Biblio

This paper proposes and describes an active authentication model based on user profiles built from user-issued commands when interacting with GUI-based application. Previous behavioral models derived from user issued commands were limited to analyzing the user's interaction with the *Nix (Linux or Unix) command shell program. Human-computer interaction (HCI) research has explored the idea of building users profiles based on their behavioral patterns when interacting with such graphical interfaces. It did so by analyzing the user's keystroke and/or mouse dynamics. However, none had explored the idea of creating profiles by capturing users' usage characteristics when interacting with a specific application beyond how a user strikes the keyboard or moves the mouse across the screen. We obtain and utilize a dataset of user command streams collected from working with Microsoft (MS) Word to serve as a test bed. User profiles are first built using MS Word commands and identification takes place using machine learning algorithms. Best performance in terms of both accuracy and Area under the Curve (AUC) for Receiver Operating Characteristic (ROC) curve is reported using Random Forests (RF) and AdaBoost with random forests.

In recent years, with growing demands towards big data application, various research on context-awareness has once again become active. This paper proposes a new type of context-aware user authentication that controls the authentication level of users, using the context of “physical trust relationship” that is built between users by visual contact. In our proposal, the authentication control is carried out by two mechanisms; “i-Contact” and “k-Contact”. i-Contact is the mechanism that visually confirms the user (owner of a mobile device) using the surrounding users' eyes. The authenticity of users can be reliably assessed by the people (witnesses), even when the user exhibits ambiguous behavior. k-Contact is the mechanism that dynamically changes the authentication level of each user using the context information collected through i-Contact. Once a user is authenticated by eyewitness reports, the user is no longer prompted for a password to unlock his/her mobile device and/or to access confidential resources. Thus, by leveraging the proposed authentication system, the usability for only trusted users can be securely enhanced. At the same time, our proposal anticipates the promotion of physical social communication as face-to-face communication between users is triggered by the proposed authentication system.
 

Revolution in the field of technology leads to the development of cloud computing which delivers on-demand and easy access to the large shared pools of online stored data, softwares and applications. It has changed the way of utilizing the IT resources but at the compromised cost of security breaches as well such as phishing attacks, impersonation, lack of confidentiality and integrity. Thus this research work deals with the core problem of providing absolute security to the mobile consumers of public cloud to improve the mobility of user's, accessing data stored on public cloud securely using tokens without depending upon the third party to generate them. This paper presents the approach of simplifying the process of authenticating and authorizing the mobile user's by implementing middleware-centric framework called MiLAMob model with the huge online data storage system i.e. HDFS. It allows the consumer's to access the data from HDFS via mobiles or through the social networking sites eg. facebook, gmail, yahoo etc using OAuth 2.0 protocol. For authentication, the tokens are generated using one-time password generation technique and then encrypting them using AES method. By implementing the flexible user based policies and standards, this model improves the authorization process.

In the present paper, we present our approach for the transformation of workflow applications based on institution theory. The workflow application is modeled with UML Activity Diagram(UML AD). Then, for a formal verification purposes, the graphical model will be translated to an Event-B specification. Institution theory will be used in two levels. First, we defined a local semantic for UML AD and Event B specification using a categorical description of each one. Second, we defined institution comorphism to link the two defined institutions. The theoretical foundations of our approach will be studied in the same mathematical framework since the use of institution theory. The resulted Event-B specification, after applying the transformation approach, will be used for the formal verification of functional proprieties and the verification of absences of problems such deadlock. Additionally, with the institution comorphism, we define a semantic correctness and coherence of the model transformation.

Cyber intrusions to substations of a power grid are a source of vulnerability since most substations are unmanned and with limited protection of the physical security. In the worst case, simultaneous intrusions into multiple substations can lead to severe cascading events, causing catastrophic power outages. In this paper, an integrated Anomaly Detection System (ADS) is proposed which contains host- and network-based anomaly detection systems for the substations, and simultaneous anomaly detection for multiple substations. Potential scenarios of simultaneous intrusions into the substations have been simulated using a substation automation testbed. The host-based anomaly detection considers temporal anomalies in the substation facilities, e.g., user-interfaces, Intelligent Electronic Devices (IEDs) and circuit breakers. The malicious behaviors of substation automation based on multicast messages, e.g., Generic Object Oriented Substation Event (GOOSE) and Sampled Measured Value (SMV), are incorporated in the proposed network-based anomaly detection. The proposed simultaneous intrusion detection method is able to identify the same type of attacks at multiple substations and their locations. The result is a new integrated tool for detection and mitigation of cyber intrusions at a single substation or multiple substations of a power grid.

