Intelligent Management and Efficient Operation of Big Data

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources, the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services, and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

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

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

Defending Against Denial of Service

Civil Society currently faces significant cyber threats. At the top of the list of those threats are Denial of Service (DoS) attacks. The websites of many organizations and individuals have already come under such attacks, and the frequency of those attacks are on the rise. Civil Society frequently does not have the kinds of resources or technical know-how that is available to commercial enterprise and government websites, and often have to exist in adverse political environments where every avenue available, both legal and illegal, is used against them. Therefore, the threat of DoS attacks is unlikely to go away any time soon.A Denial of Service (DoS) attack is any attack that overwhelms a website, causing the content normally provided by that website to no longer be available to regular visitors of the website. Distributed Denial of Service (DDoS) attacks are traffic volumebased attacks originating from a large number of computers, which are usually compromised workstations. These workstations, known as 'zombies', form a widely distributed attack network called a 'botnet'. While many modern Denial of Service attacks are Distributed Denial of Service attacks, this is certainly not true for all denials of service experienced by websites. Therefore, when users first start experiencing difficulty in getting to the website content, it should not be assumed that the site is under a DDoS attack. Many forms of DoS are far easier to implement than DDoS, and so these attacks are still used by parties with malicious intent. Many such DoS attacks are easier to defend against once the mechanism used to cause the denial of service is known. Therefore, it is paramount to do proper analysis of attack traffic when a site becomes unable to perform its normal function. There are two parts to this guide. The first part outlines preparatory steps that can be taken by Civil Society organizations to improve their website's resilience, should it come under attack. However, we do understand that most Civil Society organizations' first introduction to DoS attacks comes when they suddenly find themselves the victim of an attack. The second part of this guide provides a step-by-step process to assist the staff of NGOs to efficiently deal with that stressful situation