Biblio

Using shape context descriptors in the distance uneven grouping and its more extensive description of the shape feature, so this descriptor has the target contour point set deformation invariance. However, the twisted adhesions verification code have more outliers and more serious noise, the above-mentioned invariance of the shape context will become very bad, in order to solve the above descriptors' limitations, this article raise a new algorithm based on the relative shape context and point pattern matching to identify codes. And also experimented on the CSDN site's verification code, the result is that the recognition rate is higher than the traditional shape context and the response time is shorter.

Government-sanctioned and market-based anti-piracy measures can both mitigate economic harm from piracy.

The performance, dependability, and security of cloud service systems are vital for the ongoing operation, control, and support. Thus, controlled improvement in service requires a comprehensive analysis and systematic identification of the fundamental underlying constituents of cloud using a rigorous discipline. In this paper, we introduce a framework which helps identifying areas for potential cloud service enhancements. A cloud service cannot be completed if there is a failure in any of its underlying resources. In addition, resources are kept offline for scheduled maintenance. We use redundant resources to mitigate the impact of failures/maintenance for ensuring performance and dependability; which helps enhancing security as well. For example, at least 4 replicas are required to defend the intrusion of a single instance or a single malicious attack/fault as defined by Byzantine Fault Tolerance (BFT). Data centers with high performance, dependability, and security are outsourced to the cloud computing environment with greater flexibility of cost of owing the computing infrastructure. In this paper, we analyze the effectiveness of redundant resource usage in terms of dependability metric and cost of service deployment based on the priority of service requests. The trade-off among dependability, cost, and security under different redundancy schemes are characterized through the comprehensive analytical models.

The world is becoming an immense critical information infrastructure, with the fast and increasing entanglement of utilities, telecommunications, Internet, cloud, and the emerging IoT tissue. This may create enormous opportunities, but also brings about similarly extreme security and dependability risks. We predict an increase in very sophisticated targeted attacks, or advanced persistent threats (APT), and claim that this calls for expanding the frontier of security and dependability methods and techniques used in our current CII. Extreme threats require extreme defenses: we propose resilience as a unifying paradigm to endow systems with the capability of dynamically and automatically handling extreme adversary power, and sustaining perpetual and unattended operation. In this position paper, we present this vision and describe our methodology, as well as the assurance arguments we make for the ultra-resilient components and protocols they enable, illustrated with case studies in progress.

In this paper, we introduce a fast, secure and robust scheme for digital image encryption using chaotic system of Lorenz, 4D hyper-chaotic system and the Secure Hash Algorithm SHA-1. The encryption process consists of three layers: sub-vectors confusion and two-diffusion process. In the first layer we divide the plainimage into sub-vectors then, the position of each one is changed using the chaotic index sequence generated with chaotic attractor of Lorenz, while the diffusion layers use hyper-chaotic system to modify the values of pixels using an XOR operation. The results of security analysis such as statistical tests, differential attacks, key space, key sensitivity, entropy information and the running time are illustrated and compared to recent encryption schemes where the highest security level and speed are improved.

Root cause analysis (RCA) is a common and recurring task performed by operators of cellular networks. It is done mainly to keep customers satisfied with the quality of offered services and to maximize return on investment (ROI) by minimizing and where possible eliminating the root causes of faults in cellular networks. Currently, the actual detection and diagnosis of faults or potential faults is still a manual and slow process often carried out by network experts who manually analyze and correlate various pieces of network data such as, alarms, call traces, configuration management (CM) and key performance indicator (KPI) data in order to come up with the most probable root cause of a given network fault. In this paper, we propose an automated fault detection and diagnosis solution called adaptive root cause analysis (ARCA). The solution uses measurements and other network data together with Bayesian network theory to perform automated evidence based RCA. Compared to the current common practice, our solution is faster due to automation of the entire RCA process. The solution is also cheaper because it needs fewer or no personnel in order to operate and it improves efficiency through domain knowledge reuse during adaptive learning. As it uses a probabilistic Bayesian classifier, it can work with incomplete data and it can handle large datasets with complex probability combinations. Experimental results from stratified synthesized data affirmatively validate the feasibility of using such a solution as a key part of self-healing (SH) especially in emerging self-organizing network (SON) based solutions in LTE Advanced (LTE-A) and 5G.

