Biblio

Persisting to ignore the consequences of Cyber Warfare will bring severe concerns to all people. Hackers and governments alike should understand the barriers of which their methods take them. Governments use Cyber Warfare to give them a tactical advantage over other countries, defend themselves from their enemies or to inflict damage upon their adversaries. Hackers use Cyber Warfare to gain personal information, commit crimes, or to reveal sensitive and beneficial intelligence. Although both methods can provide ethical uses, the equivalent can be said at the other end of the spectrum. Knowing and comprehending these devices will not only strengthen the ability to detect these attacks and combat against them but will also provide means to divulge despotic government plans, as the outcome of Cyber Warfare can be worse than the outcome of conventional warfare. The paper discussed the concept of ethics and reasons that led to use information technology in military war, the effects of using cyber war on civilians, the legality of the cyber war and ways of controlling the use of information technology that may be used against civilians. This research uses a survey methodology to overlook the awareness of Arab citizens towards the idea of cyber war, provide findings and evidences of ethics behind the offensive cyber warfare. Detailed strategies and approaches should be developed in this aspect. The author recommended urging the scientific and technological research centers to improve the security and develop defending systems to prevent the use of technology in military war against civilians.
 

Programming languages have long incorporated type safety, increasing their level of abstraction and thus aiding programmers. Type safety eliminates whole classes of security-sensitive bugs, replacing the tedious and error-prone search for such bugs in each application with verifying the correctness of the type system. Despite their benefits, these protections often end at the process boundary, that is, type safety holds within a program but usually not to the file system or communication with other programs. Existing operating system approaches to bridge this gap require the use of a single programming language or common language runtime. We describe the deep integration of type safety in Ethos, a clean-slate operating system which requires that all program input and output satisfy a recognizer before applications are permitted to further process it. Ethos types are multilingual and runtime-agnostic, and each has an automatically generated unique type identifier. Ethos bridges the type-safety gap between programs by (1) providing a convenient mechanism for specifying the types each program may produce or consume, (2) ensuring that each type has a single, distributed-system-wide recognizer implementation, and (3) inescapably enforcing these type constraints.
 

The electric network frequency (ENF) signal can be captured in multimedia recordings due to electromagnetic influences from the power grid at the time of recording. Recent work has exploited the ENF signals for forensic applications, such as authenticating and detecting forgery of ENF-containing multimedia signals, and inferring their time and location of creation. In this paper, we explore a new potential of ENF signals for automatic synchronization of audio and video. The ENF signal as a time-varying random process can be used as a timing fingerprint of multimedia signals. Synchronization of audio and video recordings can be achieved by aligning their embedded ENF signals. We demonstrate the proposed scheme with two applications: multi-view video synchronization and synchronization of historical audio recordings. The experimental results show the ENF based synchronization approach is effective, and has the potential to solve problems that are intractable by other existing methods.

This paper propose a fast human detection algorithm of video surveillance in emergencies. Firstly through the background subtraction based on the single Guassian model and frame subtraction, we get the target mask which is optimized by Gaussian filter and dilation. Then the interest points of head is obtained from figures with target mask and edge detection. Finally according to detecting these pionts we can track the head and count the number of people with the frequence of moving target at the same place. Simulation results show that the algorithm can detect the moving object quickly and accurately.

Online fault diagnosis has been a crucial task for industrial processes. Reconstruction-based fault diagnosis has been drawing special attentions as a good alternative to the traditional contribution plot. It identifies the fault cause by finding the specific fault subspace that can well eliminate alarming signals from a bunch of alternatives that have been prepared based on historical fault data. However, in practice, the abnormality may result from the joint effects of multiple faults, which thus can not be well corrected by single fault subspace archived in the historical fault library. In the present work, an aggregative reconstruction-based fault diagnosis strategy is proposed to handle the case where multiple fault causes jointly contribute to the abnormal process behaviors. First, fault subspaces are extracted based on historical fault data in two different monitoring subspaces where analysis of relative changes is taken to enclose the major fault effects that are responsible for different alarming monitoring statistics. Then, a fault subspace selection strategy is developed to analyze the combinatorial fault nature which will sort and select the informative fault subspaces that are most likely to be responsible for the concerned abnormalities. Finally, an aggregative fault subspace is calculated by combining the selected fault subspaces which represents the joint effects from multiple faults and works as the final reconstruction model for online fault diagnosis. Theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with simulated multi-faults using data from the Tennessee Eastman (TE) benchmark process.
 

In this paper we address the problem of designing a fault tolerant control scheme for an HVAC control system where sensing and actuation data are exchanged with a centralized controller via a wireless sensors and actuators network where the communication nodes are subject to permanent failures and malicious intrusions.

