Online ISSN: 2515-8260

Keywords : Intrusion detection


L. Sheeba; Dr.V.S. Meenakshi

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 923-937

Sybil attack is the most threatened issues found in the network which will cause
consumption of more network spaces. Preventing the Sybil attack is difficult task where
every fake identity generated by the attacker will looks like genuine id to the other nodes.
In our previous work Sybil attack detection is performed through presenting an approach
named Latency and Power aware Reliable Intrusion Detection System (LP-RIDS).
However, this methodology failed in preventing Sybil attack occurrence which is focused in
this work. In this work, Sybil attack prevention is ensured by introducing Privacy
concerned Anonymous Authentication Method (PAAM). In this work, secondary cluster
head selection is accomplished through Hybrid genetic with ACO algorithm. Here the
secondary cluster head selection is done with the concern of higher energy value. The node
that have energy lesser than threshold will not be considered for the Sybil node attack
detection. And then anonymous authentication is performed to detect the Sybil attack. This
is done by initializing the registration phase where all cluster members will share their
temporal identity with the cluster head. Here temporal identity is generated randomly by
each cluster member which is not possible to guess by other nodes. By using this temporal
identity cluster head will generate the secret key which will be divided into two shares.
Here first share will be given to the corresponding cluster member and second share will
be given to the secondary cluster head. Thus no node can identify the privacy information
of other users. This secret information will be utilized for the authentication process.
Secondary cluster head will verify the cluster members at the time of data communication
with the help of first share of secret key. This research method avoids the Sybil attack
presence accurately. NS2 is greatly utilized in this research for validating the proposed
techniques which offers better result when compared with prevailing work.
The overall implementation of the research work is done in the NS2 from which it is
proved that the proposed techniques tends to provide better outcome than existing work.


Dr. Lokesh P Gagnani; Ramesh S; R. Senthil; Krishnakumar V; Dr. SyedKhasim

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3210-3218

IOT Alert based aggregation is an important subtask of intrusion detection. The goal is to
identify and to cluster different alert produced by low-level intrusion detection systems,
firewalls, etc.belonging to a specific attack instance which has beeninitiated by an attacker at a
certain point in time. Thus, meta-alerts can be generated for the clusters that contain all the
relevantinformation whereas the amount of data (i.e., alerts) can be reduced substantially. Metaalerts
may then be the basis for reporting tosecurity experts or for communication within a
distributed intrusion detection system. This method proposes a novel technique for online
alertaggregation which is based on a dynamic, probabilistic model of the current attack situation.
Basically, it can be regarded as a datastream version of a maximum likelihood approach for the
estimation of the model parameters.It describes the problem of intrusion detection in detail and
analyze various well known methods for intrusion detection with respect to two critical
requirements using SparkV Dataset.