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A survey of intrusion detection techniques in Cloud
Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks
A Security Monitoring Framework For Virtualization Based HEP Infrastructures
High Energy Physics (HEP) distributed computing infrastructures require
automatic tools to monitor, analyze and react to potential security incidents.
These tools should collect and inspect data such as resource consumption, logs
and sequence of system calls for detecting anomalies that indicate the presence
of a malicious agent. They should also be able to perform automated reactions
to attacks without administrator intervention. We describe a novel framework
that accomplishes these requirements, with a proof of concept implementation
for the ALICE experiment at CERN. We show how we achieve a fully virtualized
environment that improves the security by isolating services and Jobs without a
significant performance impact. We also describe a collected dataset for
Machine Learning based Intrusion Prevention and Detection Systems on Grid
computing. This dataset is composed of resource consumption measurements (such
as CPU, RAM and network traffic), logfiles from operating system services, and
system call data collected from production Jobs running in an ALICE Grid test
site and a big set of malware. This malware was collected from security
research sites. Based on this dataset, we will proceed to develop Machine
Learning algorithms able to detect malicious Jobs.Comment: Proceedings of the 22nd International Conference on Computing in High
Energy and Nuclear Physics, CHEP 2016, 10-14 October 2016, San Francisco.
Submitted to Journal of Physics: Conference Series (JPCS
The cyber security learning and research environment
This report outlines the design and configuration of the Cyber Security Learning and Research Environment (CLARE). It explains how such a system can be implemented with minimal hardware either on a single machine or across multiple machines. Moreover, details of the design of the components that constitute the environment are provided alongside sufficient implementation and configuration documentation to allow for replication of the environment
Machine Learning DDoS Detection for Consumer Internet of Things Devices
An increasing number of Internet of Things (IoT) devices are connecting to
the Internet, yet many of these devices are fundamentally insecure, exposing
the Internet to a variety of attacks. Botnets such as Mirai have used insecure
consumer IoT devices to conduct distributed denial of service (DDoS) attacks on
critical Internet infrastructure. This motivates the development of new
techniques to automatically detect consumer IoT attack traffic. In this paper,
we demonstrate that using IoT-specific network behaviors (e.g. limited number
of endpoints and regular time intervals between packets) to inform feature
selection can result in high accuracy DDoS detection in IoT network traffic
with a variety of machine learning algorithms, including neural networks. These
results indicate that home gateway routers or other network middleboxes could
automatically detect local IoT device sources of DDoS attacks using low-cost
machine learning algorithms and traffic data that is flow-based and
protocol-agnostic.Comment: 7 pages, 3 figures, 3 tables, appears in the 2018 Workshop on Deep
Learning and Security (DLS '18
CyberGuarder: a virtualization security assurance architecture for green cloud computing
Cloud Computing, Green Computing, Virtualization, Virtual Security Appliance, Security Isolation
Container-based network function virtualization for software-defined networks
Today's enterprise networks almost ubiquitously deploy middlebox services to improve in-network security and performance. Although virtualization of middleboxes attracts a significant attention, studies show that such implementations are still proprietary and deployed in a static manner at the boundaries of organisations, hindering open innovation. In this paper, we present an open framework to create, deploy and manage virtual network functions (NF)s in OpenFlow-enabled networks. We exploit container-based NFs to achieve low performance overhead, fast deployment and high reusability missing from today's NFV deployments. Through an SDN northbound API, NFs can be instantiated, traffic can be steered through the desired policy chain and applications can raise notifications. We demonstrate the systems operation through the development of exemplar NFs from common Operating System utility binaries, and we show that container-based NFV improves function instantiation time by up to 68% over existing hypervisor-based alternatives, and scales to one hundred co-located NFs while incurring sub-millisecond latency
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