This paper presents a novel design of content fingerprints based on maximization of the mutual information across the distortion channel. We use the information bottleneck method to optimize the filters and quantizers that generate these fingerprints. A greedy optimization scheme is used to select filters from a dictionary and allocate fingerprint bits. We test the performance of this method for audio fingerprinting and show substantial improvements over existing learning based fingerprints.

Fault-tolerance has huge impact on embedded safety-critical systems. As technology that assists to the development of such improvement, Safe Node Sequence Protocol (SNSP) is designed to make part of such impact. In this paper, we present a mechanism for fault-tolerance and recovery based on the Safe Node Sequence Protocol (SNSP) to strengthen the system robustness, from which the correctness of a fault-tolerant prototype system is analyzed and verified. In order to verify the correctness of more than thirty failure modes, we have partitioned the complete protocol state machine into several subsystems, followed to the injection of corresponding fault classes into dedicated independent models. Experiments demonstrate that this method effectively reduces the size of overall state space, and verification results indicate that the protocol is able to recover from the fault model in a fault-tolerant system and continue to operate as errors occur.
 

We consider several challenging problems in complex networks (communication, control, social, economic, biological, hybrid) as problems in cooperative multi-agent systems. We describe a general model for cooperative multi-agent systems that involves several interacting dynamic multigraphs and identify three fundamental research challenges underlying these systems from a network science perspective. We show that the framework of constrained coalitional network games captures in a fundamental way the basic tradeoff of benefits vs. cost of collaboration, in multi-agent systems, and demonstrate that it can explain network formation and the emergence or not of collaboration. Multi-metric problems in such networks are analyzed via a novel multiple partially ordered semirings approach. We investigate the interrelationship between the collaboration and communication multigraphs in cooperative swarms and the role of the communication topology, among the collaborating agents, in improving the performance of distributed task execution. Expander graphs emerge as efficient communication topologies for collaborative control. We relate these models and approaches to statistical physics.

Fingerprint-based Audio recognition system must address concurrent objectives. Indeed, fingerprints must be both robust to distortions and discriminative while their dimension must remain to allow fast comparison. This paper proposes to restate these objectives as a penalized sparse representation problem. On top of this dictionary-based approach, we propose a structured sparsity model in the form of a probabilistic distribution for the sparse support. A practical suboptimal greedy algorithm is then presented and evaluated on robustness and recognition tasks. We show that some existing methods can be seen as particular cases of this algorithm and that the general framework allows to reach other points of a Pareto-like continuum.

In any security system, there are many security issues that are related to either the sender or the receiver of the message. Quantum computing has proven to be a plausible approach to solving many security issues such as eavesdropping, replay attack and man-in-the-middle attack. In the e-voting system, one of these issues has been solved, namely, the integrity of the data (ballot). In this paper, we propose a scheme that solves the problem of repudiation that could occur when the voter denies the value of the ballot either for cheating purposes or for a real change in the value by a third party. By using an entanglement concept between two parties randomly, the person who is going to verify the ballots will create the entangled state and keep it in a database to use it in the future for the purpose of the non-repudiation of any of these two voters.

This paper discusses the detection of hardware Trojans (HTs) by their breaking of symmetries within integrated circuits (ICs), as measured by path delays. Typically, path delay or side channel methods rely on comparisons to a golden, or trusted, sample. However, golden standards are affected by inter-and intra-die variations which limit the confidence in such comparisons. Symmetry is a way to detect modifications to an IC with increased confidence by confirming subcircuit consistencies within as it was originally designed. The difference in delays from a given path to a set of symmetric paths will be the same unless an inserted HT breaks symmetry. Symmetry can naturally exist in ICs or be artificially added. We describe methods to find and measure path delays against symmetric paths, as well as the advantages and disadvantages of this method. We discuss results of examples from benchmark circuits demonstrating the detection of hardware Trojans.
 

Recently, the demand for more robust protection against unauthorized use of mobile devices has been rapidly growing. This paper presents a novel biometric modality Transient Evoked Otoacoustic Emission (TEOAE) for mobile security. Prior works have investigated TEOAE for biometrics in a setting where an individual is to be identified among a pre-enrolled identity gallery. However, this limits the applicability to mobile environment, where attacks in most cases are from imposters unknown to the system before. Therefore, we employ an unsupervised learning approach based on Autoencoder Neural Network to tackle such blind recognition problem. The learning model is trained upon a generic dataset and used to verify an individual in a random population. We also introduce the framework of mobile biometric system considering practical application. Experiments show the merits of the proposed method and system performance is further evaluated by cross-validation with an average EER 2.41% achieved.

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.

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.
 

