Biblio

So far, cloud storage has been accepted by an increasing number of people, which is not a fresh notion any more. It brings cloud users a lot of conveniences, such as the relief of local storage and location independent access. Nevertheless, the correctness and completeness as well as the privacy of outsourced data are what worry could users. As a result, most people are unwilling to store data in the cloud, in case that the sensitive information concerning something important is disclosed. Only when people feel worry-free, can they accept cloud storage more easily. Certainly, many experts have taken this problem into consideration, and tried to solve it. In this paper, we survey the solutions to the problems concerning auditing in cloud computing and give a comparison of them. The methods and performances as well as the pros and cons are discussed for the state-of-the-art auditing protocols.

Despite its great importance, modern network infrastructure is remarkable for the lack of rigor in its engineering. The Internet, which began as a research experiment, was never designed to handle the users and applications it hosts today. The lack of formalization of the Internet architecture meant limited abstractions and modularity, particularly for the control and management planes, thus requiring for every new need a new protocol built from scratch. This led to an unwieldy ossified Internet architecture resistant to any attempts at formal verification and to an Internet culture where expediency and pragmatism are favored over formal correctness. Fortunately, recent work in the space of clean slate Internet design-in particular, the software defined networking (SDN) paradigm-offers the Internet community another chance to develop the right kind of architecture and abstractions. This has also led to a great resurgence in interest of applying formal methods to specification, verification, and synthesis of networking protocols and applications. In this paper, we present a self-contained tutorial of the formidable amount of work that has been done in formal methods and present a survey of its applications to networking.

The security of cyber-physical systems is of paramount importance because of their pervasiveness in the critical infrastructure. Protecting cyber-physical systems greatly depends on a deep understanding of the possible attacks and their properties. The prerequisite for quantitative and qualitative analyses of attacks is a knowledge base containing attack descriptions. The structure of the attack descriptions is the indispensable foundation of the knowledge base.

This paper introduces the Cyber-Physical Attack Description Language (CP-ADL), which lays a cornerstone for the structured description of attacks on cyber-physical systems. The core of the language is a taxonomy of attacks on cyber-physical systems. The taxonomy specifies the semantically distinct aspects of attacks on cyber-physical systems that should be described. CP-ADL extends the taxonomy with the means to describe relationships between semantically distinct aspects, despite the complex relationships that exist for attacks on cyber-physical systems. The language is capable of expressing relationships between attack descriptions, including the links between attack steps and the folding of attack details.

The limited battery lifetime and rapidly increasing functionality of portable multimedia devices demand energy-efficient designs. The filters employed mainly in these devices are based on Gaussian smoothing, which is slow and, severely affects the performance. In this paper, we propose a novel energy-efficient approximate 2D Gaussian smoothing filter (2D-GSF) architecture by exploiting "nearest pixel approximation" and rounding-off Gaussian kernel coefficients. The proposed architecture significantly improves Speed-Power-Area-Accuracy (SPAA) metrics in designing energy-efficient filters. The efficacy of the proposed approximate 2D-GSF is demonstrated on real application such as edge detection. The simulation results show 72%, 79% and 76% reduction in area, power and delay, respectively with acceptable 0.4dB loss in PSNR as compared to the well-known approximate 2D-GSF.

This paper presents a model to evaluate and select security countermeasures from a pool of candidates. The model performs industrial evaluation and simulations of the financial and technical impact associated to security countermeasures. The financial impact approach uses the Return On Response Investment (RORI) index to compare the expected impact of the attack when no response is enacted against the impact after applying security countermeasures. The technical impact approach evaluates the protection level against a threat, in terms of confidentiality, integrity, and availability. We provide a use case on malware attacks that shows the applicability of our model in selecting the best countermeasure against an Advanced Persistent Threat.

