A survey on elliptic curve cryptography

Cryptography is an evolving field that research into discreet mathematical equation that is representable by computer algorithm for providing message condentiality. The scheme has been widely used by nation-states, corporates and individual who seeks privacy for data in storage and during transmission. This paper provides a ground up survey on elliptic curve cryptography. It tailors the mathematics behind elliptic curve to the applicability within a cryptosystem. In brief, elliptic curve is a study of points on two-variable polynomials of degree 3. With curve dened over a finite field, this set of points acted by an addition operation forms a finite group structure. Also known as torsion points, they are used to represent the coded messages. Encryption and decryption transform a point into another point in the same set. Besides providing conceptual understanding, discussions are targeting the issues of security and efficiency of elliptic curve cryptosystem. This paper serve as a basis in guiding anyone interested to understand the fundamental concept behind this cryptosystem. Moreover, we also highlight subareas of research within the scope of elliptic curve cryptosystem

Advanced approach for encryption using advanced encryption standard with chaotic map

At present, security is significant for individuals and organizations. All information need security to prevent theft, leakage, alteration. Security must be guaranteed by applying some or combining cryptography algorithms to the information. Encipherment is the method that changes plaintext to a secure form called cipherment. Encipherment includes diverse types, such as symmetric and asymmetric encipherment. This study proposes an improved version of the advanced encryption standard (AES) algorithm called optimized advanced encryption standard (OAES). The OAES algorithm utilizes sine map and random number to generate a new key to enhance the complexity of the generated key. Thereafter, multiplication operation was performed on the original text, thereby creating a random matrix (4×4) before the five stages of the coding cycles. A random substitution-box (S-Box) was utilized instead of a fixed S-Box. Finally, we utilized the eXclusive OR (XOR) operation with digit 255, also with the key that was generated last. This research compared the features of the AES and OAES algorithms, particularly the extent of complexity, key size, and number of rounds. The OAES algorithm can enhance complexity of encryption and decryption by using random values, random S-Box, and chaotic maps, thereby resulting in difficulty guessing the original text

Speech encryption by multiple chaotic map with fast fourier transform

There are various ways of social communication including writing (WhatsApp, Messenger, Facebook, Twitter, Skype, etc), calling (mobile phone) and voice recording (record your voice and then send it to the other party), but there are ways to eavesdropping the calls and voice messages, One way to solve this problem is via cryptographic approach. Chaos cryptography build on top of nonlinear dynamics chaotic system has gained some footstep in data security. It provides an alternative to conventional cryptography built on top of mathematical structures. This research focuses on the protection of speech recording by encrypting it with multiple encryption algorithms, including chaotic maps (Logistic Map and Sine Maps)

Performance evaluation of IDSDV over DSDV for specific traffic pattern.

Mobile Ad-Hoc Networks is an infrastructure less mobile network having mobile nodes entering and leaving the network freely at any time. The decentralized nature requires every node plays a router role and have its own routing table to other nodes in the network. Many categories of routing protocol exists, in this paper we studies one that is based on routing strategy that employs proactive approach namely DSDV and IDSDV. DSDV protocol is known to have a low performance in packet delivery ratio due to stale route problem. In case of link breakage, it is incapable of providing an alternative route. IDSDV addresses this issue by introducing a novel message exchange scheme for reconstruction of broken route, to allow packet to be transmitter and thus increases the performance. Many have reported this improvement, but none of the tests were meant for individual traffics pattern. Based on selected metrics, we re-evaluate the performance of IDSDV over DSDV for TCP traffic, with respect to chosen mobility model, varying number of nodes, pause time and nodes speed. Simulation result shows that the performance of IDSDV outclasses DSDV with respect to routing overhead metric. Meanwhile for packet delivery fraction metric, both protocols are almost equally performed

A new compression algorithm for small data communication in wireless sensor network

Energy consumption is one of the most critical issues in wireless sensor network (WSN). For a sensor device, transmission of data is considered as the most energy consuming task, and it mostly depends on the size of the data. Fortunately, data compression can be used to minimise the transmitted data size and thus extend sensor's lifetime. In this paper, we propose a new lossless compression algorithm that can handle small data communication in WSNs. Using compression ratio, memory usage, number of instructions and execution speed as a comparison parameters, the proposed algorithm is measured against a set of existing algorithms. Two different datasets have been used for this purpose; namely, self-generated dataset and real sensor dataset from Harvard Sensor Library. As a result, the proposed algorithm not only outclasses other existing algorithms but most importantly produces positive compression ratio throughout the whole test where most existing algorithms experience an expansion in data size when dealing with very small data