Cyber intrusions to substations of a power grid are a source of vulnerability since most substations are unmanned and with limited protection of the physical security. In the worst case, simultaneous intrusions into multiple substations can lead to severe cascading events, causing catastrophic power outages. In this paper, an integrated Anomaly Detection System (ADS) is proposed which contains host- and network-based anomaly detection systems for the substations, and simultaneous anomaly detection for multiple substations. Potential scenarios of simultaneous intrusions into the substations have been simulated using a substation automation testbed. The host-based anomaly detection considers temporal anomalies in the substation facilities, e.g., user-interfaces, Intelligent Electronic Devices (IEDs) and circuit breakers. The malicious behaviors of substation automation based on multicast messages, e.g., Generic Object Oriented Substation Event (GOOSE) and Sampled Measured Value (SMV), are incorporated in the proposed network-based anomaly detection. The proposed simultaneous intrusion detection method is able to identify the same type of attacks at multiple substations and their locations. The result is a new integrated tool for detection and mitigation of cyber intrusions at a single substation or multiple substations of a power grid.
 

Owing to dynamic topology changes in mobile ad hoc networks (MANETs), nodes have the freedom of movement. This characteristic necessitates the process of rekeying to secure multicast transmission. Furthermore, a secure inter cluster communication technique is also mandatory to improve the performance of multicast transmission. In this paper, we propose an inter cluster communication and rekeying technique for multicast security in MANET. The technique facilitates inter cluster communication by distributing private key shares to the nodes, which is performed by the centralised key manager. By tamper proofing the data using private key share, inter cluster communication is accomplished. Furthermore, the rekeying mechanism is invoked when a node joins the cluster. Our rekeying technique incurs low overhead and computation cost. Our technique is simulated in network simulator tool. The simulation results show the proficiency of our technique.

This paper deals with the design and implementation of the post-quantum public-key algorithm McEliece. Seamless incorporation of a new error generator and new SHA-3 module provides higher indeterminacy and more randomization of the original McEliece algorithm and achieves CCA2 security standard. Due to the lightweight and high-speed implementation of SHA-3 module the proposed 128-bit secure McEliece architecture provides 6% higher performance in only 0.78 times area of the best known existing design.
 

Establishing trust relationships between network participants by having them prove their operating system's integrity via a Trusted Platform Module (TPM) provides interesting approaches for securing local networks at a higher level. In the introduced approach on OSI layer 2, attacks carried out by already authenticated and participating nodes (insider threats) can be detected and prevented. Forbidden activities and manipulations in hard- and software, such as executing unknown binaries, loading additional kernel modules or even inserting unauthorized USB devices, are detected and result in an autonomous reaction of each network participant. The provided trust establishment and authentication protocol operates independently from upper protocol layers and is optimized for resource constrained machines. Well known concepts of backbone architectures can maintain the chain of trust between different kinds of network types. Each endpoint, forwarding and processing unit monitors the internal network independently and reports misbehaviours autonomously to a central instance in or outside of the trusted network.

Physical impairments in long-haul optical networks mandate that optical signals be regenerated within the (so-called translucent) network. Being expensive devices, regenerators are expected to be allocated sparsely and must be judiciously utilized. Next-generation optical-transport networks will include multiple domains with diverse technologies, protocols, granularities, and carriers. Because of confidentiality and scalability concerns, the scope of network-state information (e.g., topology, wavelength availability) may be limited to within a domain. In such networks, the problem of routing and wavelength assignment (RWA) aims to find an adequate route and wavelength(s) for lightpaths carrying end-to-end service demands. Some state information may have to be explicitly exchanged among the domains to facilitate the RWA process. The challenge is to determine which information is the most critical and make a wise choice for the path and wavelength(s) using the limited information. Recently, a framework for multidomain path computation called backward-recursive path-computation (BRPC) was standardized by the Internet Engineering Task Force. In this paper, we consider the RWA problem for connections within a single domain and interdomain connections so that the quality of transmission (QoT) requirement of each connection is satisfied, and the network-level performance metric of blocking probability is minimized. Cross-layer heuristics that are based on dynamic programming to effectively allocate the sparse regenerators are developed, and extensive simulation results are presented to demonstrate their effectiveness.

