Biblio

Distributed storage platforms draw much attention due to their high reliability and scalability for handling a massive amount of data. To protect user and data privacy, encryption is considered as a necessary feature for production systems like Storj. But it prohibits the nodes from performing content search. To preserve the functionality, we observe that a protocol of integration with searchable encryption and keyword search via distributed hash table allows the nodes in a network to search over encrypted and distributed data. However, this protocol does not address a practical threat in a fully distributed scenario. Malicious nodes would sabotage search results, and easily infiltrate the system as the network grows. Using primitives such as MAC and verifiable data structure may empower the users to verify the search result, but the robustness of the overall system can hardly be ensured. In this paper, we address this issue by proposing a protocol that is seamlessly incorporated to encrypted search in distributed network to attest and monitor nodes. From the moment a node joins the system, it will be attested and continuously monitored through verifiable search queries. The result of each attestation is determined via a standard quorum-based voting protocol, and then recorded on the blockchain as a consensus view of trusted nodes. Based on the proposed protocols, malicious nodes can be detected and removed by a majority of nodes in a self-determining manner. To demonstrate the security and efficiency, we conduct robustness analysis against several potential attacks, and perform performance and overhead evaluation on the proposed protocol.

We are witnessing a huge growth of cyber-physical systems, which are autonomous, mobile, endowed with sensing, controlled by software, and often wirelessly connected and Internet-enabled. They include factory automation systems, robotic assistants, self-driving cars, and wearable and implantable devices. Since they are increasingly often used in safety- or business-critical contexts, to mention invasive treatment or biometric authentication, there is an urgent need for modelling and verification technologies to support the design process, and hence improve the reliability and reduce production costs. This paper gives an overview of quantitative verification and synthesis techniques developed for cyber-physical systems, summarising recent achievements and future challenges in this important field.

We will focused the concept of serializability in order to ensure the correct processing of transactions. However, both serializability and relevant properties within transaction-based applications might be affected. Ensure transaction serialization in corrupt systems is one of the demands that can handle properly interrelated transactions, which prevents blocking situations that involve the inability to commit either transaction or related sub-transactions. In addition some transactions has been marked as malicious and they compromise the serialization of running system. In such context, this paper proposes an approach for the processing of transactions in a cloud of databases environment able to secure serializability in running transactions whether the system is compromised or not. We propose also an intrusion tolerant scheme to ensure the continuity of the running transactions. A case study and a simulation result are shown to illustrate the capabilities of the suggested system.

Software-based systems are nowadays complex and highly distributed. In contrast, existing intrusion detection mechanisms are not always suitable for protecting these systems against new and sophisticated attacks that increasingly appear. In this paper, we present a new generic approach that combines monitoring and formal methods in order to ensure attack-tolerance at a high level of abstraction. Our experiments on an authentication Web application show that this method is effective and realistic to tolerate a variety of attacks.

As the Internet becomes an important part of the infrastructure our society depends on, it is crucial to construct networks that are able to work even when part of the network is compromised. This paper presents the first practical intrusion-tolerant network service, targeting high-value applications such as monitoring and control of global clouds and management of critical infrastructure for the power grid. We use an overlay approach to leverage the existing IP infrastructure while providing the required resiliency and timeliness. Our solution overcomes malicious attacks and compromises in both the underlying network infrastructure and in the overlay itself. We deploy and evaluate the intrusion-tolerant overlay implementation on a global cloud spanning East Asia, North America, and Europe, and make it publicly available.

The cloud has become an established and widespread paradigm. This success is due to the gain of flexibility and savings provided by this technology. However, the main obstacle to full cloud adoption is security. The cloud, as many other systems taking advantage of the Internet, is also facing threats that compromise data confidentiality and availability. In addition, new cloud-specific attacks have emerged and current intrusion detection and prevention mechanisms are not enough to protect the complex infrastructure of the cloud from these vulnerabilities. Furthermore, one of the promises of the cloud is the Quality of Service (QoS) by continuous delivery, which must be ensured even in case of intrusion. This work presents an overview of the main cloud vulnerabilities, along with the solutions proposed in the context of the H2020 CLARUS project in terms of monitoring techniques for intrusion detection and prevention, including attack-tolerance mechanisms.

