Biblio

Information technology is continually changing, discoveries are made every other day. Cyber-physical systems consist of both physical and computational elements and are becoming more and more popular in today's society. They are complex systems, used in complex applications. Therefore, security is a critical and challenging aspect when developing cyber-physical systems. In this paper, we present a solution for ensuring data confidentiality and security by combining some of the most common methods in the area of security - cryptography and steganography. Furthermore, we use hierarchical access to information to ensure confidentiality and also increase the overall security of the cyber-physical system.
 

We present an analysis of a heuristic for abrupt change detection of systems with bounded state variations. The proposed analysis is based on the Singular Value Decomposition (SVD) of a history matrix built from system observations. We show that monitoring the largest singular value of the history matrix can be used as a heuristic for detecting abrupt changes in the system outputs. We provide sufficient detectability conditions for the proposed heuristic. As an application, we consider detecting malicious cyber data attacks on power systems and test our proposed heuristic on the IEEE 39-bus testbed.
 

The proliferation of digital devices in a networked industrial ecosystem, along with an exponential growth in complexity and scope, has resulted in elevated security concerns and management complexity issues. This paper describes a novel architecture utilizing concepts of autonomic computing and a simple object access protocol (SOAP)-based interface to metadata access points (IF-MAP) external communication layer to create a network security sensor. This approach simplifies integration of legacy software and supports a secure, scalable, and self-managed framework. The contribution of this paper is twofold: 1) A flexible two-level communication layer based on autonomic computing and service oriented architecture is detailed and 2) three complementary modules that dynamically reconfigure in response to a changing environment are presented. One module utilizes clustering and fuzzy logic to monitor traffic for abnormal behavior. Another module passively monitors network traffic and deploys deceptive virtual network hosts. These components of the sensor system were implemented in C++ and PERL and utilize a common internal D-Bus communication mechanism. A proof of concept prototype was deployed on a mixed-use test network showing the possible real-world applicability. In testing, 45 of the 46 network attached devices were recognized and 10 of the 12 emulated devices were created with specific operating system and port configurations. In addition, the anomaly detection algorithm achieved a 99.9% recognition rate. All output from the modules were correctly distributed using the common communication structure.

Designing a centralised group key management with minimal computation complexity to support dynamic secure multicast communication is a challenging issue in secure multimedia multicast. In this study, the authors propose a Chinese remainder theorem-based group key management scheme that drastically reduces computation complexity of the key server. The computation complexity of key server is reduced to O(1) in this proposed algorithm. Moreover, the computation complexity of group member is also minimised by performing one modulo division operation when a user join or leave operation is performed in a multicast group. The proposed algorithm has been implemented and tested using a key-star-based key management scheme and has been observed that this proposed algorithm reduces the computation complexity significantly.

Malicious hardware is a realistic threat. It can be possible to insert the malicious functionality on a device as deep as in the hardware design flow, long before manufacturing the silicon product. Towards developing a hardware Trojan horse detection methodology, we analyze capabilities and limitations of existing techniques, framing a testing strategy for uncovering efficiently hardware Trojan horses in mass-produced integrated circuits.
 

The modular exponentiation is an important operation for cryptographic transformations in public key cryptosystems like the Rivest, Shamir and Adleman, the Difie and Hellman and the ElGamal schemes. computing ax mod n and axby mod n for very large x,y and n are fundamental to the efficiency of almost all pubic key cryptosystems and digital signature schemes. To achieve high level of security, the word length in the modular exponentiations should be significantly large. The performance of public key cryptography is primarily determined by the implementation efficiency of the modular multiplication and exponentiation. As the words are usually large, and in order to optimize the time taken by these operations, it is essential to minimize the number of modular multiplications. In this paper we are presenting efficient algorithms for computing ax mod n and axbymod n. In this work we propose four algorithms to evaluate modular exponentiation. Bit forwarding (BFW) algorithms to compute ax mod n, and to compute axby mod n two algorithms namely Substitute and reward (SRW), Store and forward(SFW) are proposed. All the proposed algorithms are efficient in terms of time and at the same time demands only minimal additional space to store the pre-computed values. These algorithms are suitable for devices with low computational power and limited storage.
 

