Biblio

The concept of Virtualized Network Functions (VNFs) aims to move Network Functions (NFs) out of dedicated hardware devices into software that runs on commodity hardware. A single NF consists of multiple VNF instances, usually running on virtual machines in a cloud infrastructure. The elastic management of an NF refers to load management across the VNF instances and the autonomic scaling of the number of VNF instances as the load on the NF changes. In this paper, we present EL-SEC, an autonomic framework to elastically manage security NFs on a virtualized infrastructure. As a use case, we deploy the Snort Intrusion Detection System as the NF on the GENI testbed. Concepts from control theory are used to create an Elastic Manager, which implements various controllers - in this paper, Proportional Integral (PI) and Proportional Integral Derivative (PID) - to direct traffic across the VNF Snort instances by monitoring the current load. RINA (a clean-slate Recursive InterNetwork Architecture) is used to build a distributed application that monitors load and collects Snort alerts, which are processed by the Elastic Manager and an Attack Analyzer, respectively. Software Defined Networking (SDN) is used to steer traffic through the VNF instances, and to block attack traffic. Our results show that virtualized security NFs can be easily deployed using our EL-SEC framework. With the help of real-time graphs, we show that PI and PID controllers can be used to easily scale the system, which leads to quicker detection of attacks.

The Internet of Things is stepping out of its infancy into full maturity, requiring massive data processing and storage. Unfortunately, because of the unique characteristics of resource constraints, short-range communication, and self-organization in IoT, it always resorts to the cloud or fog nodes for outsourced computation and storage, which has brought about a series of novel challenging security and privacy threats. For this reason, one of the critical challenges of having numerous IoT devices is the capacity to manage them and their data. A specific concern is from which devices or Edge clouds to accept join requests or interaction requests. This paper discusses a design concept for developing the IoT data management platform, along with a data management and lineage traceability implementation of the platform based on blockchain and smart contracts, which approaches the two major challenges: how to implement effective data management and enrich rational interoperability for trusted groups of linked Things; And how to settle conflicts between untrusted IoT devices and its requests taking into account security and privacy preserving. Experimental results show that the system scales well with the loss of computing and communication performance maintaining within the acceptable range, works well to effectively defend against unauthorized access and empower data provenance and transparency, which verifies the feasibility and efficiency of the design concept to provide privacy, fine-grained, and integrity data management over the IoT devices by introducing the blockchain-based data management platform.

With the widespread of cloud computing, the delegation of storage and computing is becoming a popular trend. Concerns on data integrity, security, user privacy as well as the correctness of execution are highlighted due to the untrusted remote data manipulation. Most of existing proposals solve the integrity checking and verifiable computation problems by challenge-response model, but are lack of scalability and reusability. Via blockchain, we achieve efficient and transparent public verifiable delegation for both storage and computing. Meanwhile, the smart contract provides API for request handling and secure data query. The security and privacy issues of data opening are settled by applying cryptographic algorithms all through the delegations. Additionally, any access to the outsourced data requires the owner's authentication, so that the dat transference and utilization are under control.

Database outsourcing introduces a new paradigm, called Database as a Service (DBaaS). Database Service Providers (DSPs) have the ability to host outsourced databases and provide efficient facilities for their users. However, the data and the execution of database queries are under the control of the DSP, which is not always a trusted authority. Therefore, our problem is to ensure the outsourced database security. To address this problem, we propose a Secure scheme to provide Confidentiality and Integrity of Query results for Cloud Databases (SCIQ-CD). The performance analysis shows that our proposed scheme is secure and efficient for practical deployment.

Software Defined Networking (SDN) has emerged as a revolutionary paradigm to manage cloud infrastructure. SDN lacks scalable trust setup and verification mechanism between Data Plane-Control Plane elements, Control Plane elements, and Control Plane-Application Plane. Trust management schemes like Public Key Infrastructure (PKI) used currently in SDN are slow for trust establishment in a larger cloud environment. We propose a distributed trust mechanism - TRUFL to establish and verify trust in SDN. The distributed framework utilizes parallelism in trust management, in effect faster transfer rates and reduced latency compared to centralized trust management. The TRUFL framework scales well with the number of OpenFlow rules when compared to existing research works.

