Biblio
The smart grid aims to improve the efficiency, reliability and safety of the electric system via modern communication system, it's necessary to utilize cloud computing to process and store the data. In fact, it's a promising paradigm to integrate smart grid into cloud computing. However, access to cloud computing system also brings data security issues. This paper focuses on the protection of user privacy in smart meter system based on data combination privacy and trusted third party. The paper demonstrates the security issues for smart grid communication system and cloud computing respectively, and illustrates the security issues for the integration. And we introduce data chunk storage and chunk relationship confusion to protect user privacy. We also propose a chunk information list system for inserting and searching data.
Ensuring system survivability in the wake of advanced persistent threats is a big challenge that the security community is facing to ensure critical infrastructure protection. In this paper, we define metrics and models for the assessment of coordinated massive malware campaigns targeting critical infrastructure sectors. First, we develop an analytical model that allows us to capture the effect of neighborhood on different metrics (infection probability and contagion probability). Then, we assess the impact of putting operational but possibly infected nodes into quarantine. Finally, we study the implications of scanning nodes for early detection of malware (e.g., worms), accounting for false positives and false negatives. Evaluating our methodology using a small four-node topology, we find that malware infections can be effectively contained by using quarantine and appropriate rates of scanning for soft impacts.
In a system manufacturing process that use screens, for exemple, TVs, computer monitors, or notebook, the inspection images is one of the most important quality tests. Due to increasing complexity of these systems, manual inspection became complex and slow. Thus, automatic inspection is an attractive alternative. In this paper, we present an automatic inspection system images using edge and line detection algorithms, rectangles recognition and image comparison metrics. The experiments, performed to 504 images (TVs, computer monitors, and notebook) demonstrate that the system has good performance.
The ultrafast active cavitation imaging (UACI) based on plane wave can be implemented with high frame rate, in which adaptive beamforming technique was introduced to enhance resolutions and signal-to-noise ratio (SNR) of images. However, regular adaptive beamforming continuously updates the spatial filter for each sample point, which requires a huge amount of calculation, especially in the case of a high sampling rate, and, moreover, 3D imaging. In order to achieve UACI rapidly with satisfactory resolution and SNR, this paper proposed an adaptive beamforming on the basis of compressive sensing (CS), which can retain the quality of adaptive beamforming but reduce the calculating amount substantially. The results of simulations and experiments showed that comparing with regular adaptive beamforming, this new method successfully achieved about eightfold in time consuming.
This paper argues the need for considering mitigating circumstances in cybercrime. Mitigating circumstances are conditions which moderate the culpability of an offender of a committed offence. Our argument is based on several observations. The cyberspace introduces a new family of communication and interaction styles and designs which could facilitate, make available, deceive, and in some cases persuade, a user to commit an offence. User's lack of awareness could be a valid mitigation when using software features introduced without a proper management of change and enough precautionary mechanisms, e.g. warning messages. The cyber behaviour of users may not be necessarily a reflection of their real character and intention. Their irrational and unconscious actions may result from their immersed and prolonged presence in a particular cyber context. Hence, the consideration of the cyberspace design, the "cyber psychological" status of an offender and their inter-relation could form a new family of mitigating circumstances inherent and unique to cybercrime. This paper elaborates on this initial argument from different perspectives including software engineering, cyber psychology, digital forensics, social responsibility and law.
Cloud Computing has emerged as a paradigm to deliver on demand resources to facilitate the customers with access to their infrastructure and applications as per their requirements on a subscription basis. An exponential increase in the number of cloud services in the past few years provides more options for customers to choose from. To assist customers in selecting a most trustworthy cloud provider, a unified trust evaluation framework is needed. Trust helps in the estimation of competency of a resource provider in completing a task thus enabling users to select the best resources in the heterogeneous cloud infrastructure. Trust estimates obtained using the AHP process exhibit a deviation for parameters that are not in direct proportion to the contributing attributes. Such deviation can be removed using the Fuzzy AHP model. In this paper, a Fuzzy AHP based hierarchical trust model has been proposed to rate the service providers and their various plans for infrastructure as a service.
In this paper we investigate the proposals made by various industries for the Cellular Internet of Things (C-IoT). We start by introducing the context of C-IoT and demonstrate how this technology is closely linked to the Low Power-Wide Area (LPWA) technologies and networks. An in-depth look and system level evaluation is given for each clean slate technology and a comparison is made based on its specifications.
In this paper we investigate the proposals made by various industries for the Cellular Internet of Things (C-IoT). We start by introducing the context of C-IoT and demonstrate how this technology is closely linked to the Low Power-Wide Area (LPWA) technologies and networks. An in-depth look and system level evaluation is given for each clean slate technology and a comparison is made based on its specifications.
