the University of Twente (UT), and it is aimed at a Ph.D. thesis in 2012

Report of the Third Workshop on the Usage of NetFlow/IPFIX in Network Management

The Network Management Research Group (NMRG) organized in 2010 the Third Workshop on the Usage of NetFlow/IPFIX in Network Management, as part of the 78th IETF Meeting in Maastricht. Yearly organized since 2007, the workshop is an opportunity for people from both academia and industry to discuss the latest developments of the protocol, possibilities for new applications, and practical experiences. This report summarizes the presentations and the main conclusions of the workshop

On the Efficacy of Live DDoS Detection with Hadoop

Distributed Denial of Service flooding attacks are one of the biggest challenges to the availability of online services today. These DDoS attacks overwhelm the victim with huge volume of traffic and render it incapable of performing normal communication or crashes it completely. If there are delays in detecting the flooding attacks, nothing much can be done except to manually disconnect the victim and fix the problem. With the rapid increase of DDoS volume and frequency, the current DDoS detection technologies are challenged to deal with huge attack volume in reasonable and affordable response time. In this paper, we propose HADEC, a Hadoop based Live DDoS Detection framework to tackle efficient analysis of flooding attacks by harnessing MapReduce and HDFS. We implemented a counter-based DDoS detection algorithm for four major flooding attacks (TCP-SYN, HTTP GET, UDP and ICMP) in MapReduce, consisting of map and reduce functions. We deployed a testbed to evaluate the performance of HADEC framework for live DDoS detection. Based on the experiments we showed that HADEC is capable of processing and detecting DDoS attacks in affordable time