107,411 research outputs found
Recommended from our members
Passive security threats and consequences in IEEE 802.11 wireless mesh networks
The Wireless Mesh Network (WMN) is ubiquitous emerging broadband wireless network. However, the open wireless medium, multi-hop multi-radio architecture and ad-hoc connectivity amongst end-users are such characteristics which increases the vulnerabilities of WMN towards many passive and active attacks. A secure network ensures the confidentiality, integrity and availability of wireless network. Integrity and availability is compromised by active attacks, while the confidentiality of end-users traffic is compromised by passive attacks. Passive attacks are silent in nature and do not harm the network traffic or normal network operations, therefore very difficult to detect. However, passive attacks lay down a foundation for later launching an active attack. In this article, we discuss the vulnerable features and possible passive threats in WMN along with current security mechanisms as well as future research directions. This article will serve as a baseline guide for the passive security threats and related issues in WMNs
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
- …