Once we had tried to propose an unbreakable CAPTCHA and we reached a result that limitation of time is effect to prevent computers from recognizing characters accurately while computers can finally recognize all text-based CAPTCHA in unlimited time. One of the existing usual ways to prevent computers from recognizing characters is distortion, and adding noise is also effective for the prevention. However, these kinds of prevention also make recognition of characters by human beings difficult. As a solution of the problems, an effective text-based CAPTCHA algorithm with amodal completion was proposed by our team. Our CAPTCHA causes computers a large amount of calculation costs while amodal completion helps human beings to recognize characters momentarily. Our CAPTCHA has evolved with aftereffects and combinations of complementary colors. We evaluated our CAPTCHA with deep learning which is attracting the most attention since deep learning is faster and more accurate than existing methods for recognition with computers. In this paper, we add jagged lines to edges of characters since edges are one of the most important parts for recognition in deep learning. In this paper, we also evaluate that how much the jagged lines decrease recognition of human beings and how much they prevent computers from the recognition. We confirm the effects of our method to deep learning.

Aiming at the problem of internal attackers of database system, anomaly detection method of user behaviour is used to detect the internal attackers of database system. With using Discrete-time Markov Chains (DTMC), an anomaly detection system of user behavior is proposed, which can detect the internal threats of database system. First, we make an analysis on SQL queries, which are user behavior features. Then, we use DTMC model extract behavior features of a normal user and the detected user and make a comparison between them. If the deviation of features is beyond threshold, the detected user behavior is judged as an anomaly behavior. The experiments are used to test the feasibility of the detction system. The experimental results show that this detction system can detect normal and abnormal user behavior precisely and effectively.

In previous work, we proposed a solution to facilitate access to computer science related courses and learning materials using cloud computing and mobile technologies. The solution was positively evaluated by the participants, but most of them indicated that it lacks support for laboratory activities. As it is well known that many of computer science subjects (e.g. Computer Networks, Information Security, Systems Administration, etc.) require a suitable and flexible environment where students can access a set of computers and network devices to successfully complete their hands-on activities. To achieve this criteria, we created a cloud-based virtual laboratory based on OpenStack cloud platform to facilitate access to virtual machine both locally and remotely. Cloud-based virtual labs bring a lot of advantages, such as increased manageability, scalability, high availability and flexibility, to name a few. This arrangement has been tested in a case-study exercise with a group of students as part of Computer Networks and System Administration courses at Kabul Polytechnic University in Afghanistan. To measure success, we introduced a level test to be completed by participants prior and after the experiment. As a result, the learners achieved an average of 17.1 % higher scores in the post level test after completing the practical exercises. Lastly, we distributed a questionnaire after the experiment and students provided positive feedback on the effectiveness and usefulness of the proposed solution.

This paper introduces a multi-factors security key generation mechanism for self-organising Internet of Things (IoT) network and nodes. The mechanism enables users to generate unique set of security keys to enhance IoT security while meeting various business needs. The multi-factor security keys presents an additional security layer to existing security standards and practices currently being adopted by the IoT community. The proposed security key generation mechanism enables user to define and choose any physical and logical parameters he/she prefers, in generating a set of security keys to be encrypted and distributed to registered IoT nodes. IoT applications and services will only be activated after verifying that all security keys are present. Multiple levels of authorisation for different user groups can be easily created through the mix and match of the generated multi-factors security keys. A use case, covering indoor and outdoor field tests was conducted. The results of the tests showed that the mechanism is easily adaptable to meet diverse multivendor IoT devices and is scalable for various applications.

Because of the nature of vehicular communications, security is a crucial aspect, involving the continuous development and analysis of the existing security architectures and punctual theoretical and practical aspects that have been proposed and are in need of continuous updates and integrations with newer technologies. But before an update, a good knowledge of the current aspects is mandatory. Identifying weaknesses and anticipating possible risks of vehicular communication networks through a failure modes and effects analysis (FMEA) represent an important aspect of the security analysis process and a valuable step in finding efficient security solutions for all kind of problems that might occur in these systems.