With the continuous growth of cyberinfrastructure throughout modern society, the need for secure computing and communication is more important than ever before. As a result, there is also an increasing need for entry-level developers who are capable of designing and building practical solutions for systems with stringent security requirements. This calls for careful attention to algorithm choice and implementation method, as well as trade-offs between hardware and software implementations. This article describes motivation and efforts taken by three departments at Rochester Institute of Technology (Computer Engineering, Computer Science, and Software Engineering) that were focused on creating a multidisciplinary course that integrates the algorithmic, engineering, and practical aspects of security as exemplified by applied cryptography. In particular, the article presents the structure of this new course, topics covered, lab tools and results from the first two spring quarter offerings in 2011 and 2012.

Botnets are a well recognized global cyber-security threat as they enable attack communities to command large collections of compromised computers (bots) on-demand. Peer to-peer (P2P) distributed hash tables (DHT) have become particularly attractive botnet command and control (C & C) solutions due to the high level resiliency gained via the diffused random graph overlays they produce. The injection of Sybils, computers pretending to be valid bots, remains a key defensive strategy against DHT-structured P2P botnets. This research uses packet level network simulations to explore the relative merits of random, informed, and partially informed Sybil placement strategies. It is shown that random placements perform nearly as effectively as the tested more informed strategies, which require higher levels of inter-defender co-ordination. Moreover, it is shown that aspects of the DHT-structured P2P botnets behave as statistically nonergodic processes, when viewed from the perspective of stochastic processes. This suggests that although optimal Sybil placement strategies appear to exist they would need carefully tuning to each specific P2P botnet instance.

The proliferation of digital devices in a networked industrial ecosystem, along with an exponential growth in complexity and scope, has resulted in elevated security concerns and management complexity issues. This paper describes a novel architecture utilizing concepts of autonomic computing and a simple object access protocol (SOAP)-based interface to metadata access points (IF-MAP) external communication layer to create a network security sensor. This approach simplifies integration of legacy software and supports a secure, scalable, and self-managed framework. The contribution of this paper is twofold: 1) A flexible two-level communication layer based on autonomic computing and service oriented architecture is detailed and 2) three complementary modules that dynamically reconfigure in response to a changing environment are presented. One module utilizes clustering and fuzzy logic to monitor traffic for abnormal behavior. Another module passively monitors network traffic and deploys deceptive virtual network hosts. These components of the sensor system were implemented in C++ and PERL and utilize a common internal D-Bus communication mechanism. A proof of concept prototype was deployed on a mixed-use test network showing the possible real-world applicability. In testing, 45 of the 46 network attached devices were recognized and 10 of the 12 emulated devices were created with specific operating system and port configurations. In addition, the anomaly detection algorithm achieved a 99.9% recognition rate. All output from the modules were correctly distributed using the common communication structure.

Recent advances in adaptive filter theory and the hardware for signal acquisition have led to the realization that purely linear algorithms are often not adequate in these domains. Nonlinearities in the input space have become apparent with today's real world problems. Algorithms that process the data must keep pace with the advances in signal acquisition. Recently kernel adaptive (online) filtering algorithms have been proposed that make no assumptions regarding the linearity of the input space. Additionally, advances in wavelet data compression/dimension reduction have also led to new algorithms that are appropriate for producing a hybrid nonlinear filtering framework. In this paper we utilize a combination of wavelet dimension reduction and kernel adaptive filtering. We derive algorithms in which the dimension of the data is reduced by a wavelet transform. We follow this by kernel adaptive filtering algorithms on the reduced-domain data to find the appropriate model parameters demonstrating improved minimization of the mean-squared error (MSE). Another important feature of our methods is that the wavelet filter is also chosen based on the data, on-the-fly. In particular, it is shown that by using a few optimal wavelet coefficients from the constructed wavelet filter for both training and testing data sets as the input to the kernel adaptive filter, convergence to the near optimal learning curve (MSE) results. We demonstrate these algorithms on simulated and a real data set from food processing.

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.

Image inpainting is the process of filling the unwanted region in an image marked by the user. It is used for restoring old paintings and photographs, removal of red eyes from pictures, etc. In this paper, we propose an efficient inpainting algorithm which takes care of false edge propagation. We use the classical exemplar based technique to find out the priority term for each patch. To ensure that the edge content of the nearest neighbor patch found by minimizing L2 distance between patches, we impose an additional constraint that the entropy of the patches be similar. Entropy of the patch acts as a good measure of edge content. Additionally, we fill the image by considering overlapping patches to ensure smoothness in the output. We use structural similarity index as the measure of similarity between ground truth and inpainted image. The results of the proposed approach on a number of examples on real and synthetic images show the effectiveness of our algorithm in removing objects and thin scratches or text written on image. It is also shown that the proposed approach is robust to the shape of the manually selected target. Our results compare favorably to those obtained by existing techniques.
 