In the past few years, Internet of Things (IoT) devices have emerged and spread everywhere. Many researchers have been motivated to study the security issues of IoT devices due to the sensitive information they carry about their owners. Privacy is not simply about encryption and access authorization, but also about what kind of information is transmitted, how it used and to whom it will be shared with. Thus, IoT manufacturers should be compelled to issue Privacy Policy Agreements for their respective devices as well as ensure that the actual behavior of the IoT device complies with the issued privacy policy. In this paper, we implement a test bed for ensuring compliance of Internet of Things data disclosure to the corresponding privacy policy. The fundamental approach used in the test bed is to capture the data traffic between the IoT device and the cloud, between the IoT device and its application on the smart-phone, and between the IoT application and the cloud and analyze those packets for various features. We test 11 IoT manufacturers and the results reveal that half of those IoT manufacturers do not have an adequate privacy policy specifically for their IoT devices. In addition, we prove that the action of two IoT devices does not comply with what they stated in their privacy policy agreement.

The rapid development of the Internet of Things (IoT) results in generating massive amounts of data. Significant portions of these data are sensitive since they reflect (directly or indirectly) peoples' behaviors, interests, lifestyles, etc. Protecting sensitive IoT data from privacy violations is a challenge since these data need to be communicated, processed, analyzed, and stored by public networks, servers, and clouds; most of them are untrusted parties for data owners. We propose a solution for protecting sensitive IoT data called Policy Enforcement Fog Module (PEFM). The major task of the PEFM solution is mandatory enforcement of privacy policies for sensitive IoT data-wherever these data are accessed throughout their entire lifecycle. The key feature of PEFM is its placement within the fog computing infrastructure, which assures that PEFM operates as closely as possible to data sources within the edge. PEFM enforces policies directly for local IoT applications. In contrast, for remote applications, PEFM provides a self-protecting mechanism based on creating and disseminating Active Data Bundles (ADBs). ADBs are software constructs bundling inseparably sensitive data, their privacy policies, and an execution engine able to enforce privacy policies. To prove effectiveness and efficiency of the proposed module, we developed a smart home proof-of-concept scenario. We investigate privacy threats for sensitive IoT data. We run simulation experiments, based on network calculus, for testing performance of the PEFM controls for different network configurations. The results of the simulation show that-even with using from 1 to 5 additional privacy policies for improved data privacy-penalties in terms of execution time and delay are reasonable (approx. 12-15% and 13-19%, respectively). The results also show that PEFM is scalable regarding the number of the real-time constraints for real-time IoT applications.

We propose a novel framework for outsourced private function evaluation with privacy policy enforcement (OPFE-PPE). Suppose an evaluator evaluates a function with private data contributed by a data contributor, and a client obtains the result of the evaluation. OPFE-PPE enables a data contributor to enforce two different kinds of privacy policies to the process of function evaluation: evaluator policy and client policy. An evaluator policy restricts entities that can conduct function evaluation with the data. A client policy restricts entities that can obtain the result of function evaluation. We demonstrate our construction with three applications: personalized medication, genetic epidemiology, and prediction by machine learning. Experimental results show that the overhead caused by enforcing the two privacy policies is less than 10% compared to function evaluation by homomorphic encryption without any privacy policy enforcement.

The Internet of Things (IoT) is changing the way we interact with everyday objects. "Smart" devices will reduce energy use, keep our homes safe, and improve our health. However, as recent attacks have shown, these devices also create tremendous security vulnerabilities in our computing networks. Securing all of these devices is a daunting task. In this paper, we argue that IoT device communications should be default-off and desired network communications must be explicitly enabled. Unlike traditional networked applications or devices like a web browser or PC, IoT applications and devices serve narrowly defined purposes and do not require access to all services in the network. Our proposal, Bark, a policy language and runtime for specifying and enforcing minimal access permissions in IoT networks, exploits this fact. Bark phrases access control policies in terms of natural questions (who, what, where, when, and how) and transforms them into transparently enforceable rules for IoT application protocols. Bark can express detailed rules such as "Let the lights see the luminosity of the bedroom sensor at any time" and "Let a device at my front door, if I approve it, unlock my smart lock for 30 seconds" in a way that is presentable and explainable to users. We implement Bark for Wi-Fi/IP and Bluetooth Low Energy (BLE) networks and evaluate its efficacy on several example applications and attacks.