Increased use of Android devices and its open source development framework has attracted many digital crime groups to use Android devices as one of the key attack surfaces. Due to the extensive connectivity and multiple sources of network connections, Android devices are most suitable to botnet based malware attacks. The research focuses on developing a cloud-based Android botnet malware detection system. A prototype of the proposed system is deployed which provides a runtime Android malware analysis. The paper explains architectural implementation of the developed system using a botnet detection learning dataset and multi-layered algorithm used to predict botnet family of a particular application.

The ownership transfer of RFID tag means a tagged product changes control over the supply chain. Recently, Doss et al. proposed two secure RFID tag ownership transfer (RFID-OT) protocols based on quadratic residues. However, we find that they are vulnerable to the desynchronization attack. The attack is probabilistic. As the parameters in the protocols are adopted, the successful probability is 93.75%. We also show that the use of the pseudonym of the tag h(TID) and the new secret key KTID are not feasible. In order to solve these problems, we propose the improved schemes. Security analysis shows that the new protocols can resist in the desynchronization attack and other attacks. By optimizing the performance of the new protocols, it is more practical and feasible in the large-scale deployment of RFID tags.

Networked systems have adapted Radio Frequency identification technology (RFID) to automate their business process. The Networked RFID Systems (NRS) has some unique characteristics which raise new privacy and security concerns for organizations and their NRS systems. The businesses are always having new realization of business needs using NRS. One of the most recent business realization of NRS implementation on large scale distributed systems (such as Internet of Things (IoT), supply chain) is to ensure visibility and traceability of the object throughout the chain. However, this requires assurance of security and privacy to ensure lawful business operation. In this paper, we are proposing a secure tracker protocol that will ensure not only visibility and traceability of the object but also genuineness of the object and its travel path on-site. The proposed protocol is using Physically Unclonable Function (PUF), Diffie-Hellman algorithm and simple cryptographic primitives to protect privacy of the partners, injection of fake objects, non-repudiation, and unclonability. The tag only performs a simple mathematical computation (such as combination, PUF and division) that makes the proposed protocol suitable to passive tags. To verify our security claims, we performed experiment on Security Protocol Description Language (SPDL) model of the proposed protocol using automated claim verification tool Scyther. Our experiment not only verified our claims but also helped us to eliminate possible attacks identified by Scyther.

The speedy advancement in computer hardware has caused data encryption to no longer be a 100% safe solution for secure communications. To battle with adversaries, a countermeasure is to avoid message routing through certain insecure areas, e.g., Malicious countries and nodes. To this end, avoidance routing has been proposed over the past few years. However, the existing avoidance protocols are single-path-based, which means that there must be a safe path such that no adversary is in the proximity of the whole path. This condition is difficult to satisfy. As a result, routing opportunities based on the existing avoidance schemes are limited. To tackle this issue, we propose an avoidance routing framework, namely Multi-Path Avoidance Routing (MPAR). In our approach, a source node first encodes a message into k different pieces, and each piece is sent via k different paths. The destination can assemble the original message easily, while an adversary cannot recover the original message unless she obtains all the pieces. We prove that the coding scheme achieves perfect secrecy against eavesdropping under the condition that an adversary has incomplete information regarding the message. The simulation results validate that the proposed MPAR protocol achieves its design goals.

Spatial-multiplexing cameras have emerged as a promising alternative to classical imaging devices, often enabling acquisition of `more for less'. One popular architecture for spatial multiplexing is the single-pixel camera (SPC), which acquires coded measurements of the scene with pseudo-random spatial masks. Significant theoretical developments over the past few years provide a means for reconstruction of the original imagery from coded measurements at sub-Nyquist sampling rates. Yet, accurate reconstruction generally requires high measurement rates and high signal-to-noise ratios. In this paper, we enquire if one can perform high-level visual inference problems (e.g. face recognition or action recognition) from compressive cameras without the need for image reconstruction. This is an interesting question since in many practical scenarios, our goals extend beyond image reconstruction. However, most inference tasks often require non-linear features and it is not clear how to extract such features directly from compressed measurements. In this paper, we show that one can extract nontrivial correlational features directly without reconstruction of the imagery. As a specific example, we consider the problem of face recognition beyond the visible spectrum e.g in the short-wave infra-red region (SWIR) - where pixels are expensive. We base our framework on smashed filters which suggests that inner-products between high-dimensional signals can be computed in the compressive domain to a high degree of accuracy. We collect a new face image dataset of 30 subjects, obtained using an SPC. Using face recognition as an example, we show that one can indeed perform reconstruction-free inference with a very small loss of accuracy at very high compression ratios of 100 and more.

