Vallipuram Muthukkumarasamy

Off-line Signature Verification Using Enhanced Modified Direction Features in Conjunction with Neural Classifiers and Support Vector Machines

As a biometric, signatures have been widely used to identify people. In the context of static image processing, the lack of dynamic information such as velocity, pressure and the direction and sequence of strokes has made the realization of accurate off-line signature verification systems more challenging as compared to their on-line counterparts. In this paper, we propose an effective method to perform off-line signature verification based on intelligent techniques. Structural features are extracted from the signatures contour using the modified direction feature (MDF) and its extended version: the Enhanced MDF (EMDF). Two neural network-based techniques and Support Vector Machines (SVMs) were investigated and compared for the process of signature verification. The classifiers were trained using genuine specimens and other randomly selected signatures taken from a publicly available database of 3840 genuine signatures from 160 volunteers and 4800 targeted forged signatures. A distinguishing error rate (DER) of 17.78% was obtained with the SVM whilst keeping the false acceptance rate for random forgeries (FARR) below 0.16%.

Securing Wireless Mesh Networks

Now found in domestic, commercial, industrial, military, and healthcare applications, wireless networks are becoming ubiquitous. Wireless mesh networks (WMNs) combine the robustness and performance of conventional infrastructure networks with the large service area and self-organizing and self-healing properties of mobile ad hoc networks. In this article, the authors consider the problem of ensuring security in WMNs, introduce the IEEE 802.11s draft standard, and discuss the open security threats faced at the network and data-link layers.

Off-line Signature Verification based on the Modified Direction Feature

Signature identification and verification has been a topic of interest and importance for many years in the area of biometrics. In this paper we present an effective method to perform off-line signature verification and identification. To commence the process, the signatures contour is first determined from its binary representation. Unique structural features are subsequently extracted from the signatures contour through the use of a novel combination of the modified direction feature (MDF) in conjunction with additional distinguishing features to train and test two neural network-based classifiers. A resilient back propagation neural network and a radial basis function neural network were compared. Using a publicly available database of 2106 signatures containing 936 genuine and 1170 forgeries, we obtained a verification rate of 91.12%

Off-line Signature Verification using the Enhanced Modified Direction Feature and Neural-based Classification

Signatures continue to be an important biometric for authenticating the identity of human beings. This paper presents an effective method to perform off-line signature verification using unique structural features extracted from the signatures contour. A novel combination of the modified direction feature (MDF) and additional distinguishing features such as the centroid, surface area, length and skew are used for classification. A resilient backpropagation (RBP) neural network and a radial basis function (RBF) network were compared in terms of verification accuracy. Using a publicly available database of 2106 signatures (936 genuine and 1170 forgeries), verification rates of 91.21% and 88.0% were obtained using RBF and RBP respectively.

On the Security of Permutation-Only Image Encryption Schemes

Permutation is a commonly used primitive in multimedia (image/video) encryption schemes, and many permutation-only algorithms have been proposed in recent years for the protection of multimedia data. In permutation-only image ciphers, the entries of the image matrix are scrambled using a permutation mapping matrix which is built by a pseudo-random number generator. The literature on the cryptanalysis of image ciphers indicates that the permutation-only image ciphers are insecure against ciphertext-only attacks and/or known/chosenplaintext attacks. However, the previous studies have not been able to ensure the correct retrieval of the complete plaintext elements. In this paper, we revisited the previous works on cryptanalysis of permutation-only image encryption schemes and made the cryptanalysis work on chosen-plaintext attacks complete and more efficient. We proved that in all permutationonly image ciphers, regardless of the cipher structure, the correct permutation mapping is recovered completely by a chosenplaintext attack. To the best of our knowledge, for the first time, this paper gives a chosen-plaintext attack that completely determines the correct plaintext elements using a deterministic method. When the plain-images are of size M × N and with L different color intensities, the number n of required chosen plain-images to break the permutation-only image encryption algorithm is n = ΓlogL(MN)1. The complexity of the proposed attack is O (n · M N) which indicates its feasibility in a polynomial amount of computation time. To validate the performance of the proposed chosen-plaintext attack, numerous experiments were performed on two recently proposed permutation-only image/video ciphers. Both theoretical and experimental results showed that the proposed attack outperforms the state-of-theart cryptanalytic methods.