This paper describes the achievements of project EUBra-BIGSEA, which has delivered programming models and data analytics tools for the development of distributed Big Data applications. As framework components, multiple data models are supported (e.g. data streams, multidimensional data, etc.) and efficient mechanisms to ensure privacy and security, on top of a QoS-aware layer for the smart and rapid provisioning of resources in a cloud-based environment.

Science gateways bring out the possibility of reproducible science as they are integrated into reusable techniques, data and workflow management systems, security mechanisms, and high performance computing (HPC). We introduce BioinfoPortal, a science gateway that integrates a suite of different bioinformatics applications using HPC and data management resources provided by the Brazilian National HPC System (SINAPAD). BioinfoPortal follows the Software as a Service (SaaS) model and the web server is freely available for academic use. The goal of this paper is to describe the science gateway and its usage, addressing challenges of designing a multiuser computational platform for parallel/distributed executions of large-scale bioinformatics applications using the Brazilian HPC resources. We also present a study of performance and scalability of some bioinformatics applications executed in the HPC environments and perform machine learning analyses for predicting features for the HPC allocation/usage that could better perform the bioinformatics applications via BioinfoPortal.

Cloud computing, cloud security and cloud storage have been gradually introduced into people's life and become hot topicsof research, for which relevant technologies have permeated through the computer industry and relevant industries. With the coming of the modern information society, secure storage of data has been becoming increasingly important. Proceeding from traditional policy storage, this paper includes comparison and improvement of policy storage for the purpose of meeting requirements of storage of cloud security policy. Policy storage technology refers to a technology used to realize storage of policies created by users and relevant policy information. Policy repository can conduct centralized management and processing of multiple policies and their relevant information. At present, popular policy repositories generally include policy storage for relational database or policy storage for directory server or a file in a fixed format, such as XML file format.

Live VM migration is the most vulnerable process in cloud federations for DDOS attacks, loss of data integrity, confidentiality, unauthorized access and injection of malicious viruses on VM disk images. We have scrutinized following set of crucial security features which are; authorization, confidentiality, replay protection (accountability), integrity, mutual authentication and source non-repudiation (availability) to cater different threats and vulnerabilities during live VM migration. The investigated threats and vulnerabilities are catered and implemented in a proposed solution, presented in this paper. Six security features-authorization, confidentiality, replay protection, integrity, mutual authentication and source non-repudiation are focused and modular implementation has been done. Solution is validated in AVISPA tool in modules for threats for all the notorious security requirements and no outbreak were seen.

Securing Internet of things is a major concern as it deals with data that are personal, needed to be reliable, can direct and manipulate device decisions in a harmful way. Also regarding data generation process is heterogeneous, data being immense in volume, complex management. Quantum Computing and Internet of Things (IoT) coined as Quantum IoT defines a concept of greater security design which harness the virtue of quantum mechanics laws in Internet of Things (IoT) security management. Also it ensures secured data storage, processing, communication, data dynamics. In this paper, an IoT security infrastructure is introduced which is a hybrid one, with an extra layer, which ensures quantum state. This state prevents any sort of harmful actions from the eavesdroppers in the communication channel and cyber side, by maintaining its state, protecting the key by quantum cryptography BB84 protocol. An adapted version is introduced specific to this IoT scenario. A classical cryptography system `One-Time pad (OTP)' is used in the hybrid management. The novelty of this paper lies with the integration of classical and quantum communication for Internet of Things (IoT) security.

As cloud services greatly facilitate file sharing online, there's been a growing awareness of the security challenges brought by outsourcing data to a third party. Traditionally, the centralized management of cloud service provider brings about safety issues because the third party is only semi-trusted by clients. Besides, it causes trouble for sharing online data conveniently. In this paper, the blockchain technology is utilized for decentralized safety administration and provide more user-friendly service. Apart from that, Ciphertext-Policy Attribute Based Encryption is introduced as an effective tool to realize fine-grained data access control of the stored files. Meanwhile, the security analysis proves the confidentiality and integrity of the data stored in the cloud server. Finally, we evaluate the performance of computation overhead of our system.