Formal methods, models and tools for social big data analytics are largely limited to graph theoretical approaches such as social network analysis (SNA) informed by relational sociology. There are no other unified modeling approaches to social big data that integrate the conceptual, formal and software realms. In this paper, we first present and discuss a theory and conceptual model of social data. Second, we outline a formal model based on set theory and discuss the semantics of the formal model with a real-world social data example from Facebook. Third, we briefly present and discuss the Social Data Analytics Tool (SODATO) that realizes the conceptual model in software and provisions social data analysis based on the conceptual and formal models. Fourth and last, based on the formal model and sentiment analysis of text, we present a method for profiling of artifacts and actors and apply this technique to the data analysis of big social data collected from Facebook page of the fast fashion company, H&M.
 

In this paper, an algorithm is proposed to automatically produce hierarchical graph-based representations of maritime shipping lanes extrapolated from historical vessel positioning data. Each shipping lane is generated based on the detection of the vessel behavioural changes and represented in a compact synthetic route composed of the network nodes and route segments. The outcome of the knowledge discovery process is a geographical maritime network that can be used in Maritime Situational Awareness (MSA) applications such as track reconstruction from missing information, situation/destination prediction, and detection of anomalous behaviour. Experimental results are presented, testing the algorithm in a specific scenario of interest, the Dover Strait.
 

To protect complex power-grid control networks, power operators need efficient security assessment techniques that take into account both cyber side and the power side of the cyber-physical critical infrastructures. In this paper, we present CPINDEX, a security-oriented stochastic risk management technique that calculates cyber-physical security indices to measure the security level of the underlying cyber-physical setting. CPINDEX installs appropriate cyber-side instrumentation probes on individual host systems to dynamically capture and profile low-level system activities such as interprocess communications among operating system assets. CPINDEX uses the generated logs along with the topological information about the power network configuration to build stochastic Bayesian network models of the whole cyber-physical infrastructure and update them dynamically based on the current state of the underlying power system. Finally, CPINDEX implements belief propagation algorithms on the created stochastic models combined with a novel graph-theoretic power system indexing algorithm to calculate the cyber-physical index, i.e., to measure the security-level of the system's current cyber-physical state. The results of our experiments with actual attacks against a real-world power control network shows that CPINDEX, within few seconds, can efficiently compute the numerical indices during the attack that indicate the progressing malicious attack correctly.
 

Very high resolution satellite imagery used to be a rare commodity, with infrequent satellite pass-over times over a specific area-of-interest obviating many useful applications. Today, more and more such satellite systems are available, with visual analysis and interpretation of imagery still important to derive relevant features and changes from satellite data. In order to allow efficient, robust and routine image analysis for humanitarian purposes, semi-automated feature extraction is of increasing importance for operational emergency mapping tasks. In the frame of the European Earth Observation program COPERNICUS and related research activities under the European Union's Seventh Framework Program, substantial scientific developments and mapping services are dedicated to satellite based humanitarian mapping and monitoring. In this paper, recent results in methodological research and development of routine services in satellite mapping for humanitarian situational awareness are reviewed and discussed. Ethical aspects of sensitivity and security of humanitarian mapping are deliberated. Furthermore methods for monitoring and analysis of refugee/internally displaced persons camps in humanitarian settings are assessed. Advantages and limitations of object-based image analysis, sample supervised segmentation and feature extraction are presented and discussed.
 

In wireless networks, spoofing attack is one of the most common and challenging attacks. Due to these attacks the overall network performance would be degraded. In this paper, a medoid based clustering approach has been proposed to detect a multiple spoofing attacks in wireless networks. In addition, a Enhanced Partitioning Around Medoid (EPAM) with average silhouette has been integrated with the clustering mechanism to detect a multiple spoofing attacks with a higher accuracy rate. Based on the proposed method, the received signal strength based clustering approach has been adopted for medoid clustering for detection of attacks. In order to prevent the multiple spoofing attacks, dynamic MAC address allocation scheme using MD5 hashing technique is implemented. The experimental results shows, the proposed method can detect spoofing attacks with high accuracy rate and prevent the attacks. Thus the overall network performance is improved with high accuracy rate.
 