Web Service (WS) plays an important role in today's word to provide effective services for humans and these web services are built with the standard of SOAP, WSDL & UDDI. This technology enables various service providers to register and service sender their intelligent agent based privacy preserving modelservices to utilize the service over the internet through pre established networks. Also accessing these services need to be secured and protected from various types of attacks in the network environment. Exchanging data between two applications on a secure channel is a challenging issue in today communication world. Traditional security mechanism such as secured socket layer (SSL), Transport Layer Security (TLS) and Internet Protocol Security (IP Sec) is able to resolve this problem partially, hence this research paper proposes the privacy preserving named as HTTPI to secure the communication more efficiently. This HTTPI protocol satisfies the QoS requirements, such as authentication, authorization, integrity and confidentiality in various levels of the OSI layers. This work also ensures the QoS that covers non functional characteristics like performance (throughput), response time, security, reliability and capacity. This proposed intelligent agent based model results in excellent throughput, good response time and increases the QoS requirements.
 

With the global widespread usage of the Internet, more and more cyber-attacks are being performed. Many of these attacks utilize IP address spoofing. This paper describes IP spoofing attacks and the proposed methods currently available to detect or prevent them. In addition, it presents a statistical analysis of the Hop Count parameter used in our proposed IP spoofing detection algorithm. We propose an algorithm, inspired by the Hop Count Filtering (HCF) technique, that changes the learning phase of HCF to include all the possible available Hop Count values. Compared to the original HCF method and its variants, our proposed method increases the true positive rate by at least 9% and consequently increases the overall accuracy of an intrusion detection system by at least 9%. Our proposed method performs in general better than HCF method and its variants.
 

Wireless sensor networks offer benefits in several applications but are vulnerable to various security threats, such as eavesdropping and hardware tampering. In order to reach secure communications among nodes, many approaches employ symmetric encryption. Several key management schemes have been proposed in order to establish symmetric keys. The paper presents an innovative key management scheme called random seed distribution with transitory master key, which adopts the random distribution of secret material and a transitory master key used to generate pairwise keys. The proposed approach addresses the main drawbacks of the previous approaches based on these techniques. Moreover, it overperforms the state-of-the-art protocols by providing always a high security level.

This paper discusses strategies for I/O sharing in Multiple Independent Levels of Security (MILS) systems mostly deployed in the special environment of avionic systems. MILS system designs are promising approaches for handling the increasing complexity of functionally integrated systems, where multiple applications run concurrently on the same hardware platform. Such integrated systems, also known as Integrated Modular Avionics (IMA) in the aviation industry, require communication to remote systems located outside of the hosting hardware platform. One possible solution is to provide each partition, the isolated runtime environment of an application, a direct interface to the communication's hardware controller. Nevertheless, this approach requires a special design of the hardware itself. This paper discusses efficient system architectures for I/O sharing in the environment of high-criticality embedded systems and the exemplary analysis of Free scale's proprietary Data Path Acceleration Architecture (DPAA) with respect to generic hardware requirements. Based on this analysis we also discuss the development of possible architectures matching with the MILS approach. Even though the analysis focuses on avionics it is equally applicable to automotive architectures such as Auto SAR.

Infrastructure-based Vehicular Networks can be applied in different social contexts, such as health care, transportation and entertainment. They can easily take advantage of the benefices provided by wireless mesh networks (WMNs) to mobility, since WMNs essentially support technological convergence and resilience, required for the effective operation of services and applications. However, infrastructure-based vehicular networks are prone to attacks such as ARP packets flooding that compromise mobility management and users' network access. Hence, this work proposes MIRF, a secure mobility scheme based on reputation and filtering to mitigate flooding attacks on mobility management. The efficiency of the MIRF scheme has been evaluated by simulations considering urban scenarios with and without attacks. Analyses show that it significantly improves the packet delivery ratio in scenarios with attacks, mitigating their intentional negative effects, as the reduction of malicious ARP requests. Furthermore, improvements have been observed in the number of handoffs on scenarios under attacks, being faster than scenarios without the scheme.

Data confidentiality can be effectively preserved through encryption. In certain situations, this is inadequate, as users may be coerced into disclosing their decryption keys. Steganographic techniques and deniable encryption algorithms have been devised to hide the very existence of encrypted data. We examine the feasibility and efficacy of deniable encryption for mobile devices. To address obstacles that can compromise plausibly deniable encryption (PDE) in a mobile environment, we design a system called Mobiflage. Mobiflage enables PDE on mobile devices by hiding encrypted volumes within random data in a devices free storage space. We leverage lessons learned from deniable encryption in the desktop environment, and design new countermeasures for threats specific to mobile systems. We provide two implementations for the Android OS, to assess the feasibility and performance of Mobiflage on different hardware profiles. MF-SD is designed for use on devices with FAT32 removable SD cards. Our MF-MTP variant supports devices that instead share a single internal partition for both apps and user accessible data. MF-MTP leverages certain Ext4 file system mechanisms and uses an adjusted data-block allocator. These new techniques for soring hidden volumes in Ext4 file systems can also be applied to other file systems to enable deniable encryption for desktop OSes and other mobile platforms.