3,367 research outputs found
LAMP: Prompt Layer 7 Attack Mitigation with Programmable Data Planes
While there are various methods to detect application layer attacks or
intrusion attempts on an individual end host, it is not efficient to provide
all end hosts in the network with heavy-duty defense systems or software
firewalls. In this work, we leverage a new concept of programmable data planes,
to directly react on alerts raised by a victim and prevent further attacks on
the whole network by blocking the attack at the network edge. We call our
design LAMP, Layer 7 Attack Mitigation with Programmable data planes. We
implemented LAMP using the P4 data plane programming language and evaluated its
effectiveness and efficiency in the Behavioral Model (bmv2) environment
The Challenges in SDN/ML Based Network Security : A Survey
Machine Learning is gaining popularity in the network security domain as many
more network-enabled devices get connected, as malicious activities become
stealthier, and as new technologies like Software Defined Networking (SDN)
emerge. Sitting at the application layer and communicating with the control
layer, machine learning based SDN security models exercise a huge influence on
the routing/switching of the entire SDN. Compromising the models is
consequently a very desirable goal. Previous surveys have been done on either
adversarial machine learning or the general vulnerabilities of SDNs but not
both. Through examination of the latest ML-based SDN security applications and
a good look at ML/SDN specific vulnerabilities accompanied by common attack
methods on ML, this paper serves as a unique survey, making a case for more
secure development processes of ML-based SDN security applications.Comment: 8 pages. arXiv admin note: substantial text overlap with
arXiv:1705.0056
A Cognitive Framework to Secure Smart Cities
The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms
DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments
With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST
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