Cloud Storage Service(CSS) provides unbounded, robust file storage capability and facilitates for pay-per-use and collaborative work to end users. But due to security issues like lack of confidentiality, malicious insiders, it has not gained wide spread acceptance to store sensitive information. Researchers have proposed proxy re-encryption schemes for secure data sharing through cloud. Due to advancement of computing technologies and advent of quantum computing algorithms, security of existing schemes can be compromised within seconds. Hence there is a need for designing security schemes which can be quantum computing resistant. In this paper, a secure file sharing scheme through cloud storage using proxy re-encryption technique has been proposed. The proposed scheme is proven to be chosen ciphertext secure(CCA) under hardness of ring-LWE, Search problem using random oracle model. The proposed scheme outperforms the existing CCA secure schemes in-terms of re-encryption time and decryption time for encrypted files which results in an efficient file sharing scheme through cloud storage.

Public cloud data storage services were considered as a potential alternative to store low-cost digital data in the short term. They are offered by different providers on the Internet. Some providers offer limited free plans for the users who are starting the service. However, data security concern arises when data stored are considered as a valuable asset. This study explores the usage of secret sharing scheme: Rabin's IDA and Shamir's SSA to implement a tool called dCloud for file protection stored in public cloud storage in a seamless way. It addresses data security by hiding its complexities when targeting ordinary non-technical users. The secret key is automatically generated by dCloud in a secure random way on Rabin's IDA. Shamir's SSA completes the process through dispersing the key into each of Rabin's IDA output files. Moreover, the Hash value of the original file is added to each of those output files to confirm the integrity of the file during reconstruction. Besides, the authentication key is used to communicate with all of the defined service providers during storage and reconstruction as well. It is stored into local secure key-store. By having a key to access the key-store, an ordinary non-technical user will be able to use dCloud to store and retrieve targeted file within defined public cloud storage services securely.

Cloud storage brokerage systems abstract cloud storage complexities by mediating technical and business relationships between cloud stakeholders, while providing value-added services. This however raises security challenges pertaining to the integration of disparate components with sometimes conflicting security policies and architectural complexities. Assessing the security risks of these challenges is therefore important for Cloud Storage Brokers (CSBs). In this paper, we present a threat modeling schema to analyze and identify threats and risks in cloud brokerage brokerage systems. Our threat modeling schema works by generating attack trees, attack graphs, and data flow diagrams that represent the interconnections between identified security risks. Our proof-of-concept implementation employs the Common Configuration Scoring System (CCSS) to support the threat modeling schema, since current schemes lack sufficient security metrics which are imperatives for comprehensive risk assessments. We demonstrate the efficiency of our proposal by devising CCSS base scores for two attacks commonly launched against cloud storage systems: Cloud sStorage Enumeration Attack and Cloud Storage Exploitation Attack. These metrics are then combined with CVSS based metrics to assign probabilities in an Attack Tree. Thus, we show the possibility combining CVSS and CCSS for comprehensive threat modeling, and also show that our schemas can be used to improve cloud security.

In the cloud computing environment, a growing number of users share their own data files through cloud storage. However, there will be some security and privacy problems due to the reason that the cloud is not completely trusted, so it needs to be resolved by access control. Attribute-based encryption (ABE) and searchable encryption (SE) can solve fine-grained access control. At present, researchers combine the two to propose an attribute-based searchable encryption scheme and achieved remarkable results. Nevertheless, most of existing attribute-based searchable encryption schemes cannot resist online/offline keyword guessing attack. To solve the problem, we present an attribute-based (CP-ABE) searchable encryption scheme that supports trapdoor updating (CSES-TU). In this scheme, the data owner can formulate an access strategy for the encrypted data. Only the attributes of the data user are matched with the strategy can the effective trapdoor be generated and the ciphertext be searched, and that this scheme will update trapdoors at the same time. Even if the keywords are the same, new trapdoors will be generated every time when the keyword is searched, thus minimizing the damage caused by online/offline keyword guessing attack. Finally, the performance of the scheme is analyzed, and the proof of correctness and security are given at the same time.

Searchable encryption server protects privacal data of data owner from leaks. This paper analyzes the security of a multi-user searchable encryption scheme and points out that this scheme does not satisfy the invisibility of trapdoors. In order to improve the security of the original scheme, this paper proposes a probably secure multi-user multi-keyword searchable encryption scheme. New secheme not only ensures the confidentiality of the cipher text keyword, but also does not increase the encryption workload of the data owner when the new data user joins. In the random oracle model, based on the hard problem of decisional Diffie-Hellman, it is proved that the scheme has trapdoor indistinguishability. In the end, obtained by the simulation program to achieve a new computationally efficient communication at low cost.

