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Conference

International Conference on Mobile Networks and Management 

About: International Conference on Mobile Networks and Management is an academic conference. The conference publishes majorly in the area(s): The Internet & Heterogeneous network. Over the lifetime, 234 publications have been published by the conference receiving 1119 citations.

Papers published on a yearly basis

Papers
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Book ChapterDOI
13 Dec 2017
TL;DR: Investigating the role of ML techniques for developing a Network forensic mechanism based on network flow identifiers that can track suspicious activities of botnets revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets attacks and their tracks.
Abstract: The IoT is a network of interconnected everyday objects called “things” that have been augmented with a small measure of computing capabilities. Lately, the IoT has been affected by a variety of different botnet activities. As botnets have been the cause of serious security risks and financial damage over the years, existing Network forensic techniques cannot identify and track current sophisticated methods of botnets. This is because commercial tools mainly depend on signature-based approaches that cannot discover new forms of botnet. In literature, several studies have conducted the use of Machine Learning (ML) techniques in order to train and validate a model for defining such attacks, but they still produce high false alarm rates with the challenge of investigating the tracks of botnets. This paper investigates the role of ML techniques for developing a Network forensic mechanism based on network flow identifiers that can track suspicious activities of botnets. The experimental results using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets’ attacks and their tracks.

79 citations

Book ChapterDOI
22 Sep 2010
TL;DR: New security challenges as considered in SAIL for ensuring legitimate usage of cloud networking resources and for preventing misuse are presented.
Abstract: Cloud computing is widely considered as an attractive service model since the users commitments for investment and operations are minimised, and costs are in direct relation to usage and demand. However, when networking aspects for distributed clouds are considered, there is little support and the effort is often underestimated. The project SAIL is addressing cloud networking as the combination of management for cloud computing and vital networking capabilities between distributed cloud resources involved to improve the management of both. This position paper presents new security challenges as considered in SAIL for ensuring legitimate usage of cloud networking resources and for preventing misuse.

36 citations

Book ChapterDOI
21 Sep 2011
TL;DR: An analytical models for the Trickle algorithm’s behaviour for the time to consistency are introduced and this model is compared with simulation results for a set of network topologies and enables to discover efficient settings of the algorithm for various application areas, such as logistics.
Abstract: The Trickle algorithm has proven to be of great benefit to the Wireless Sensor Networking area. It has shown general applicability in this field, e.g. for code distribution to smart objects and routing information distribution between smart objects. Up to now analysis of the algorithm has focussed on simulation studies and measurement campaigns. This paper introduces an analytical models for the algorithm’s behaviour for the time to consistency. The model is compared with simulation results for a set of network topologies and enables to discover efficient settings of the algorithm for various application areas, such as logistics.

35 citations

Book ChapterDOI
22 Sep 2014
TL;DR: The results suggest that the proposed verification framework automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions.
Abstract: The concept known as Self-Organizing Networks (SON) has been developed for modern radio networks that deliver mobile broadband capabilities. In such highly complex and dynamic networks, changes to the configuration management (CM) parameters for network elements could have unintended effects on network performance and stability. To minimize unintended effects, the coordination of configuration changes before they are carried out and the verification of their effects in a timely manner are crucial. This paper focuses on the verification problem, proposing a novel framework that uses anomaly detection and diagnosis techniques that operate within a specified spatial scope. The aim is to detect any anomaly, which may indicate actual degradations due to any external or system-internal condition and also to characterize the state of the network and thereby determine whether the CM changes negatively impacted the network state. The results, generated using real cellular network data, suggest that the proposed verification framework automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions.

33 citations

Book ChapterDOI
13 Dec 2017
TL;DR: A Host-based IDS design procedure based on Convolutional Neural Network for system call traces is implemented and decent preliminary results harvested from modern benchmarking datasets NGIDS-DS and ADFA-LD demonstrated this approachs feasibility.
Abstract: Along with the drastic growth of telecommunication and networking, the cyber-threats are getting more and more sophisticated and certainly leading to severe consequences. With the fact that various segments of industrial systems are deployed with Information and Computer Technology, the damage of cyber-attacks is now expanding to physical infrastructure. In order to mitigate the damage as well as reduce the False Alarm Rate, an advanced yet well-design Intrusion Detection System (IDS) must be deployed. This paper focuses on system call traces as an object for designing a Host-based anomaly IDS. Sharing several similarities with research objects in Natural Language Processing and Image Recognition, a Host-based IDS design procedure based on Convolutional Neural Network (CNN) for system call traces is implemented. The decent preliminary results harvested from modern benchmarking datasets NGIDS-DS and ADFA-LD demonstrated this approachs feasibility.

29 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
202019
201730
201616
201521
201432
201319