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About

About

Anisa Allahdadi received her B.Sc. and M.Sc in computer science and software engineering, respectively, from BIHE University (Bahá'í Institute for Higher Education), in Iran. Anisa holds a Ph.D. in Computer Science from the University of Porto under the MAP-i Doctoral Programme. She is currently a researcher in the Center of High-Assurance Software at INESC TEC. Her research interests include machine learning, data mining, deep learning, natural language processing, probabilistic graphical models, and wireless network management and security.

Interest
Topics
Details

Details

  • Name

    Anisa Allahdadidastjerdi
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    17th November 2011
001
Publications

2020

802.11 wireless simulation and anomaly detection using HMM and UBM

Authors
Allahdadi, A; Morla, R; Cardoso, JS;

Publication
SIMULATION

Abstract
Despite the growing popularity of 802.11 wireless networks, users often suffer from connectivity problems and performance issues due to unstable radio conditions and dynamic user behavior, among other reasons. Anomaly detection and distinction are in the thick of major challenges that network managers encounter. The difficulty of monitoring broad and complex Wireless Local Area Networks, that often requires heavy instrumentation of the user devices, makes anomaly detection analysis even harder. In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model (HMM) and Universal Background Model (UBM) on data that is inexpensive to obtain. We then generate a number of network anomalous scenarios in OMNeT++/INET network simulator and compare the detection outcomes with those in baseline approaches—RawData and Principal Component Analysis. The experimental results show the superiority of HMM and HMM-UBM models in detection precision and sensitivity.

2019

Anomaly Detection and Modeling in 802.11 Wireless Networks

Authors
Allahdadi, A; Morla, R;

Publication
CoRR

Abstract

2014

Outlier Detection in 802.11 Wireless Access Points Using Hidden Markov Models

Authors
Allahdadi, A; Morla, R; Cardoso, JS;

Publication
2014 7TH IFIP WIRELESS AND MOBILE NETWORKING CONFERENCE (WMNC)

Abstract
In 802.11 Wireless Networks, detecting faulty equipment, poor radio conditions, and changes in user behavior through anomaly detection techniques is of great importance in network management. The traffic load and user movement on different access points (APs) in a wireless covered area vary with time, making these network management tasks harder. We intend to inspect the evolving structure of wireless networks and their inherent dynamics in order to provide models for anomaly detection. For this purpose we explore the temporal usage behavior of the network by applying various types of Hidden Markov Models. We observe the usage pattern of up to 100 APs in one week period in 2011 at the Faculty of Engineering of the University of Porto. The first step of this study consists of constructing various Hidden Markov Models from 802.11 AP usage data. We then apply statistical techniques for outlier detection and justify the presented outliers by inspecting the models' parameters and a set of HMM indicators. We finally introduce examples of wireless networks anomalous patterns based on the transitions between HMM states and provide an analysis of the entire set of APs under study.