The convergence of the Internet and mobile computing enables personalised access to online services anywhere and anytime. This potent access capability creates opportunities for new business models which stimulates vigorous investment and rapid innovation. Unfortunately, this innovation also produces new vulnerabilities and threats, and the new business models also create incentives for attacks, because criminals will always follow the money. Unless the new threats are balanced with appropriate countermeasures, growth in the Internet and mobile services will encounter painful setbacks. Security and trust are two fundamental factors for sustainable development of identity management in online markets and communities. The aim of this study is to present an overview of the central aspects of identity management in the Internet and mobile computing with respect to security and trust.

Checking remote data possession is of crucial importance in public cloud storage. It enables the users to check whether their outsourced data have been kept intact without downloading the original data. The existing remote data possession checking (RDPC) protocols have been designed in the PKI (public key infrastructure) setting. The cloud server has to validate the users' certificates before storing the data uploaded by the users in order to prevent spam. This incurs considerable costs since numerous users may frequently upload data to the cloud server. This study addresses this problem with a new model of identity-based RDPC (ID-RDPC) protocols. The authors present the first ID-RDPC protocol proven to be secure assuming the hardness of the standard computational Diffie-Hellman problem. In addition to the structural advantage of elimination of certificate management and verification, the authors ID-RDPC protocol also outperforms the existing RDPC protocols in the PKI setting in terms of computation and communication.
 

Sampling and reconstruction (S&R) are used in virtually all areas of science and technology. The classical sampling theorem is a theoretical foundation of S&R. However, for a long time, only sampling rates and ways of the sampled signals representation were derived from it. The fact that the design of S&R circuits (SCs and RCs) is based on a certain interpretation of the sampling theorem was mostly forgotten. The traditional interpretation of this theorem was selected at the time of the theorem introduction because it offered the only feasible way of S&R realization then. At that time, its drawbacks did not manifest themselves. By now, this interpretation has largely exhausted its potential and inhibits future progress in the field. This tutorial expands the theoretical foundation of S&R. It shows that the traditional interpretation, which is indirect, can be replaced by the direct one or by various combinations of the direct and indirect interpretations that enable development of novel SCs and RCs (NSCs and NRCs) with advanced properties. The tutorial explains the basic principles of the NSCs and NRCs design, their advantages, as well as theoretical problems and practical challenges of their realization. The influence of the NSCs and NRCs on the architectures of SDRs and CRs is also discussed.

There is an increasing need for wireless sensor networks (WSNs) to be more tightly integrated with the Internet. Several real world deployment of stand-alone wireless sensor networks exists. A number of solutions have been proposed to address the security threats in these WSNs. However, integrating WSNs with the Internet in such a way as to ensure a secure End-to-End (E2E) communication path between IPv6 enabled sensor networks and the Internet remains an open research issue. In this paper, the 6LoWPAN adaptation layer was extended to support both IPsec's Authentication Header (AH) and Encapsulation Security Payload (ESP). Thus, the communication endpoints in WSNs are able to communicate securely using encryption and authentication. The proposed AH and ESP compressed headers performance are evaluated via test-bed implementation in 6LoWPAN for IPv6 communications on IEEE 802.15.4 networks. The results confirm the possibility of implementing E2E security in IPv6 enabled WSNs to create a smooth transition between WSNs and the Internet. This can potentially play a big role in the emerging "Internet of Things" paradigm.

The dazzling emergence of cyber-threats exert today's cyberspace, which needs practical and efficient capabilities for malware traffic detection. In this paper, we propose an extension to an initial research effort, namely, towards fingerprinting malicious traffic by putting an emphasis on the attribution of maliciousness to malware families. The proposed technique in the previous work establishes a synergy between automatic dynamic analysis of malware and machine learning to fingerprint badness in network traffic. Machine learning algorithms are used with features that exploit only high-level properties of traffic packets (e.g. packet headers). Besides, the detection of malicious packets, we want to enhance fingerprinting capability with the identification of malware families responsible in the generation of malicious packets. The identification of the underlying malware family is derived from a sequence of application protocols, which is used as a signature to the family in question. Furthermore, our results show that our technique achieves promising malware family identification rate with low false positives.