Cultivation of Smart Grid refurbish with brisk and ingenious. The delinquent breed and sow mutilate in massive. This state of affair coerces security as a sapling which incessantly is to be irrigated with Research and Analysis. The Cyber Security is endowed with resiliency to the SYN flooding induced Denial of Service attack in this work. The proposed secure web server algorithm embedded in the LPC1768 processor ensures the smart resources to be precluded from the attack.

Discrete fractional Fourier transform (DFRFT) is a generalization of discrete Fourier transform. There are a number of DFRFT proposals, which are useful for various signal processing applications. This paper investigates practical solutions toward the construction of unconditionally secure communication systems based on DFRFT via cross-layer approach. By introducing a distort signal parameter, the sender randomly flip-flops between the distort signal parameter and the general signal parameter to confuse the attacker. The advantages of the legitimate partners are guaranteed. We extend the advantages between legitimate partners via developing novel security codes on top of the proposed cross-layer DFRFT security communication model, aiming to achieve an error-free legitimate channel while preventing the eavesdropper from any useful information. Thus, a cross-layer strong mobile communication secure model is built.
 

The transmission of data over a common transmission media revolute the world of information sharing from personal desktop to cloud computing. But the risk of the information theft has increased in the same ratio by the third party working on the same channel. The risk can be avoided using the suitable encryption algorithm. Using the best suited algorithm the transmitted data will be encrypted before placing it on the common channel. Using the public key or the private key the encrypted data can be decrypted by the authenticated user. It will avoid the risk of information theft by the unauthenticated user. In this work we have proposed an encryption algorithm which uses the ASCII code to encrypt the plain text. The common key will be used by sender or receiver to encrypt and decrypt the text for secure communication.

Cyber-attacks have been evolved in a way to be more sophisticated by employing combinations of attack methodologies with greater impacts. For instance, Advanced Persistent Threats (APTs) employ a set of stealthy hacking processes running over a long period of time, making it much hard to detect. With this trend, the importance of big-data security analytics has taken greater attention since identifying such latest attacks requires large-scale data processing and analysis. In this paper, we present SEAS-MR (Security Event Aggregation System over MapReduce) that facilitates scalable security event aggregation for comprehensive situation analysis. The introduced system provides the following three core functions: (i) periodic aggregation, (ii) on-demand aggregation, and (iii) query support for effective analysis. We describe our design and implementation of the system over MapReduce and high-level query languages, and report our experimental results collected through extensive settings on a Hadoop cluster for performance evaluation and design impacts.

The term “Advanced Persistent Threat” refers to a well-organized, malicious group of people who launch stealthy attacks against computer systems of specific targets, such as governments, companies or military. The attacks themselves are long-lasting, difficult to expose and often use very advanced hacking techniques. Since they are advanced in nature, prolonged and persistent, the organizations behind them have to possess a high level of knowledge, advanced tools and competent personnel to execute them. The attacks are usually preformed in several phases - reconnaissance, preparation, execution, gaining access, information gathering and connection maintenance. In each of the phases attacks can be detected with different probabilities. There are several ways to increase the level of security of an organization in order to counter these incidents. First and foremost, it is necessary to educate users and system administrators on different attack vectors and provide them with knowledge and protection so that the attacks are unsuccessful. Second, implement strict security policies. That includes access control and restrictions (to information or network), protecting information by encrypting it and installing latest security upgrades. Finally, it is possible to use software IDS tools to detect such anomalies (e.g. Snort, OSSEC, Sguil).