Fingerprint is a widely used biometrics. Its extensive usage motivates imposter to fabricate fake fingerprints. Vitality detection has been proposed to prevent counterfeit finger attack. Currently the detection can be done either during the process of acquiring fingerprint image or by comparing multiple sequentially acquired images. It is an ongoing research problem to detect whether a given fingerprint image is obtained from a real or a fake fingertip. In this paper we look into the differences between real and fake fingerprints as the first step to approach this problem. Specifically, we study the effects of different imaging sensors on the sizes of templates and on the matching scores between real and fake fingerprints. We also compare the fake fingerprints made from different materials. Experiments are carried out with two publicly available fingerprint databases and the findings are reported.

The increasing popularity of smart devices have led users to complete all of their daily work with these devices. Users are now able to shop online, share information with the applications that they install on their smart devices. Installed applications gain access to various sensitive information, such as the user's contact list, phone number, location. However, there is no control mechanism in place that can check whether these applications are safe to install. Therefore, applications are installed according to the users’ decisions, without any limitations or warnings. As a result, users become the target of malicious applications, and the personal security and privacy are compromised. In this study, we investigate the security solutions that aim to protect the privacy and security of Android users. We reveal the shortcomings of mobile security solutions and shed light on the research community. Additionally, we present the taxonomy of Android-based mobile security solutions.

Centrality is an important concept in the study of social network analysis (SNA), which is used to measure the importance of a node in a network. While many different centrality measures exist, most of them are proposed and applied to static networks. However, most types of networks are dynamic that their topology changes over time. A popular approach to represent such networks is to construct a sequence of time windows with a single aggregated static graph that aggregates all edges observed over some time period. In this paper, an approach which overcomes the limitation of this representation is proposed based on the notion of the time-ordered graph, to measure the communication centrality of a node in dynamic networks.

This paper presents a novel energy aware centralized dynamic clustering routing framework for large-scale Wireless Sensor Network (WSN). The main advantage of the proposed method is pluggability of clustering algorithms in the framework. It uses some clustering algorithms that some of their usages are new in this field. The clustering algorithms are K-means, FCM, UPC, GA, IGA and FGKA that run at base station used to identify cluster of sensors. Six clustering algorithms are evaluated in the framework and results of them are compared in three models named unicast, multicast and broadcast.

Sensor nodes are prone to failure due to energy depletion and some other reasons in Distributed Sensor Networks (DSNs). In this regard fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation DSNs. This paper proposes a node fault detection and recovery by using Genetic Algorithm (GA), when some of the sensor nodes faulty in DSN. The main objective of this work is to provide fault tolerance mechanism, which is energy efficient and responsive to network by using GA which is used to detect the faulty of nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). We have evaluated the performance parameters for the proposed scheme.

Alert correlation is the process of analyzing, relating and fusing the alerts generated by one or more Intrusion Detection Systems (IDS) in order to provide a high-level and comprehensive view of the security situation of the system or network. Different approaches, such as rule-based, prerequisites consequences-based, learning-based and similarity-based approach are used in correlation process. In this paper, a new AIS-inspired architecture is presented for alert correlation. Different aspects of human immune system (HIS) are considered to design iCorrelator. Its three-level structure is inspired by three types of responses in human immune system: the innate immune system's response, the adaptive immune system's primary response, and the adaptive immune system's secondary response. iCorrelator also uses the concepts of Danger theory to decrease the computational complexity of the correlation process without considerable accuracy degradation. By considering the importance of signals in Danger theory, a new alert selection policy is introduced. It is named Enhanced Random Directed Time Window (ERDTW) and is used to classify time slots to Relevant (Dangerous) and Irrelevant (Safe) slots based on the context information gathered during previous correlations. iCorrelator is evaluated using the DARPA 2000 dataset and a netForensics honeynet data. Completeness, soundness, false correlation rate and the execution time are investigated. Results show that iCorrelator generates attack graph with an acceptable accuracy that is comparable to the best known solutions. Moreover, inspiring by the Danger theory and using context information, the computational complexity of the correlation process is decreased considerably and makes it more applicable to online correlation.

This paper introduces a robust image watermarking technique for the copyright protection. The proposed method is based on 3-level discrete wavelet transform (DWT). Encoded secret image using spiral scanning is hidden by alpha blending technique in LL sub bands. During embedding process, secret image is dispersed within LL band depending upon alpha value. Encoded secret images are extracted and decoded to recover the original secret image. The experimental results demonstrate that the watermarks generated with the proposed algorithm are invisible and the quality of watermarked image and the recovered image are improved. The scheme is found robust to various image processing attacks such as JPEG compression, Gaussian noise, blurring, median filtering and rotation.

Mobile ad-hoc networks are networks that have properties of self configuration and multi hopping. These networks do not have any fixed infrastructure and need to be dynamic in nature. The specification of dynamism leads to various security breaches that a MANET suffers from such as impersonation, data modification etc. which results in degradation of performance and hence QOS is strongly affected. Hence this paper focuses on improving security performance of MANET by employing biometric technique in combination with cryptography, since biometric perception is specified as the most neoteric technological advancement which enhance security specifications of various networks by specifying exclusive human identification features. Cryptography is designed on computational hardness assumptions making various algorithms hard to break by an adversary. Simulation and experimental results specify that the proposed crypto-metric perception technique leads in achieving better QOS parameters by avoiding security intrusions hence better performance of mobile ad-hoc networks.