The dazzling emergence of cyber-threats exert today's cyberspace, which needs practical and efficient capabilities for malware traffic detection. In this paper, we propose an extension to an initial research effort, namely, towards fingerprinting malicious traffic by putting an emphasis on the attribution of maliciousness to malware families. The proposed technique in the previous work establishes a synergy between automatic dynamic analysis of malware and machine learning to fingerprint badness in network traffic. Machine learning algorithms are used with features that exploit only high-level properties of traffic packets (e.g. packet headers). Besides, the detection of malicious packets, we want to enhance fingerprinting capability with the identification of malware families responsible in the generation of malicious packets. The identification of the underlying malware family is derived from a sequence of application protocols, which is used as a signature to the family in question. Furthermore, our results show that our technique achieves promising malware family identification rate with low false positives.

The dazzling emergence of cyber-threats exert today's cyberspace, which needs practical and efficient capabilities for malware traffic detection. In this paper, we propose an extension to an initial research effort, namely, towards fingerprinting malicious traffic by putting an emphasis on the attribution of maliciousness to malware families. The proposed technique in the previous work establishes a synergy between automatic dynamic analysis of malware and machine learning to fingerprint badness in network traffic. Machine learning algorithms are used with features that exploit only high-level properties of traffic packets (e.g. packet headers). Besides, the detection of malicious packets, we want to enhance fingerprinting capability with the identification of malware families responsible in the generation of malicious packets. The identification of the underlying malware family is derived from a sequence of application protocols, which is used as a signature to the family in question. Furthermore, our results show that our technique achieves promising malware family identification rate with low false positives.

The security issue of complex networks has drawn significant concerns recently. While pure topological analyzes from a network security perspective provide some effective techniques, their inability to characterize the physical principles requires a more comprehensive model to approximate failure behavior of a complex network in reality. In this paper, based on an extended topological metric, we proposed an approach to examine the vulnerability of a specific type of complex network, i.e., the power system, against cascading failure threats. The proposed approach adopts a model called extended betweenness that combines network structure with electrical characteristics to define the load of power grid components. By using this power transfer distribution factor-based model, we simulated attacks on different components (buses and branches) in the grid and evaluated the vulnerability of the system components with an extended topological cascading failure simulator. Influence of different loading and overloading situations on cascading failures was also evaluated by testing different tolerance factors. Simulation results from a standard IEEE 118-bus test system revealed the vulnerability of network components, which was then validated on a dc power flow simulator with comparisons to other topological measurements. Finally, potential extensions of the approach were also discussed to exhibit both utility and challenge in more complex scenarios and applications.

Today's more reliable communication technology, together with the availability of higher computational power, have paved the way for introduction of more advanced automation systems based on distributed intelligence and multi-agent technology. However, abundance of data, while making these systems more powerful, can at the same time act as their biggest vulnerability. In a web of interconnected devices and components functioning within an automation framework, potential impact of malfunction in a single device, either through internal failure or external damage/intrusion, may lead to detrimental side-effects spread across the whole underlying system. The potentially large number of devices, along with their inherent interrelations and interdependencies, may hinder the ability of human operators to interpret events, identify their scope of impact and take remedial actions if necessary. Through utilization of the concepts of graph-theoretic fuzzy cognitive maps (FCM) and expert systems, this paper puts forth a solution that is able to reveal weak links and vulnerabilities of an automation system, should it become exposed to partial internal failure or external damage. A case study has been performed on the IEEE 34-bus test distribution system to show the efficiency of the proposed scheme.

Many surveillance cameras are using everywhere, the videos or images captured by these cameras are still dumped but they are not processed. Many methods are proposed for tracking and detecting the objects in the videos but we need the meaningful content called semantic content from these videos. Detecting Human activity recognition is quite complex. The proposed method called Semantic Content Extraction (SCE) from videos is used to identify the objects and the events present in the video. This model provides useful methodology for intruder detecting systems which provides the behavior and the activities performed by the intruder. Construction of ontology enhances the spatial and temporal relations between the objects or features extracted. Thus proposed system provides a best way for detecting the intruders, thieves and malpractices happening around us.

