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2016 | Buch

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Advanced Computing and Systems for Security

Volume 1

herausgegeben von: Rituparna Chaki, Agostino Cortesi, Khalid Saeed, Nabendu Chaki

Verlag: Springer India

Buchreihe : Advances in Intelligent Systems and Computing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

The book contains the extended version of the works that have been presented and discussed in the Second International Doctoral Symposium on Applied Computation and Security Systems (ACSS 2015) held during May 23-25, 2015 in Kolkata, India. The symposium has been jointly organized by the AGH University of Science & Technology, Cracow, Poland; Ca’ Foscari University, Venice, Italy and University of Calcutta, India. The book is divided into volumes and presents dissertation works in the areas of Image Processing, Biometrics-based Authentication, Soft Computing, Data Mining, Next Generation Networking and Network Security, Remote Healthcare, Communications, Embedded Systems, Software Engineering and Service Engineering.

Inhaltsverzeichnis

Frontmatter

Security

Frontmatter

Computer User Profiling Based on Keystroke Analysis

Abstract

The article concerns the issues related to a computer user verification based on the analysis of a keyboard activity in a computer system. The research focuses on the analysis of a user’s continuous work in a computer system, which constitutes a type of a free-text analysis. To ensure a high level of a users’ data protection, an encryption of keystrokes was implemented. A new method of a computer user profiling based on encrypted keystrokes is introduced. Additionally, an attempt to an intrusion detection based on the \( k \)-NN classifier is performed.

Tomasz Emanuel Wesołowski, Piotr Porwik

Heart-Based Biometrics and Possible Use of Heart Rate Variability in Biometric Recognition Systems

Abstract

Heart rate variability (HRV) is an intrinsic property of heart and active research domain of the medical research community since last two decades. But in biometrics it is still in its infancy. This article is intended to present the state of art into heart-based biometrics and also explore the possibility of using HRV in biometric recognition systems. Subsequently, we designed hardware and software for data collection and also developed software for HRV analysis in Matlab, which generates 101 HRV Parameters (Features) using various HRV analysis techniques like statistical, spectral, geometrical, etc., which are commonly used and recommended for HRV analysis. All these features have their relative significance in medical interpretations and analysis, but among these 101 features reliable features that can be useful for biometric recognition were unknown; therefore feature selection becomes a necessary step. We used five different wrapper algorithms for feature selection, and obtained 10 reliable features out of 101. Using the proposed 10 HRV features, we used KNN for classification of subjects. The classification test gave us encouraging results with 82.22 % recognition rate.

Nazneen Akhter, Sumegh Tharewal, Vijay Kale, Ashish Bhalerao, K. V. Kale

Dynamic Ciphering-15 Based on Multiplicative Polynomial Inverses Over Galois Field GF(73)

Abstract

A new stream ciphering technique based on multiplicative polynomial inverses over Galois Field GF(7³) is proposed, where a set of randomly generated key-bytes, between 1 and 15, is dynamically permuted and XORed with the identical number of message bytes. The output cipher is tested using NIST Statistical Test Suite and results are compared with that obtained by the well-known RC4 stream cipher. The new cipher is statistically random and observed to be better than RC4.

J K M Sadique Uz Zaman, Sankhanil Dey, Ranjan Ghosh

On Preventing SQL Injection Attacks

Abstract

In this paper, we propose three new approaches to detect and prevent SQL Injection Attacks (SQLIA), as an alternative to the existing solutions namely: (i) Query Rewriting-based approach, (ii) Encoding-based approach, and (iii) Assertion-based approach. We discuss in detail the benefits and shortcomings of the proposals w.r.t. the literature.

Bharat Kumar Ahuja, Angshuman Jana, Ankit Swarnkar, Raju Halder

Securing Service in Remote Healthcare

Abstract

Health-care service in remote environment opens for several security challenges. These may affect confidentiality, integrity, and availability of resource. Securing service is a big concern for this kind of application. Encoding is required before uploading data to remote web server. Identity management is another primary aspect to validate any service. One-time identity verification during login has no importance, because valid session may be hijacked by impostors. Compared to other techniques, identity management based on human computer interaction is simple and less costly in remote environment. Service verification also needs to be considered to control access rights along with end user verification. A secured remote service (SecReS) framework is proposed here to ensure availability of health-care resource to valid end users. This service is capable to reduce time complexity, bandwidth cost, and to increase accuracy and attack resistance capacity. Theoretical analysis shows its efficiency.