The explosive growth of IT infrastructures, cloud systems, and Internet of Things (IoT) have resulted in complex systems that are extremely difficult to secure and protect against cyberattacks that are growing exponentially in the complexity and also in the number. Overcoming the cybersecurity challenges require cybersecurity environments supporting the development of innovative cybersecurity algorithms and evaluation of the experiments. In this paper, we present the design, analysis, and evaluation of the Cybersecurity Lab as a Service (CLaaS) which offers virtual cybersecurity experiments as a cloud service that can be accessed from anywhere and from any device (desktop, laptop, tablet, smart mobile device, etc.) with Internet connectivity. We exploit cloud computing systems and virtualization technologies to provide isolated and virtual cybersecurity experiments for vulnerability exploitation, launching cyberattacks, how cyber resources and services can be hardened, etc. We also present our performance evaluation and effectiveness of CLaaS experiments used by students.
Intrusion Detection Systems (IDSs) are an important defense tool against the sophisticated and ever-growing network attacks. These systems need to be evaluated against high quality datasets for correctly assessing their usefulness and comparing their performance. We present an Intrusion Detection Dataset Toolkit (ID2T) for the creation of labeled datasets containing user defined synthetic attacks. The architecture of the toolkit is provided for examination and the example of an injected attack, in real network traffic, is visualized and analyzed. We further discuss the ability of the toolkit of creating realistic synthetic attacks of high quality and low bias.
This paper analyzes score normalization for keystroke dynamics authentication systems. Previous studies have shown that the performance of behavioral biometric recognition systems (e.g. voice and signature) can be largely improved with score normalization and target-dependent techniques. The main objective of this work is twofold: i) to analyze the effects of different thresholding techniques in 4 different keystroke dynamics recognition systems for real operational scenarios; and ii) to improve the performance of keystroke dynamics on the basis of target-dependent score normalization techniques. The experiments included in this work are worked out over the keystroke pattern of 114 users from two different publicly available databases. The experiments show that there is large room for improvements in keystroke dynamic systems. The results suggest that score normalization techniques can be used to improve the performance of keystroke dynamics systems in more than 20%. These results encourage researchers to explore this research line to further improve the performance of these systems in real operational environments.
Robust and stringent fault detection and correction techniques in executing Advanced Encryption Standard (AES) are still interesting issues for many critical applications. The purpose of fault detection and correction techniques is not only to ensure the reliability of a cryptosystem, but also protect the system against side channel attacks. Such errors could result due to a fault injection attack, production faults, noise or radiation effects in deep space. Devising a proper error control mechanisms for AES cipher during execution would improve both system reliability and security. In this work a novel fault detection and correction algorithm is proposed. The proposed mechanism is making use of the linear mappings of AES round structure to detect errors in the ShiftRow (SR) and MixColumn (MC) transformations. The error correction is achieved by creating temporary redundant check words through the combined SR and MC mapping to create in case of errors an error syndrome leading to error correction with relatively minor additional complexity. The proposed technique is making use of an error detecting and correcting capability in the combined mapping of SR and MC rather than detecting and/or correcting errors in each transformation separately. The proposed technique is making use especially of the MC mapping exhibiting efficient ECC properties, which can be deployed to simplify the design of a fault-tolerance technique. The performance of the algorithm proposed is evaluated by a simulated system model in FPGA technology. The simulation results demonstrate the ability to reach relatively high fault coverage with error correction up to four bytes of execution errors in the merged transformation SR-MC. The overall gate complexity overhead of the resulting system is estimated for proposed technique in FPGA technology.
More and more advanced persistent threat attacks has happened since 2009. This kind of attacks usually use more than one zero-day exploit to achieve its goal. Most of the times, the target computer will execute malicious program after the user open an infected compound document. The original detection method becomes inefficient as the attackers using a zero-day exploit to structure these compound documents. Inspired by the detection method based on structural entropy, we apply wavelet analysis to malicious document detection system. In our research, we use wavelet analysis to extract features from the raw data. These features will be used todetect whether the compound document was embed malicious code.
A fundamental drawback of current anomaly detection systems (ADSs) is the ability of a skilled attacker to evade detection. This is due to the flawed assumption that an attacker does not have any information about an ADS. Advanced persistent threats that are capable of monitoring network behavior can always estimate some information about ADSs which makes these ADSs susceptible to evasion attacks. Hence in this paper, we first assume the role of an attacker to launch evasion attacks on anomaly detection systems. We show that the ADSs can be completely paralyzed by parameter estimation attacks. We then present a mathematical model to measure evasion margin with the aim to understand the science of evasion due to ADS design. Finally, to minimize the evasion margin, we propose a key-based randomization scheme for existing ADSs and discuss its robustness against evasion attacks. Case studies are presented to illustrate the design methodology and extensive experimentation is performed to corroborate the results.