Cloud storage backends such as Amazon S3 are a potential storage solution to enterprises. However, to couple enterprises with these backends, at least two problems must be solved: first, how to make these semi-trusted backends as secure as on-premises storage; and second, how to selectively retrieve files as easy as on-premises storage. A security proxy can address both the problems by building a local index from keywords in files before encrypting and uploading files to these backends. But, if the local index is built in plaintext, file content is still vulnerable to local malicious staff. Searchable Encryption (SE) can get rid of this vulnerability by making index into ciphertext; however, its known constructions often require modifications to index database, and, to support wildcard queries, they are not efficient at all. In this paper, we present a security proxy that, based on our wildcard SE construction, can securely and efficiently couple enterprises with these backends. In particular, since our SE construction can work directly with existing database systems, it incurs only a little overhead, and when needed, permits the security proxy to run with constantly small storage footprint by readily out-sourcing all built indices to existing cloud databases.

Industry 4.0 is based on the CPS architecture since it is the next generation in the industry. The CPS architecture is a system based on Cloud Computing technology and Internet of Things where computer elements collaborate for the control of physical entities. The security framework in this architecture is necessary for the protection of two parts (physical and information) so basically, security in CPS is classified into two main parts: information security (data) and security of control. In this work, we propose two models to solve the two problems detected in the security framework. The first proposal SCCAF (Smart Cloud Computing Adoption Framework) treats the nature of information that serves for the detection and the blocking of the threats our basic architecture CPS. The second model is a modeled detector related to the physical nature for detecting node information.

The evolution of the microelectronics manufacturing industry is characterized by increased complexity, analysis, integration, distribution, data sharing and collaboration, all of which is enabled by the big data explosion. This evolution affords a number of opportunities in improved productivity and quality, and reduced cost, however it also brings with it a number of risks associated with maintaining security of data systems. The International Roadmap for Devices and System Factory Integration International Focus Team (IRDS FI IFT) determined that a security technology roadmap for the industry is needed to better understand the needs, challenges and potential solutions for security in the microelectronics industry and its supply chain. As a first step in providing this roadmap, the IFT conducted a security survey, soliciting input from users, suppliers and OEMs. Preliminary results indicate that data partitioning with IP protection is the number one topic of concern, with the need for industry-wide standards as the second most important topic. Further, the "fear" of security breach is considered to be a significant hindrance to Advanced Process Control efforts as well as use of cloud-based solutions. The IRDS FI IFT will endeavor to provide components of a security roadmap for the industry in the 2018 FI chapter, leveraging the output of the survey effort combined with follow-up discussions with users and consultations with experts.

We consider a moving-target defense of a proxied multiserver tenant of the cloud where the proxies dynamically change to defeat reconnaissance activity by a botnet planning a DDoS attack targeting the tenant. Unlike the system of [4] where all proxies change simultaneously at a fixed rate, we consider a more “responsive” system where the proxies may change more rapidly and selectively based on the current session request intensity, which is expected to be abnormally large during active reconnaissance. In this paper, we study a tractable “adversarial” coupon-collector model wherein proxies change after a random period of time from the latest request, i.e., asynchronously. In addition to determining the stationary mean number of proxies discovered by the attacker, we study the age of a proxy (coupon type) when it has been identified (requested) by the botnet. This gives us the rate at which proxies change (cost to the defender) when the nominal client request load is relatively negligible.

Recent research has proposed middleware to enable efficient distributed apps over mobile-cloud platforms. This paper presents a Context-Aware File Discovery Service (CAFDS) that allows distributed mobile-cloud applications to find and access files of interest shared by collaborating users. CAFDS enables programmers to search for files defined by context and content features, such as location, creation time, or the presence of certain object types within an image file. CAFDS provides low-latency through a cloud-based metadata server, which uses a decision tree to locate the nearest files that satisfy the context and content features requested by applications. We implemented CAFDS in Android and Linux. Experimental results show CAFDS achieves substantially lower latency than peer-to-peer solutions that cannot leverage context information.