Tapalina Bhattasali, Rituparna Chaki, Nabendu Chaki, Khalid Saeed

Systems Biology

Frontmatter

Inference of Gene Regulatory Networks with Neural-Cuckoo Hybrid

Abstract

Current progress in cellular biology and bioinformatics allow researchers to get a distinct picture of the complex biochemical processes those occur within a cell of the human body and remain as the cause for many diseases. Therefore, this technology opened up a new door to the researchers of computer science as well as to biologists to work together to investigate the causes of a disease. One of the greatest challenges of the post-genomic era is the investigation and inference of the regulatory interactions or dependencies between genes from the microarray data. Here, a new methodology has been devised for investigating the genetic interactions among genes from temporal gene expression data by combining the features of Neural Network and Cuckoo Search optimization. The developed technique has been applied on the real-world microarray dataset of Lung Adenocarcinoma for detection of genes which may be directly responsible for the cause of Lung Adenocarcinoma.

Sudip Mandal, Goutam Saha, Rajat K. Pal

Detection of Diabetic Retinopathy Using the Wavelet Transform and Feedforward Neural Network

Abstract

The early detection of diabetic retinopathy plays a significant role in modern ophthalmology. This experimental work presents the detection of diabetic retinopathy images by the implementation of the wavelet transform and feedforward neural network. The wavelet transform segments blood vessels from the retinal images and the changes in retinal vasculature due to diabetic retinopathy are characterized by the vessel features. The vessel features of all the input images are evaluated by the wavelet-based retinal image analyzer to tabulate the input and target databases. The input and target matrices are then fed to the neural network to detect the diabetic retinopathy images from the set of normal and diabetic retina images.

Manas Saha, Mrinal Kanti Naskar, B. N. Chatterji

Liver Fibrosis Diagnosis Support System Using Machine Learning Methods

Abstract

Liver fibrosis is a common disease of the European population (but not only them). It may have many backgrounds and may develop with a different rapidity—it may stay hidden for many years or rapidly develop into terminal stage called cirrhosis, where liver can no longer fulfill its function. Unfortunately, current methods of diagnosis are either connected with a potential risk for a patient and require a hospitalization or are expensive and not very accurate. This paper presents a comparative study of various feature selection algorithms combined with selected machine learning algorithms which may be used to build an advanced liver fibrosis diagnosis support system based on a nonexpensive and safe routine blood tests. Experiments carried out on a dataset collected by authors, proved usability and satisfactory accuracy of the presented algorithms.

Tomasz Orczyk, Piotr Porwik

Light-Weighted DNA-Based Cryptographic Mechanism Against Chosen Cipher Text Attacks

Abstract

DNA cryptography is a new cryptographic paradigm from hastily growing biomolecular computation, as its computational power will determine next generation computing. As technology is growing much faster, data protection is getting more important and it is necessary to design the unbreakable encryption technology to protect the information. In this paper, we proposed a biotic DNA-based secret key cryptographic mechanism, seeing as DNA computing had made great strides in ultracompact information storage, vast parallelism, and exceptional energy efficiency. This Biotic Pseudo DNA cryptography method is based upon the genetic information on biological systems. This method makes use of splicing system to improve security and random multiple key sequence to increase the degree of diffusion and confusion, which makes resulting cipher texts difficult to decipher and makes to realize a perfect secrecy system. Moreover, we also modeled the DNA-assembled public key cryptography for effective storage of public key as well as double binded encryption scheme for a given message. The formal and experimental analysis not only shows that this method is powerful against brute force attack and chosen cipher text attacks, but also it is very efficient in storage, computation as well as transmission.

E. Suresh Babu, C. Nagaraju, M. H. M. Krishna Prasad

Genetic Algorithm Using Guide Tree in Mutation Operator for Solving Multiple Sequence Alignment

Abstract

An improved mutation operator in genetic algorithm for solving multiple sequence alignment problems is proposed. In this step, the UPGMA method is used to generate the guide tree where two different matrices such as edit distance or dynamic distance have been used. The performance of the proposed method has been tested on Bali base with some of the existing methods such as, HMMT, DIALIGN, ML–PIMA, and PILEUP8. It has been observed that the proposed method perform better in most of the cases.

Rohit Kumar Yadav, Haider Banka

A Comparative Analysis of Image Segmentation Techniques Toward Automatic Risk Prediction of Solitary Pulmonary Nodules

Abstract

Lung cancer is considered as a leading cause of death throughout the globe. Manual interpretation of cancer detection is time consuming and thus increases the death rate. With the help of improvement in medical imaging technology, a computer-aided diagnostics system could be an aid to combat this disease. Automatic segmentation of a region of interest is one of the most challenging problem in medical image analysis. An inaccurate segmentation of solitary pulmonary nodule may lead to an erroneous prediction of the disease. In this paper, we perform a comparative study among the available segmentation techniques, which can automatically segment the solitary pulmonary nodules from high-resolution computed tomography (CT) images and then we propose a computerized lung nodule risk prediction model based on the best segmentation technique.