Detecting Man-in-the-Middle and Wormhole Attacks in Wireless Mesh Networks

Wireless networks are being used increasingly in industrial, health care, military and public-safety environments. In these environments security is extremely important because a successful attack against the network may pose a threat to human life. To secure such wireless networks against hostile attack requires both preventative and detective measures.In this paper we propose a novel intrusion detection mechanism that identifies man-in-the-middle and wormhole attacks against wireless mesh networks by external adversaries. A simple modification to the wireless MAC protocol is proposed to expose the presence of an adversary conducting a frame-relaying attack. We evaluate the modified MAC protocol experimentally and show the detection mechanism to have a high detection rate, no false positives and a small computational and communication overhead.

Authenticated Key Establishment Protocols for a Home Health Care System

Wireless sensor networks provide solutions to a range of monitoring problems. However, they also introduce a new set of problems mainly due to small memories, weak processors, limited energy and small packet size. Thus only a very few conventional protocols can readily be used in sensor networks. Sensor networks can exist in many different environments, and each environment has its own unique characteristics and requirements. As an example application, a home health care system is proposed and examined in detail in this paper. We show how cryptographically weak physiological data can be used to establish keys between body sensors, where the sensors have no other prior secret. This paper also proposes a protocol where a hand held device, such as a PDA, can establish a key with the majority of sensors found in our home health care system. This is achieved without the necessity of using traditional encryption. Detailed analysis of each of the protocols is provided. The protocols were implemented in TinyOS and simulated using TOSSIM and ATEMU. Energy consumption and memory requirements are analysed and it was found that an RSA implementation of our protocols has some advantages over an ECC implementation.

Flow-Based Anomaly Detection Using Neural Network Optimized with GSA Algorithm

Reliable high-speed networks are essential to provide quality services to ever growing Internet applications. A Network Intrusion Detection System (NIDS) is an important tool to protect computer networks from attacks. Traditional packet-based NIDSs are time-intensive as they analyze all network packets. A state-of-the-art NIDS should be able to handle a high volume of traffic in real time. Flow-based intrusion detection is an effective method for high speed networks since it inspects only packet headers. The existence of new attacks in the future is another challenge for intrusion detection. Anomaly-based intrusion detection is a well-known method capable of detecting unknown attacks. In this paper, we propose a flow-based anomaly detection system. Artificial Neural Network (ANN) is an important approach for anomaly detection. We used a Multi-Layer Perceptron (MLP) neural network with one hidden layer. We investigate the use of a Gravitational Search Algorithm (GSA) in optimizing interconnection weights of a MLP network. Our proposed GSA-based flow anomaly detection system (GFADS) is trained with a flow-based data set. The trained system can classify benign and malicious flows with 99.43% accuracy. We compare the performance of GSA with traditional gradient descent training algorithms and a particle swarm optimization (PSO) algorithm. The results show that GFADS is effective in flow-based anomaly detection. Finally, we propose a four-feature subset as the optimal set of features.

Trust-Based Cluster Head Selection Algorithm for Mobile Ad Hoc Networks

Mobile Ad hoc Networks (MANETs) consist of a large number of relatively low-powered mobile nodes communicating in a network using radio signals. Clustering is one of the techniques used to manage data exchange amongst interacting nodes. Each group of nodes has one or more elected Cluster head(s), where all Cluster heads are interconnected for forming a communication backbone to transmit data. Moreover, Cluster heads should be capable of sustaining communication with limited energy sources for longer period of time. Misbehaving nodes and cluster heads can drain energy rapidly and reduce the total life span of the network. In this context, selection of best cluster heads with trusted information becomes critical for the overall performance. In this paper, we propose Cluster head(s) selection algorithm based on an efficient trust model. This algorithm aims to elect trustworthy stable cluster head(s) that can provide secure communication via cooperative nodes. Simulations were conducted to evaluate trusted Cluster head(s) in terms of clusters stability, longevity and throughput.

Securing Smart Cities Using Blockchain Technology

A smart city uses information technology to integrate and manage physical, social, and business infrastructures in order to provide better services to its dwellers while ensuring efficient and optimal utilization of available resources. With the proliferation of technologies such as Internet of Things (IoT), cloud computing, and interconnected networks, smart cities can deliver innovative solutions and more direct interaction and collaboration between citizens and the local government. Despite a number of potential benefits, digital disruption poses many challenges related to information security and privacy. This paper proposes a security framework that integrates the blockchain technology with smart devices to provide a secure communication platform in a smart city.