The success of the IoT world requires service provision attributed with ubiquity, reliability, high-performance, efficiency, and scalability. In order to accomplish this attribution, future business and research vision is to merge the Cloud Computing and IoT concepts, i.e., enable an “Everything as a Service” model: specifically, a Cloud ecosystem, encompassing novel functionality and cognitive-IoT capabilities, will be provided. Hence the paper will describe an innovative IoT centric Cloud smart infrastructure addressing individual IoT and Cloud Computing challenges.
 

This paper presents a novel architecture to manage identity and access (IAM) in a Multi-tier cloud infrastructure, in which most services are supported by massive-scale data centres over the Internet. Multi-tier cloud infrastructure uses tier-based model from Software Engineering to provide resources in different tires. In this paper we focus on design and implementation of a centralized identity and access management system for the multi-tier cloud infrastructure. First, we discuss identity and access management requirements in such an environment and propose our solution to address these requirements. Next, we discuss approaches to improve performance of the IAM system and make it scalable to billions of users. Finally, we present experimental results based on the current deployment in the SAVI Testbed. We show that our IAM system outperforms the previously proposed IAM systems for cloud infrastructure by factor 9 in throughput when the number of users is small, it handle about 50 times more requests in peak usage. Because our architecture is a combination of Green-thread and load balanced process, it uses less systems resources, and easily scales up to address high number of requests.
 

Hash functions, such as SHA (secure hash algorithm) and MD (message digest) families that are built upon Merkle-Damgard construction, suffer many attacks due to the iterative nature of block-by-block message processing. Chum and Zhang [4] proposed a new hash function construction that takes advantage of the randomize-then-combine technique, which was used in the incremental hash functions, to the iterative hash function. In this paper, we implement such hash construction in three ways distinguished by their corresponding padding methods. We conduct the experiment in parallel multi-threaded programming settings. The results show that the speed of proposed hash function is no worse than SHA1.
 

An improved harmony search algorithm is presented for solving continuous optimization problems in this paper. In the proposed algorithm, an elimination principle is developed for choosing from the harmony memory, so that the harmonies with better fitness will have more opportunities to be selected in generating new harmonies. Two key control parameters, pitch adjustment rate (PAR) and bandwidth distance (bw), are dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process with the different search spaces of the optimization problems. Numerical results of 12 benchmark problems show that the proposed algorithm performs more effectively than the existing HS variants in finding better solutions.

Sensors of diverse capabilities and modalities, carried by us or deeply embedded in the physical world, have invaded our personal, social, work, and urban spaces. Our relationship with these sensors is a complicated one. On the one hand, these sensors collect rich data that are shared and disseminated, often initiated by us, with a broad array of service providers, interest groups, friends, and family. Embedded in this data is information that can be used to algorithmically construct a virtual biography of our activities, revealing intimate behaviors and lifestyle patterns. On the other hand, we and the services we use, increasingly depend directly and indirectly on information originating from these sensors for making a variety of decisions, both routine and critical, in our lives. The quality of these decisions and our confidence in them depend directly on the quality of the sensory information and our trust in the sources. Sophisticated adversaries, benefiting from the same technology advances as the sensing systems, can manipulate sensory sources and analyze data in subtle ways to extract sensitive knowledge, cause erroneous inferences, and subvert decisions. The consequences of these compromises will only amplify as our society increasingly complex human-cyber-physical systems with increased reliance on sensory information and real-time decision cycles.Drawing upon examples of this two-faceted relationship with sensors in applications such as mobile health and sustainable buildings, this talk will discuss the challenges inherent in designing a sensor information flow and processing architecture that is sensitive to the concerns of both producers and consumer. For the pervasive sensing infrastructure to be trusted by both, it must be robust to active adversaries who are deceptively extracting private information, manipulating beliefs and subverting decisions. While completely solving these challenges would require a new science of resilient, secure and trustworthy networked sensing and decision systems that would combine hitherto disciplines of distributed embedded systems, network science, control theory, security, behavioral science, and game theory, this talk will provide some initial ideas. These include an approach to enabling privacy-utility trade-offs that balance the tension between risk of information sharing to the producer and the value of information sharing to the consumer, and method to secure systems against physical manipulation of sensed information.