3,324 research outputs found
Sleep Deprivation Attack Detection in Wireless Sensor Network
Deployment of sensor network in hostile environment makes it mainly
vulnerable to battery drainage attacks because it is impossible to recharge or
replace the battery power of sensor nodes. Among different types of security
threats, low power sensor nodes are immensely affected by the attacks which
cause random drainage of the energy level of sensors, leading to death of the
nodes. The most dangerous type of attack in this category is sleep deprivation,
where target of the intruder is to maximize the power consumption of sensor
nodes, so that their lifetime is minimized. Most of the existing works on sleep
deprivation attack detection involve a lot of overhead, leading to poor
throughput. The need of the day is to design a model for detecting intrusions
accurately in an energy efficient manner. This paper proposes a hierarchical
framework based on distributed collaborative mechanism for detecting sleep
deprivation torture in wireless sensor network efficiently. Proposed model uses
anomaly detection technique in two steps to reduce the probability of false
intrusion.Comment: 7 pages,4 figures, IJCA Journal February 201
Detection techniques of selective forwarding attacks in wireless sensor networks: a survey
The wireless sensor network has become a hot research area due its wide range
of application in military and civilian domain, but as it uses wireless media
for communication these are easily prone to security attacks. There are number
of attacks on wireless sensor networks like black hole attack, sink hole
attack, Sybil attack, selective forwarding attacks etc. in this paper we will
concentrate on selective forwarding attacks In selective forwarding attacks,
malicious nodes behave like normal nodes and selectively drop packets. The
selection of dropping nodes may be random. Identifying such attacks is very
difficult and sometimes impossible. In this paper we have listed up some
detection techniques, which have been proposed by different researcher in
recent years, there we also have tabular representation of qualitative analysis
of detection techniquesComment: 6 Page
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Intrusion-aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks
Existing anomaly and intrusion detection schemes of wireless sensor networks
have mainly focused on the detection of intrusions. Once the intrusion is
detected, an alerts or claims will be generated. However, any unidentified
malicious nodes in the network could send faulty anomaly and intrusion claims
about the legitimate nodes to the other nodes. Verifying the validity of such
claims is a critical and challenging issue that is not considered in the
existing cooperative-based distributed anomaly and intrusion detection schemes
of wireless sensor networks. In this paper, we propose a validation algorithm
that addresses this problem. This algorithm utilizes the concept of
intrusion-aware reliability that helps to provide adequate reliability at a
modest communication cost. In this paper, we also provide a security resiliency
analysis of the proposed intrusion-aware alert validation algorithm.Comment: 19 pages, 7 figure
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