The UHF Radiofrequency Identification technology offers nowadays a viable technological solution for the implementation of low-level environmental monitoring of connected critical infrastructures to be protected from both physical threats and cyber attacks. An RFID sensor network was developed within the H2020 SCISSOR project, by addressing the design of both hardware components, that is a new family of multi-purpose wireless boards, and of control software handling the network topology. The hierarchical system is able to the detect complex, potentially dangerous, events such as the un-authorized access to a restricted area, anomalies of the electrical equipments, or the unusual variation of environmental parameters. The first real-world test-bed has been deployed inside an operational smart-grid on the Favignana Island. Currently, the network is fully working and remotely accessible.

A wireless sensor network (WSN) can provide a low cost and flexible solution to sensing and monitoring for large distributed applications. To save energy and prolong the network lifetime, the WSN is often partitioned into a set of spatial clusters. Each cluster includes sensor nodes with similar sensing data, and only a few sensor nodes (samplers) report their sensing data to a base node. Then the base node may predict the missed data of non-samplers using the spatial correlation between sensor nodes. The problem is that the WSN is vulnerable to internal security threat such as node compromise. If the samplers are compromised and report incorrect data intentionally, then the WSN should be contaminated rapidly due to the process of data prediction at the base node. In this paper, we propose three algorithms to detect compromised samplers for secure data collection in the WSN. The proposed algorithms leverage the unique property of spatial clustering to alleviate the overhead of compromised node detection. Experiment results indicate that the proposed algorithms can identify compromised samplers with a high accuracy and low energy consumption when as many as 50% sensor nodes are misbehaving.

Internet of Things (IoT) and its various application domains are radically changing the lives of people, providing smart services which will ultimately constitute integral components of the living environment. The services of IoT operate based on the data flows collected from the different sensors and actuators. In this respect, the correctness and security of the sensor data transported over the IoT system is a crucial factor in ensuring the correct functioning of the IoT services. In this work, we present a method that can detect abnormal sensor events based on “apriori” knowledge of the behavior of the monitored process. The main advantage of the proposed methodology is that it builds on well-established theoretical works, while delivering a practical technique with low computational requirements. As a result, the developed technique can be hosted on various components of an IoT system. The developed approach is evaluated through real-world use-cases.

New generations of industrial control systems offer higher performance, they are distributed, and it is very likely that they are internet connected in one way or another. These trends raise new challenges in the contexts of reliability and security. We propose a novel approach that tackles the complexity of industrial control systems at design time and run time. At design time our target is to ease the configuration and verification of controller configurations through model-driven engineering techniques together with the contract-based design paradigm. At run time the information from design time is reused in order to support a modular and distributed self-adaptive software system that aims to increase reliability and security. The industrial setting of the presented approach are control devices for hydropower plant units.

In order to ensure the security of electric power supervisory control and data acquisition (SCADA) system, this paper proposes a dynamic awareness security protection model based on security policy, the design idea of which regards safety construction protection as a dynamic analysis process and the security policy should adapt to the network dynamics. According to the current situation of the power SCADA system, the related security technology and the investigation results of system security threat, the paper analyzes the security requirements and puts forward the construction ideas of security protection based on policy protection detection response (P2DR) policy model. The dynamic awareness security protection model proposed in this paper is an effective and useful tool for protecting the security of power-SCADA system.

When clients interact with a cloud-based service, they expect certain levels of quality of service guarantees. These are expressed as security and privacy policies, interaction authorization policies, and service performance policies among others. The main security challenge in a cloud-based service environment, typically modeled using service-oriented architecture (SOA), is that it is difficult to trust all services in a service composition. In addition, the details of the services involved in an end-to-end service invocation chain are usually not exposed to the clients. The complexity of the SOA services and multi-tenancy in the cloud environment leads to a large attack surface. In this paper we propose a novel approach for end-to-end security and privacy in cloud-based service orchestrations, which uses a service activity monitor to audit activities of services in a domain. The service monitor intercepts interactions between a client and services, as well as among services, and provides a pluggable interface for different modules to analyze service interactions and make dynamic decisions based on security policies defined over the service domain. Experiments with a real-world service composition scenario demonstrate that the overhead of monitoring is acceptable for real-time operation of Web services.

