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

Typing pattern analysis for fake profile detection in social Media

Nowadays, interaction with fake profiles of a genuine user in social media is a common problem. General users may not easily identify profiles created by fake users. Although various research works are going on all over the world to detect fake profiles in social media, focus of this paper is to remove additional efforts in detection procedure. Behavioral biometrics like typing pattern of users can be considered to classify genuine profile and fake profile without disrupting normal activities of the users. In this paper, DEEP_ID model is designed to detect fake profiles in Facebook like social media considering typing patterns like keystroke, mouse-click, and touch stroke. Proposed model can silently detect the profiles created by fake users when they type or click in social media from desktop, laptop, or touch devices. DEEP_ID model can also identify whether genuine profiles have been hacked by fake users or not in the middle of the session. The objective of proposed work is to demonstrate the hypothesis that user recognition algorithms applied to raw data can perform better if requirement for feature extraction can be avoided, which in turn can remove the problem of inappropriate attribute selection. Proposed DEEP_ID model is based on multi-view deep neural network, where network layers can learn data representation for user recognition based on raw data of typing pattern without feature selection and extraction. Proposed DEEP_ID model has achieved better results compared to traditional machine learning classifiers. It provides strong evidence that the stated hypothesis is valid. Evaluation results indicate that Deep_ID model is highly accurate in profile detection and efficient enough to perform fast detection

U-Stroke Pattern Modeling for End User Identity Verification Through Ubiquitous Input Device

Part 4: Data Analysis and Information RetrievalInternational audienceIdentity verification on ubiquitous input devices is a major concern to validate end-users, because of mobility of the devices. User device interaction (UDI) is capable to capture end-users’ behavioral nature from their device usage pattern. The primary goal of this paper is to collect heterogeneous parameters of usage patterns from any device and build personal profile with good-recognition capability. This work mainly focuses on finding multiple features captured from the usage of smart devices; so that parameters could be used to compose hybrid profile to verify end- users accurately. In this paper, U-Stroke modeling is proposed to capture behavioral data mainly from smart input devices in ubiquitous environment. In addition to this, concept of CCDA (capture, checking, decision, and action) model is proposed to process U-Stroke data efficiently to verify enduser’s identity. This proposal can draw attention of many researchers working on this domain to extend their research towards this direction