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All Journal JOIV : International Journal on Informatics Visualization
Elham Bahmani
Islamic Azad University, Malayer, Iran
Computer Science & IT

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Advanced Extremely Efficient Detection of Replica Nodes in Mobile Wireless Sensor Networks Mehdi Safari; Elham Bahmani; Mojtaba Jamshidi; Abdusalam Shaltooki
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (900.726 KB) | DOI: 10.30630/joiv.3.4.254

Abstract

Today, wireless sensor networks (WSNs) are widely used in many applications including the environment, military, and explorations. One of the most dangerous attacks against these networks is node replication. In this attack, the adversary captures a legal node of the network, generates several copies of the node (called, replica nodes) and injects them in the network. Various algorithms have been proposed to handle replica nodes in stationary and mobile WSNs. One of the most well-known algorithms to handle this attack in mobile WSNs is eXtremely Efficient Detection (XED). The main idea of XED is to generate and exchange random numbers among neighboring nodes. The XED has some drawbacks including high communication and memory overheads and low speed in the detection of replica nodes. In this paper, an algorithm is presented to improve XED. The proposed algorithm is called Advanced XED (AXED) in which each node observes a few numbers of nodes and whenever two nodes meet, a new random number is generated and exchanged. The efficiency of the proposed algorithm is evaluated in terms of the memory and communication overheads and its results are compared with existing algorithms. The comparison results show that the proposed algorithm imposes lower overheads to the nodes. In addition, the proposed algorithm is simulated and the simulation results show that the proposed algorithm is able to detect replica nodes faster than XED.
Using One-hop and Two-hop Neighbouring Information to Defend Against Sybil Attacks in Stationary Wireless Sensor Network Elham Bahmani; Sheida Dashtevan; Abdusalam Shaltooki; Mojtaba Jamshidi
JOIV : International Journal on Informatics Visualization Vol 3, No 2 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1045.831 KB) | DOI: 10.30630/joiv.3.2.235

Abstract

Considering the application of wireless sensor networks (WSNs) in critical areas like war fields, establishing security in these networks is of great challenge. One of the important and dangerous attacks in these networks is the Sybil attack. In this attack, a malicious node broadcasts several IDs simultaneously. Thus, the malicious node of the adversary attracts high traffic to itself and disrupts routing protocols and affects other operations of the network like data aggregation, voting, and resource allocation, negatively. In this paper, an efficient algorithm based on one-hop and two-hop neighborhood information is proposed to detect Sybil nodes in the stationary WSNs. The proposed algorithm is executed locally with the collaboration of neighboring nodes. The main purpose of the proposed algorithm is to increase the accuracy of detecting Sybil nodes under various conditions including the condition in which a malicious node broadcasts a few numbers of Sybil IDs which is the shortcoming of most existing algorithms. The proposed algorithm is simulated in MATLAB and its efficiency is compared with two similar algorithms in terms of true and false detection rates. The proposed algorithm not only reduces communication overhead but also increases the accuracy of detecting Sybil nodes compared to two similar algorithms.
Breast Cancer Prediction Using a Hybrid Data Mining Model Elham Bahmani; Mojtaba Jamshidi; Abdusalam Shaltooki
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (972.825 KB) | DOI: 10.30630/joiv.3.4.240

Abstract

Today, with the emergence of data mining technology and access to useful data, valuable information in different areas can be explored. Data mining uses machine learning algorithms to extract useful relationships and knowledge from a large amount of data and offers an automatic tool for various predictions and classifications. One of the most common applications of data mining in medicine and health-care is to predict different types of breast cancer which has attracted the attention of many scientists. In this paper, a hybrid model employing three algorithms of Naive Bayes Network, RBF Network, and K-means clustering is presented to predict breast cancer type. In the proposed model, the voting approach is used to combine the results obtained from the above three algorithms. Dataset used in this study is called Breast Cancer Wisconsin taken from data sources of UCI. The proposed model is implemented in MATLAB and its efficiency in predicting breast cancer type is evaluated on Breast Cancer Wisconsin dataset. Results show that the proposed hybrid model achieves an accuracy of 99% and mean absolute error of 0.019 which is superior over other models.
Co-Authors Abdusalam Shaltooki Mehdi Safari Mojtaba Jamshidi Sheida Dashtevan