Two emerging architectural paradigms, i.e., Software Defined Networking (SDN) and Network Function Virtualization (NFV), enable the deployment and management of Service Function Chains (SFCs). A SFC is an ordered sequence of abstract Service Functions (SFs), e.g., firewalls, VPN-gateways, traffic monitors, that packets have to traverse in the route from source to destination. While this appealing solution offers significant advantages in terms of flexibility, it also introduces new challenges such as the correct configuration and ordering of SFs in the chain to satisfy overall security requirements. This paper presents a formal model conceived to enable the verification of correct policy enforcements in SFCs. Software tools based on the model can then be designed to cope with unwanted network behaviors (e.g., security flaws) deriving from incorrect interactions of SFs of the same SFC. 

A problem in managing the ever growing computer networks nowadays is the analysis of events detected by intrusion detection systems and the classification whether an event was correctly detected or not. When a false positive is detected by the user, changes to the configuration must be made and evaluated before they can be adopted to productive use. This paper describes an approach for a visual analysis framework that integrates the monitoring and analysis of events and the resulting changes on the configuration of detection systems after finding false alarms, together with a preliminary simulation and evaluation of the changes.

The focus of this paper is to propose an integration between Internet of Things (IoT) and Video Surveillance, with the aim to satisfy the requirements of the future needs of Video Surveillance, and to accomplish a better use. IoT is a new technology in the sector of telecommunications. It is a network that contains physical objects, items, and devices, which are embedded with sensors and software, thus enabling the objects, and allowing for their data exchange. Video Surveillance systems collect and exchange the data which has been recorded by sensors and cameras and send it through the network. This paper proposes an innovative topology paradigm which could offer a better use of IoT technology in Video Surveillance systems. Furthermore, the contribution of these technologies provided by Internet of Things features in dealing with the basic types of Video Surveillance technology with the aim to improve their use and to have a better transmission of video data through the network. Additionally, there is a comparison between our proposed topology and relevant proposed topologies focusing on the security issue.

Link quality protocols employ link quality estimators to collect statistics on the wireless link either independently or cooperatively among the sensor nodes. Furthermore, link quality routing protocols for wireless sensor networks may modify an estimator to meet their needs. Link quality estimators are vulnerable against malicious attacks that can exploit them. A malicious node may share false information with its neighboring sensor nodes to affect the computations of their estimation. Consequently, malicious node may behave maliciously such that its neighbors gather incorrect statistics about their wireless links. This paper aims to detect malicious nodes that manipulate the link quality estimator of the routing protocol. In order to accomplish this task, MINTROUTE and CTP routing protocols are selected and updated with intrusion detection schemes (IDSs) for further investigations with other factors. It is proved that these two routing protocols under scrutiny possess inherent susceptibilities, that are capable of interrupting the link quality calculations. Malicious nodes that abuse such vulnerabilities can be registered through operational detection mechanisms. The overall performance of the new LQR protocol with IDSs features is experimented, validated and represented via the detection rates and false alarm rates.

Science is conducted collaboratively, often requiring knowledge sharing about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs). An experiment, however, seldom includes only datasets, but more often includes software, its past execution, provenance, and associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While a necessary method, mere aggregation is not sufficient for the sharing of computational experiments. Other users must be able to easily recompute on these shared research objects. In this paper, we present the sciunit, a reusable research object in which aggregated content is recomputable. We describe a Git-like client that efficiently creates, stores, and repeats sciunits. We show through analysis that sciunits repeat computational experiments with minimal storage and processing overhead. Finally, we provide an overview of sharing and reproducible cyberinfrastructure based on sciunits gaining adoption in the domain of geosciences.

