2,618 research outputs found
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
Security and Privacy Issues in Wireless Mesh Networks: A Survey
This book chapter identifies various security threats in wireless mesh
network (WMN). Keeping in mind the critical requirement of security and user
privacy in WMNs, this chapter provides a comprehensive overview of various
possible attacks on different layers of the communication protocol stack for
WMNs and their corresponding defense mechanisms. First, it identifies the
security vulnerabilities in the physical, link, network, transport, application
layers. Furthermore, various possible attacks on the key management protocols,
user authentication and access control protocols, and user privacy preservation
protocols are presented. After enumerating various possible attacks, the
chapter provides a detailed discussion on various existing security mechanisms
and protocols to defend against and wherever possible prevent the possible
attacks. Comparative analyses are also presented on the security schemes with
regards to the cryptographic schemes used, key management strategies deployed,
use of any trusted third party, computation and communication overhead involved
etc. The chapter then presents a brief discussion on various trust management
approaches for WMNs since trust and reputation-based schemes are increasingly
becoming popular for enforcing security in wireless networks. A number of open
problems in security and privacy issues for WMNs are subsequently discussed
before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the
author's previous submission in arXiv submission: arXiv:1102.1226. There are
some text overlaps with the previous submissio
Towards Loop-Free Forwarding of Anonymous Internet Datagrams that Enforce Provenance
The way in which addressing and forwarding are implemented in the Internet
constitutes one of its biggest privacy and security challenges. The fact that
source addresses in Internet datagrams cannot be trusted makes the IP Internet
inherently vulnerable to DoS and DDoS attacks. The Internet forwarding plane is
open to attacks to the privacy of datagram sources, because source addresses in
Internet datagrams have global scope. The fact an Internet datagrams are
forwarded based solely on the destination addresses stated in datagram headers
and the next hops stored in the forwarding information bases (FIB) of relaying
routers allows Internet datagrams to traverse loops, which wastes resources and
leaves the Internet open to further attacks. We introduce PEAR (Provenance
Enforcement through Addressing and Routing), a new approach for addressing and
forwarding of Internet datagrams that enables anonymous forwarding of Internet
datagrams, eliminates many of the existing DDoS attacks on the IP Internet, and
prevents Internet datagrams from looping, even in the presence of routing-table
loops.Comment: Proceedings of IEEE Globecom 2016, 4-8 December 2016, Washington,
D.C., US
- …