Security has always been concern when it comes to data sharing in cloud computing. Cloud computing provides high computation power and memory. Cloud computing is convenient way for data sharing. But users may sometime needs to outsourced the shared data to cloud server though it contains valuable and sensitive information. Thus it is necessary to provide cryptographically enhanced access control for data sharing system. This paper discuss about the promising access control for data sharing in cloud which is identity-based encryption. We introduce the efficient revocation scheme for the system which is revocable-storage identity-based encryption scheme. It provides both forward and backward security of ciphertext. Then we will have glance at the architecture and steps involved in identity-based encryption. Finally we propose system that provide secure file sharing system using identity-based encryption scheme.

Security cases-which document the rationale for believing that a system is adequately secure-have not been sufficiently used for a lack of practical construction method. This paper presents a hierarchical software security case development method to address this issue. We present a security concept relationship model first, then come up with a hierarchical asset-threat-control measure argument strategy, together with the consideration of an asset classification and threat classification for software security case. Lastly, we propose 11 software security case patterns and illustrate one of them.

This presents a new model to support empirical failure probability estimation for a software-intensive system. The new element of the approach is that it combines the results of testing using a simulated hardware platform with results from testing on the real platform. This approach addresses a serious practical limitation of a technique known as statistical testing. This limitation will be called the test time expansion problem (or simply the 'time problem'), which is that the amount of testing required to demonstrate useful levels of reliability over a time period T is many orders of magnitude greater than T. The time problem arises whether the aim is to demonstrate ultra-high reliability levels for protection system, or to demonstrate any (desirable) reliability levels for continuous operation ('high demand') systems. Specifically, the theoretical feasibility of a platform simulation approach is considered since, if this is not proven, questions of practical implementation are moot. Subject to the assumptions made in the paper, theoretical feasibility is demonstrated.

Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity problem. Recently proposed topic evolution models are more suitable for short texts, but they need to manually specify topic number which is fixed during different time period. To address these issues, in this paper, we propose a nonparametric topic evolution model for social media short texts. We first propose the recurrent semantic dependent Chinese restaurant process (rsdCRP), which is a nonparametric process incorporating word embeddings to capture semantic similarity information. Then we combine rsdCRP with word co-occurrence modeling and build our short-text oriented topic evolution model sdTEM. We carry out experimental studies on Twitter dataset. The results demonstrate the effectiveness of our method to monitor social media topic evolution compared to the baseline methods.

Many a time's assumptions are key to inventions. One such notion in recent past is about data exchange between two disjoint computer systems. It is always assumed that, if any two computers are separated physically without any inter communication, it is considered to be very secure and will not be compromised, the exchange of data between them would be impossible. But recent growth in the field of computers emphasizes the requirements of security analysis. One such security concern is with the air-gapped systems. This paper deals with the flaws and flow of air-gapped systems.

In recent works, numerous physical-layer security systems have been proposed as alternatives to classic cryptography. Such systems aim to use the intrinsic properties of radio signals and the wireless medium to provide confidentiality and authentication to wireless devices. However, fundamental vulnerabilities are often discovered in these systems shortly after their inception. We therefore challenge the assumptions made by existing physical-layer security systems, and postulate that weaker assumptions are needed in order to adapt for practical scenarios. We also argue that if no computational advantage over an adversary can be ensured, secure communication cannot be realistically achieved.

In order to support large volume of transactions and number of users, as estimated by the load demand modeling, a system needs to scale in order to continue to satisfy required quality attributes. In particular, for systems exposed to the Internet, scaling up may increase the attack surface susceptible to malicious intrusions. The new proactive approach based on the concept of Moving Target Defense (MTD) should be considered as a complement to current cybersecurity protection. In this paper, we analyze the scalability of the Self Cleansing Intrusion Tolerance (SCIT) MTD approach using Cloud infrastructure services. By applying the model of MTD with continuous rotation and diversity to a multi-node or multi-instance system, we argue that the effectiveness of the approach is dependent on the share-nothing architecture pattern of the large system. Furthermore, adding more resources to the MTD mechanism can compensate to achieve the desired level of secure availability.

Confidentiality concerns are important in the context of cloud databases. In this paper, the technique of vertical fragmentation is explored to break sensitive associations between columns of several database tables according to confidentiality constraints. By storing insensitive portions of the database at different non-communicating servers it is possible to overcome confidentiality concerns. In addition, visibility constraints and data dependencies are supported. Moreover, to provide some control over the distribution of columns among different servers, novel closeness constraints are introduced. Finding confidentiality-preserving fragmentations is studied in the context of mathematical optimization and a corresponding integer linear program formulation is presented. Benchmarks were performed to evaluate the suitability of our approach.