Provenance counterfeit and packet loss assaults are measured as threats in the large scale wireless sensor networks which are engaged for diverse application domains. The assortments of information source generate necessitate promising the reliability of information such as only truthful information is measured in the decision procedure. Details about the sensor nodes play an major role in finding trust value of sensor nodes. In this paper, a novel lightweight secure provenance method is initiated for improving the security of provenance data transmission. The anticipated system comprises provenance authentication and renovation at the base station by means of Merkle-Hellman knapsack algorithm based protected provenance encoding in the Bloom filter framework. Side Channel Monitoring (SCM) is exploited for noticing the presence of selfish nodes and packet drop behaviors. This lightweight secure provenance method decreases the energy and bandwidth utilization with well-organized storage and secure data transmission. The investigational outcomes establishes the efficacy and competence of the secure provenance secure system by professionally noticing provenance counterfeit and packet drop assaults which can be seen from the assessment in terms of provenance confirmation failure rate, collection error, packet drop rate, space complexity, energy consumption, true positive rate, false positive rate and packet drop attack detection.

Nowadays wireless networks are fast, becoming more secure than their wired counterparts. Recent technological advances in wireless networking, IC fabrication and sensor technology have lead to the emergence of millimetre scale devices that collectively form a Wireless Sensor Network (WSN) and are radically changing the way in which we sense, process and transport signals of interest. They are increasingly become viable solutions to many challenging problems and will successively be deployed in many areas in the future such as in environmental monitoring, business, and military applications. However, deploying new technology, without security in mind has often proved to be unreasonably dangerous. This also applies to WSNs, especially those used in applications that monitor sensitive information (e.g., health care applications). There have been significant contributions to overcome many weaknesses in sensor networks like coverage problems, lack in power and making best use of limited network bandwidth, however; work in sensor network security is still in its infancy stage. Security in WSNs presents several well-known challenges stemming from all kinds of resource constraints of individual sensors. The problem of securing these networks emerges more and more as a hot topic. Symmetric key cryptography is commonly seen as infeasible and public key cryptography has its own key distribution problem. In contrast to this prejudice, this paper presents a new symmetric encryption standard algorithm which is an extension of the previous work of the authors i.e. UES version-II and III. Roy et al recently developed few efficient encryption methods such as UES version-I, Modified UES-I, UES version-II, UES version-III. The algorithm is named as Ultra Encryption Standard version — IV algorithm. It is a Symmetric key Cryptosystem which includes multiple encryption, bit-wise reshuffling method and bit-wise columnar transposition method. In the present - ork the authors have performed the encryption process at the bit-level to achieve greater strength of encryption. The proposed method i.e. UES-IV can be used to encrypt short message, password or any confidential key.

The survey of related works on insider information security (IS) threats is presented. Special attention is paid to works that consider the insiders' behavioral models as it is very up-to-date for behavioral intrusion detection. Three key research directions are defined: 1) the problem analysis in general, including the development of taxonomy for insiders, attacks and countermeasures; 2) study of a specific IS threat with forecasting model development; 3) early detection of a potential insider. The models for the second and third directions are analyzed in detail. Among the second group the works on three IS threats are examined, namely insider espionage, cyber sabotage and unintentional internal IS violation. Discussion and a few directions for the future research conclude the paper.

Most of the social media platforms generate a massive amount of raw data that is slow-paced. On the other hand, Internet Relay Chat (IRC) protocol, which has been extensively used by hacker community to discuss and share their knowledge, facilitates fast-paced and real-time text communications. Previous studies of malicious IRC behavior analysis were mostly either offline or batch processing. This results in a long response time for data collection, pre-processing, and threat detection. However, since the threats can use the latest vulnerabilities to exploit systems (e.g. zero-day attack) and which can spread fast using IRC channels. Current IRC channel monitoring techniques cannot provide the required fast detection and alerting. In this paper, we present an alternative approach to overcome this limitation by providing real-time and autonomic threat detection in IRC channels. We demonstrate the capabilities of our approach using as an example the shadow brokers' leak exploit (the exploit leveraged by WannaCry ransomware attack) that was captured and detected by our framework.