We provide a generic framework that, with the help of a preprocessing phase that is independent of the inputs of the users, allows an arbitrary number of users to securely outsource a computation to two non-colluding external servers. Our approach is shown to be provably secure in an adversarial model where one of the servers may arbitrarily deviate from the protocol specification, as well as employ an arbitrary number of dummy users. We use these techniques to implement a secure recommender system based on collaborative filtering that becomes more secure, and significantly more efficient than previously known implementations of such systems, when the preprocessing efforts are excluded. We suggest different alternatives for preprocessing, and discuss their merits and demerits.

Educational software systems have an increasingly significant presence in engineering sciences. They aim to improve students' attitudes and knowledge acquisition typically through visual representation and simulation of complex algorithms and mechanisms or hardware systems that are often not available to the educational institutions. This paper presents a novel software system for CryptOgraphic ALgorithm visuAl representation (COALA), which was developed to support a Data Security course at the School of Electrical Engineering, University of Belgrade. The system allows users to follow the execution of several complex algorithms (DES, AES, RSA, and Diffie-Hellman) on real world examples in a step by step detailed view with the possibility of forward and backward navigation. Benefits of the COALA system for students are observed through the increase of the percentage of students who passed the exam and the average grade on the exams during one school year.
 

Future networks may change the way how network administrators monitor and account their users. History shows that usually a completely new design (clean slate) is used to propose a new network architecture - e.g. Network Control Protocol to TCP/IP, IPv4 to IPv6 or IP to Recursive Inter Network Architecture. The incompatibility between these architectures changes the user accounting process as network administrators have to use different information to identify a user. The paper presents a methodology how it is possible to gather all necessary information needed for smooth transition between two incompatible architectures. The transition from IPv4 and IPv6 is used as a use case, but it should be able to use the same process with any new networking architecture.
 

The deployment and operation of global network architectures can exhibit complex, dynamic behavior and the comprehensive validation of their properties, without actually building and running the systems, can only be achieved with the help of simulations. Packet-level models are not feasible in the Internet scale, but we are still interested in the phenomena that emerge when the systems are run in their intended environment. We argue for the high-level simulation methodology and introduce a simulation environment based on aggregate models built on state-of-the-art datasets available while respecting invariants observed in measurements. The models developed are aimed at studying a clean slate name-based interdomain routing architecture and provide an abundance of parameters for sensitivity analysis and a modular design with a balanced level of detail in different aspects of the model. In addition to introducing several reusable models for traffic, topology, and deployment, we report our experiences in using the high-level simulation approach and potential pitfalls related to it.
 

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call “Smart-Frame.” The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.
 

During recent years, establishing proper metrics for measuring system security has received increasing attention. Security logs contain vast amounts of information which are essential for creating many security metrics. Unfortunately, security logs are known to be very large, making their analysis a difficult task. Furthermore, recent security metrics research has focused on generic concepts, and the issue of collecting security metrics with log analysis methods has not been well studied. In this paper, we will first focus on using log analysis techniques for collecting technical security metrics from security logs of common types (e.g., Network IDS alarm logs, workstation logs, and Net flow data sets). We will also describe a production framework for collecting and reporting technical security metrics which is based on novel open-source technologies for big data.
 

In the future Internet of Things, it is envisioned that things are collaborating to serve people. Unfortunately, this vision could not be realised without relations between things and people. To solve the problem this paper proposes a user centric identity management system that incorporates user identity, device identity and the relations between them. The proposed IDM system is user centric and allows device authentication and authorization based on the user identity. A typical compelling use case of the proposed solution is also given.

Cloud computing is a new paradigm and emerged technology for hosting and delivering resources over a network such as internet by using concepts of virtualization, processing power and storage. However, many challenging issues are still unclear in cloud-based environments and decrease the rate of reliability and efficiency for service providers and users. User Authentication is one of the most challenging issues in cloud-based environments and according to this issue this paper proposes an efficient user authentication model that involves both of defined phases during registration and accessing processes. Geo Detection and Digital Signature Authorization (GD2SA) is a user authentication tool for provisional access permission in cloud computing environments. The main aim of GD2SA is to compare the location of an un-registered device with the location of the user by using his belonging devices (e.g. smart phone). In addition, this authentication algorithm uses the digital signature of account owner to verify the identity of applicant. This model has been evaluated in this paper according to three main parameters: efficiency, scalability, and security. In overall, the theoretical analysis of the proposed model showed that it can increase the rate of efficiency and reliability in cloud computing as an emerging technology.