We present and explore a model of stateless and self-stabilizing distributed computation, inspired by real-world applications such as routing on today's Internet. Processors in our model do not have an internal state, but rather interact by repeatedly mapping incoming messages ("labels") to outgoing messages and output values. While seemingly too restrictive to be of interest, stateless computation encompasses both classical game-theoretic notions of strategic interaction and a broad range of practical applications (e.g., Internet protocols, circuits, diffusion of technologies in social networks). Our main technical contribution is a general impossibility result for stateless self-stabilization in our model, showing that even modest asynchrony (with wait times that are linear in the number of processors) can prevent a stateless protocol from reaching a stable global configuration. Furthermore, we present hardness results for verifying stateless self-stabilization. We also address several aspects of the computational power of stateless protocols. Most significantly, we show that short messages (of length that is logarithmic in the number of processors) yield substantial computational power, even on very poorly connected topologies.

Peer-To-Peer (P2P) networks open up great possibilities for intercommunication, collaborative and social projects like file sharing, communication protocols or social networks while offering advantages over the conventional Client-Server model of computing pattern. Such networks counter the problems of centralized servers such as that P2P networks can scale to millions without additional costs. In previous work, we presented Distributed Data Structure (DDS) which offers a middle-ware scheme for distributed applications. This scheme builds on top of DHT (Distributed Hash Table) based P2P overlays, and offers distributed data storage services as a middle-ware it still needs to address security issues. The main objective of this paper is to investigate possible ways to handle the security problem for DDS, and to develop a possibly reusable security architecture for access control for secure distributed data structures in P2P networks without depending on trusted third parties.

Cyber Physical Systems (CPS) operating in modern critical infrastructures (CIs) are increasingly being targeted by highly sophisticated cyber attacks. Threat actors have quickly learned of the value and potential impact of targeting CPS, and numerous tailored multi-stage cyber-physical attack campaigns, such as Advanced Persistent Threats (APTs), have been perpetrated in the last years. They aim at stealthily compromising systems' operations and cause severe impact on daily business operations such as shutdowns, equipment damage, reputation damage, financial loss, intellectual property theft, and health and safety risks. Protecting CIs against such threats has become as crucial as complicated. Novel distributed detection and reaction methodologies are necessary to effectively uncover these attacks, and timely mitigate their effects. Correlating large amounts of data, collected from a multitude of relevant sources, is fundamental for Security Operation Centers (SOCs) to establish cyber situational awareness, and allow to promptly adopt suitable countermeasures in case of attacks. In our previous work we introduced three methods for security information correlation. In this paper we define metrics and benchmarks to evaluate these correlation methods, we assess their accuracy, and we compare their performance. We finally demonstrate how the presented techniques, implemented within our cyber threat intelligence analysis engine called CAESAIR, can be applied to support incident handling tasks performed by SOCs.

High accurate time synchronization is very important for many applications and industrial environments. In a computer network, synchronization of time for connected devices is provided by the Precision Time Protocol (PTP), which in principal allows for device time synchronization down to microsecond level. However, PTP and network infrastructures are vulnerable to cyber-attacks, which can de-synchronize an entire network, leading to potentially devastating consequences. This paper will focus on the issue of internal attacks on time synchronization networks and discuss how counter-measures based on public key infrastructures, trusted platform modules, network intrusion detection systems and time synchronization supervisors can be adopted to defeat or at least detect such internal attacks.

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.

The growing computerization of critical infrastructure as well as the pervasiveness of computing in everyday life has led to increased interest in secure application development. We observe a flurry of new security technologies like ARM TrustZone and Intel SGX, but a lack of a corresponding architectural vision. We are convinced that point solutions are not sufficient to address the overall challenge of secure system design. In this paper, we outline our take on a trusted component ecosystem of small individual building blocks with strong isolation. In our view, applications should no longer be designed as massive stacks of vertically layered frameworks, but instead as horizontal aggregates of mutually isolated components that collaborate across machine boundaries to provide a service. Lateral thinking is needed to make secure systems going forward.