A cyber-attack detection system issues alerts when an attacker attempts to coerce a trusted software application to perform unsafe actions on the attacker's behalf. One way of issuing such alerts is to create an application-agnostic cyber- attack detection system that responds to prevalent software vulnerabilities. The creation of such an autonomic alert system, however, is impeded by the disparity between implementation language, function, quality-of-service (QoS) requirements, and architectural patterns present in applications, all of which contribute to the rapidly changing threat landscape presented by modern heterogeneous software systems. This paper evaluates the feasibility of creating an autonomic cyber-attack detection system and applying it to several exemplar web-based applications using program transformation and machine learning techniques. Specifically, we examine whether it is possible to detect cyber-attacks (1) online, i.e., as they occur using lightweight structures derived from a call graph and (2) offline, i.e., using machine learning techniques trained with features extracted from a trace of application execution. In both cases, we first characterize normal application behavior using supervised training with the test suites created for an application as part of the software development process. We then intentionally perturb our test applications so they are vulnerable to common attack vectors and then evaluate the effectiveness of various feature extraction and learning strategies on the perturbed applications. Our results show that both lightweight on-line models based on control flow of execution path and application specific off-line models can successfully and efficiently detect in-process cyber-attacks against web applications.

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, houses, and cities, as well as electrical grids, gas plants, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that are pervasive, cost-effective, and can be accessed from anywhere and at any time. However, due to the exponential number of interconnected devices, cyber-security in the IoT is a major challenge. It heavily relies on the digital identity concept to build security mechanisms such as authentication and authorization. Current centralized identity management systems are built around third party identity providers, which raise privacy concerns and present a single point of failure. In addition, IoT unconventional characteristics such as scalability, heterogeneity and mobility require new identity management systems to operate in distributed and trustless environments, and uniquely identify a particular device based on its intrinsic digital properties and its relation to its human owner. In order to deal with these challenges, we present a Blockchain-based Identity Framework for IoT (BIFIT). We show how to apply our BIFIT to IoT smart homes to achieve identity self-management by end users. In the context of smart home, the framework autonomously extracts appliances signatures and creates blockchain-based identifies for their appliance owners. It also correlates appliances signatures (low level identities) and owners identifies in order to use them in authentication credentials and to make sure that any IoT entity is behaving normally.

In this paper, we present AnomalyDetect, an approach for detecting anomalies in cloud services. A cloud service consists of a set of interacting applications/processes running on one or more interconnected virtual machines. AnomalyDetect uses the Kalman Filter as the basis for predicting the states of virtual machines running cloud services. It uses the cloud service's virtual machine historical data to forecast potential anomalies. AnomalyDetect has been integrated with the AutoMigrate framework and serves as the means for detecting anomalies to automatically trigger live migration of cloud services to preserve their availability. AutoMigrate is a framework for developing intelligent systems that can monitor and migrate cloud services to maximize their availability in case of cloud disruption. We conducted a number of experiments to analyze the performance of the proposed AnomalyDetect approach. The experimental results highlight the feasibility of AnomalyDetect as an approach to autonomic cloud availability.

Remote Access Trojans (RATs) give remote attackers interactive control over a compromised machine. Unlike large-scale malware such as botnets, a RAT is controlled individually by a human operator interacting with the compromised machine remotely. The versatility of RATs makes them attractive to actors of all levels of sophistication: they've been used for espionage, information theft, voyeurism and extortion. Despite their increasing use, there are still major gaps in our understanding of RATs and their operators, including motives, intentions, procedures, and weak points where defenses might be most effective. In this work we study the use of DarkComet, a popular commercial RAT. We collected 19,109 samples of DarkComet malware found in the wild, and in the course of two, several-week-long experiments, ran as many samples as possible in our honeypot environment. By monitoring a sample's behavior in our system, we are able to reconstruct the sequence of operator actions, giving us a unique view into operator behavior. We report on the results of 2,747 interactive sessions captured in the course of the experiment. During these sessions operators frequently attempted to interact with victims via remote desktop, to capture video, audio, and keystrokes, and to exfiltrate files and credentials. To our knowledge, we are the first large-scale systematic study of RAT use.