Cyber-physical systems (CPSs), due to their direct influence on the physical world, have to meet extended security and dependability requirements. This is particularly true for CPS that operate in close proximity to humans or that control resources that, when tampered with, put all our lives at stake. In this paper, we review the challenges and some early solutions that arise at the architectural and operating-system level when we require cyber-physical systems and CPS infrastructure to withstand advanced and persistent threats. We found that although some of the challenges we identified are already matched by rudimentary solutions, further research is required to ensure sustainable and dependable operation of physically exposed CPS infrastructure and, more importantly, to guarantee graceful degradation in case of malfunction or attack.
This paper proposes an improved mesh simplification algorithm based on quadric error metrics (QEM) to efficiently processing the huge data in 3D image processing. This method fully uses geometric information around vertices to avoid model edge from being simplified and to keep details. Meanwhile, the differences between simplified triangular meshes and equilateral triangles are added as weights of errors to decrease the possibilities of narrow triangle and then to avoid the visual mutation. Experiments show that our algorithm has obvious advantages over the time cost, and can better save the visual characteristics of model, which is suitable for solving most image processing, that is, "Real-time interactive" problem.
The passive radar also known as Green Radar exploits the available commercial communication signals and is useful for target tracking and detection in general. Recent communications standards frequently employ Orthogonal Frequency Division Multiplexing (OFDM) waveforms and wideband for broadcasting. This paper focuses on the recent developments of the target detection algorithms in the OFDM passive radar framework where its channel estimates have been derived using the matched filter concept using the knowledge of the transmitted signals. The MUSIC algorithm, which has been modified to solve this two dimensional delay-Doppler detection problem, is first reviewed. As the target detection problem can be represented as sparse signals, this paper employs compressive sensing to compare with the detection capability of the 2-D MUSIC algorithm. It is found that the previously proposed single time sample compressive sensing cannot significantly reduce the leakage from the direct signal component. Furthermore, this paper proposes the compressive sensing method utilizing multiple time samples, namely l1-SVD, for the detection of multiple targets. In comparison between the MUSIC and compressive sensing, the results show that l1-SVD can decrease the direct signal leakage but its prerequisite of computational resources remains a major issue. This paper also presents the detection performance of these two algorithms for closely spaced targets.
The explosive growth of IT infrastructures, cloud systems, and Internet of Things (IoT) have resulted in complex systems that are extremely difficult to secure and protect against cyberattacks which are growing exponentially in complexity and in number. Overcoming the cybersecurity challenges is even more complicated due to the lack of training and widely available cybersecurity environments to experiment with and evaluate new cybersecurity methods. The goal of our research is to address these challenges by exploiting cloud services. In this paper, we present the design, analysis, and evaluation of a cloud service that we refer to as Cybersecurity Lab as a Service (CLaaS) which offers virtual cybersecurity experiments that can be accessed from anywhere and from any device (desktop, laptop, tablet, smart mobile device, etc.) with Internet connectivity. In CLaaS, we exploit cloud computing systems and virtualization technologies to provide virtual cybersecurity experiments and hands-on experiences on how vulnerabilities are exploited to launch cyberattacks, how they can be removed, and how cyber resources and services can be hardened or better protected. We also present our experimental results and evaluation of CLaaS virtual cybersecurity experiments that have been used by graduate students taking our cybersecurity class as well as by high school students participating in GenCyber camps.
Security features are often hardwired into software applications, making it difficult to adapt security responses to reflect changes in runtime context and new attacks. In prior work, we proposed the idea of architecture-based self-protection as a way of separating adaptation logic from application logic and providing a global perspective for reasoning about security adaptations in the context of other business goals. In this paper, we present an approach, based on this idea, for combating denial-of-service (DoS) attacks. Our approach allows DoS-related tactics to be composed into more sophisticated mitigation strategies that encapsulate possible responses to a security problem. Then, utility-based reasoning can be used to consider different business contexts and qualities. We describe how this approach forms the underpinnings of a scientific approach to self-protection, allowing us to reason about how to make the best choice of mitigation at runtime. Moreover, we also show how formal analysis can be used to determine whether the mitigations cover the range of conditions the system is likely to encounter, and the effect of mitigations on other quality attributes of the system. We evaluate the approach using the Rainbow self-adaptive framework and show how Rainbow chooses DoS mitigation tactics that are sensitive to different business contexts.