Attacks, vulnerabilities and security requirements in smart metering networks

A smart meter is one of the core components in Advanced Metering Infrastructure (AMI) that is responsible for providing effective control and monitor of electrical energy consumptions. The multifunction tasks that a smart meter carries out such as facilitating two-way communication between utility providers and consumers, managing metering data, delivering anomalies reports, analyzing fault and power quality, simply show that there are huge amount of data exchange in smart metering networks (SMNs). These data are prone to security threats due to high dependability of SMNs on Internet-based communication, which is highly insecure. Therefore, there is a need to identify all possible security threats over this network and propose suitable countermeasures for securing the communication between smart meters and utility provider office. This paper studies the architecture of the smart grid communication networks, focuses on smart metering networks and discusses how such networks can be vulnerable to security attacks. This paper also presents current mechanisms that have been used to secure the smart metering networks from specific type of attacks in SMNs. Moreover, we highlight several open issues related to the security and privacy of SMNs which we anticipate could serve as baseline for future research directions

Recent trends in channel assignment algorithms for multi-radio multi-channel in Wireless Mesh Network

Wireless Mesh Networks (WMN) are an attractive technology and has been widely accepted by many organizations due to features such as accessing and routing. The issues regarding capabilities of multi-radio multi-channel (MRMC) has been extensively studied to design an efficient algorithm for WMN. Channel assignment and various techniques have been designed and developed to improve the network performance of MRMC. This paper offers conceptual understanding through a systematic review by classifying channel assignment constraints and its proposed solution. The results from our study provide clear understanding of approaches reported by previous studies in solving channel assignment problem. The analysis offered variety of areas that can be explored in leveraging channel assignment techniques towards improving the network performances

A hybrid heuristic of variable neighbourhood descent and great deluge algorithm for efficient task scheduling in grid computing

To achieve the ultimate success of global collaborative resource sharing in Grid computing, an effective and efficient Grid resource management system is necessary and it is only possible if its core component, the scheduler, can perform scheduling in an efficient manner. Scheduling tasks to resources in Grid computing is a challenging task and known as a NP hard problem. In this paper, we propose a novel hybrid heuristic-based algorithm, which synergised the excellent diversification capability of Great Deluge (GD) algorithm with the powerful systematic multi-neighbourhood search strategy captured in Variable Neighbourhood Descent (VND) algorithm, to efficiently schedule independent tasks in Grid computing environment with an objective of minimising the makespan. Simulation experiments have been conducted to examine the impact of hybridising GD and VND. In addition, the performance of the proposed algorithm has been evaluated and compared with some other recent meta-heuristics in the literature. The experimental simulation results show that our proposed algorithm outperforms the other algorithms in the literature and the performance improvement achieved by this hybrid strategy is effective and efficient with respect to makespan and computational time as it can obtain good quality (makespan) of solutions while obviating the drawback of requiring high computational cost from the VND

Improve of contrast-distorted image quality assessment based on convolutional neural networks

Many image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for Contrast Distorted Images (CDI), e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQA-CDI). The existing NR-IQA-CDI relies on features designed by human or handcrafted features because considerable level of skill, domain expertise and efforts are required to design good handcrafted features. Recently, there is great advancement in machine learning with the introduction of deep learning through Convolutional Neural Networks (CNN) which enable machine to learn good features from raw image automatically without any human intervention. Therefore, it is tempting to explore the ways to transform the existing NR-IQA-CDI from using handcrafted features to machine-crafted features using deep learning, specifically Convolutional Neural Networks (CNN).The results show that NR-IQA-CDI based on non-pre-trained CNN (NR-IQA-CDI-NonPreCNN) significantly outperforms those which are based on handcrafted features. In addition to showing best performance, NR-IQA-CDI-NonPreCNN also enjoys the advantage of zero human intervention in designing feature, making it the most attractive solution for NR-IQA-CDI

A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication

Authentication is a way to enable an individual to be uniquely identified usually based on passwords and personal identification number (PIN). The main problems of such authentication techniques are the unwillingness of the users to remember long and challenging combinations of numbers, letters, and symbols that can be lost, forged, stolen, or forgotten. In this paper, we investigate the current advances in the use of behavioral-based biometrics for user authentication. The application of behavioral-based biometric authentication basically contains three major modules, namely, data capture, feature extraction, and classifier. This application is focusing on extracting the behavioral features related to the user and using these features for authentication measure. The objective is to determine the classifier techniques that mostly are used for data analysis during authentication process. From the comparison, we anticipate to discover the gap for improving the performance of behavioral-based biometric authentication. Additionally, we highlight the set of classifier techniques that are best performing for behavioral-based biometric authentication