Jhilam Mukherjee, Soharab Hossain Shaikh, Madhuchanda Kar, Amlan Chakrabarti

Networking and Cloud Computing

Frontmatter

Safe Cloud: Secure and Usable Authentication Framework for Cloud Environment

Abstract

Cloud computing an emerging computing model having its roots in grid and utility computing is gaining increasing attention of both the industry and laymen. The ready availability of storage, compute, and infrastructure services provides a potentially attractive option for business enterprises to process and store data without investing on computing infrastructure. The attractions of Cloud are accompanied by many concerns among which Data Security is the one that requires immediate attention. Strong user authentication mechanisms which prevent illegal access to Cloud services and resources are one of the core requirements to ensure secure access. This paper proposes a user authentication framework for Cloud which facilitates authentication by individual service providers as well as by a third party identity provider. The proposed two-factor authentication protocols uses password as the first factor and a Smart card or Mobile Phone as the second factor. The protocols are resistant to various known security attacks.

Binu Sumitra, Pethuru Raj, M. Misbahuddin

KPS: A Fermat Point Based Energy Efficient Data Aggregating Routing Protocol for Multi-sink Wireless Sensor Networks

Abstract

Lifetime of a multi-sink wireless sensor network (WSN) may increase considerably when data aggregation is introduced in a Fermat point based routing protocol. However, data aggregation should come with a cost of delay in packet forwarding time. It has been seen that increasing the transmission distance could increase the network lifetime. However, our observation shows that after a certain point, lifetime readings of the network would dip for a distance vector type of protocol. Thus, it becomes necessary to choose an appropriate aggregation factor and transmission radius depending upon the requirement of the application for which the WSN has been installed. In this paper we have presented a Fermat point based data aggregating protocol which is distance vector protocol in nature. We have compared its lifetime with some other Fermat point based protocols and studied the effect of aggregation factor on cumulative delay for packet forwarding. Moreover, effect of increased transmission radius on the lifetime of the proposed protocol too was studied.

Kaushik Ghosh, Pradip K. Das, Sarmistha Neogy

The Design of Hierarchical Routing Protocol for Wireless Sensor Network

Abstract

Energy efficiency is the main challenge for wireless sensor networks. Power-aware schemes send the same data through multiple paths, which causes data redundancy and a huge amount of energy drainage within the network. Use of a head node for a cluster can be a better solution. The proposed algorithm creates several clusters, selects a cluster head, aggregates the data, and sends that to base station through other cluster heads. Simulation result shows that it helps to increase network longevity.

Ayan Kumar Das, Rituparna Chaki, Kashi Nath Dey

Network Selection Using AHP for Fast Moving Vehicles in Heterogeneous Networks

Abstract

In today’s world, there are various means of accessing the Internet such as cellular, wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX), etc., for the mobile users. Also, various applications demand different quality of service (QoS) parameters. But for seamless connectivity in the case of fast moving vehicles, velocity of vehicle becomes an important issue. Traditional schemes trigger the handover process based on signal strength. These schemes do not incorporate the network parameters and user preferences required for optimal vertical handover. In this paper, analytic hierarchy process (AHP) method has been used for network selection in heterogeneous environments for moving vehicles. The method has been applied for various types of applications like conversational, streaming, interactive, and background applications. From the results, it has been found that WLAN’s performance degrades significantly when the vehicles are moving at higher velocities while Universal Mobile Telecommunication Systems (UMTS) performs best for fast moving vehicles.

Raman Kumar Goyal, Sakshi Kaushal

Data Analytics

Frontmatter

Context-Aware Graph-Based Visualized Clustering Approach (CAVCA)

Abstract

The Clustering algorithms cannot detect the number of clusters for unlabeled data. The visual access tendency (VAT) is recognized as the best approach for cluster detection. However, context-aware-based graphs (CAG) give more informative cluster assessment for VAT. Hence, we extend the VAT using CAG, known as CAVAT. This paper investigates the existing cluster detection methods and proposes a data clustering method for the CAVAT for archiving the efficient clustering results.

K. Rajendra Prasad, B. Eswara Reddy

Materialized View Construction Using Linearizable Nonlinear Regression

Abstract

Query processing at runtime is an important issue for data-centric applications. A faster query execution is highly required which means searching and returning the appropriate data of database. Different techniques have been proposed over the time and materialized view construction is one of them. The efficiency of a materialized view (MV) is measured based on hit ratio, which indicates the ratio of number of successful search to total numbers of accesses. Literature survey shows that few research works has been carried out to analyze the relationship between the attributes based on nonlinear equations for materialized view creation. However, as nonlinear regression is slower, in this research work they are mapped into linear equations to keep the benefit of both the approaches. This approach is applied to recently executed query set to analyze the attribute affinity and then the materialized view is formed based on the result of attribute affinity.

Soumya Sen, Partha Ghosh, Agostino Cortesi

Backmatter

Titel: Advanced Computing and Systems for Security
herausgegeben von: Rituparna Chaki
Agostino Cortesi
Khalid Saeed
Nabendu Chaki
Verlag: Springer India
Electronic ISBN: 978-81-322-2650-5
Print ISBN: 978-81-322-2648-2
DOI: https://doi.org/10.1007/978-81-322-2650-5