Biblio

The collaborative recommendation mechanism is beneficial for the subject in an open network to find efficiently enough referrers who directly interacted with the object and obtain their trust data. The uncertainty analysis to the collected trust data selects the reliable trust data of trustworthy referrers, and then calculates the statistical trust value on certain reliability for any object. After that the subject can judge its trustworthiness and further make a decision about interaction based on the given threshold. The feasibility of this method is verified by three experiments which are designed to validate the model's ability to fight against malicious service, the exaggeration and slander attack. The interactive success rate is significantly improved by using the new model, and the malicious entities are distinguished more effectively than the comparative model.

Metaheuristic search technique is one of the advance approach when compared with traditional heuristic search technique. To select one option among different alternatives is not hard to get but really hard is give assurance that being cost effective. This hard problem is solved by the meta-heuristic search technique with the help of fitness function. Fitness function is a crucial metrics or a measure which helps in deciding which solution is optimal to choose from available set of test sets. This paper discusses hill climbing, simulated annealing, tabu search, genetic algorithm and particle swarm optimization techniques in detail explaining with the help of the algorithm. If metaheuristic search techniques combine some of the security testing methods, it would result in better searching technique as well as secure too. This paper primarily focusses on the metaheuristic search techniques.

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.

Software has emerged as a significant part of many domains, including financial service platforms, social networks and vehicle control. Standards organizations have responded to this by creating regulations to address issues such as safety and privacy. In this context, compliance of software with standards has emerged as a key issue. For software development organizations, compliance is a complex and costly goal to achieve and is often accomplished by producing so-called assurance cases, which demonstrate that the system indeed satisfies the property imposed by a standard (e.g., safety, privacy, security). As systems and standards undergo evolution for a variety of reasons, maintaining assurance cases multiplies the effort. In this work, we propose to exploit the connection between the field of model management and the problem of compliance management and propose methods that use model management techniques to address compliance scenarios such as assurance case evolution and reuse. For validation, we ground our approaches on the automotive domain and the ISO 26262 standard for functional safety of road vehicles.

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.

There is widening chasm between the ease of creating software and difficulty of "building security in". This paper reviews the approach, the findings and recent experiments from a seven-year effort to enable consistency across a large, diverse development organization and software portfolio via policies, guidance, automated tools and services. Experience shows that developing secure software is an elusive goal for most. It requires every team to know and apply a wide range of security knowledge in the context of what software is being built, how the software will be used, and the projected threats in the environment where the software will operate. The drive for better outcomes for secure development and increased developer productivity led to experiments to augment developer knowledge and eventually realize the goal of "building the right security in".

We present in this paper a security analysis of electronic devices which considers the lifecycle properties of embedded systems. We first define a generic model of electronic devices lifecycle showing the complex interactions between the numerous assets and the actors. The method is illustrated through a case study: a connected insulin pump. The lifecycle induced vulnerabilities are analyzed using the EBIOS methodology. An analysis of associated countermeasures points out the lack of consideration of the life cycle in order to provide an acceptable security level of each assets of the device.

In order to provide reliable security solution to the people, the concept of smart ATM security system based on Embedded Linux platform is suggested in this paper. The study is focused on Design and Implementation of Face Detection based ATM Security System using Embedded Linux Platform. The system is implemented on the credit card size Raspberry Pi board with extended capability of open source Computer Vision (OpenCV) software which is used for Image processing operation. High level security mechanism is provided by the consecutive actions such as initially system captures the human face and check whether the human face is detected properly or not. If the face is not detected properly, it warns the user to adjust him/her properly to detect the face. Still the face is not detected properly the system will lock the door of the ATM cabin for security purpose. As soon as the door is lock, the system will automatic generates 3 digit OTP code. The OTP code will be sent to the watchman's registered mobile number through SMS using GSM module which is connected with the raspberry Pi. Watchman will enter the generated OTP through keypad which is interfaced with the Pi Board. The OTP will be verified and if it is correct then door will be unlock otherwise it will remain lock.

Conventional cyber defenses require continual maintenance: virus, firmware, and software updates; costly functional impact tests; and dedicated staff within a security operations center. The conventional defenses require access to external sources for the latest updates. The whitelisted system, however, is ideally a system that can sustain itself freed from external inputs. Cyber-Physical Systems (CPS), have the following unique traits: digital commands are physically observable and verifiable; possible combinations of commands are limited and finite. These CPS traits, combined with a trust anchor to secure an unclonable digital identity (i.e., digitally unclonable function [DUF] - Patent Application \#15/183,454; CodeLock), offers an excellent opportunity to explore defenses built on whitelisting approach called “Trustworthy Design Architecture (TDA).” There exist significant research challenges in defining what are the physically verifiable whitelists as well as the criteria for cyber-physical traits that can be used as the unclonable identity. One goal of the project is to identify a set of physical and/or digital characteristics that can uniquely identify an endpoint. The measurements must have the properties of being reliable, reproducible, and trustworthy. Given that adversaries naturally evolve with any defense, the adversary will have the goal of disrupting or spoofing this process. To protect against such disruptions, we provide a unique system engineering technique, when applied to CPSs (e.g., nuclear processing facilities, critical infrastructures), that will sustain a secure operational state without ever needing external information or active inputs from cybersecurity subject-matter experts (i.e., virus updates, IDS scans, patch management, vulnerability updates). We do this by eliminating system dependencies on external sources for protection. Instead, all internal co- munication is actively sealed and protected with integrity, authenticity and assurance checks that only cyber identities bound to the physical component can deliver. As CPSs continue to advance (i.e., IoTs, drones, ICSs), resilient-maintenance free solutions are needed to neutralize/reduce cyber risks. TDA is a conceptual system engineering framework specifically designed to address cyber-physical systems that can potentially be maintained and operated without the persistent need or demand for vulnerability or security patch updates.

Vehicular ad-Hoc Networks (VANETs) have been promoted as a key technology that can provide a wide variety of services such as traffic management, passenger safety, as well as travel convenience and comfort. VANETs are now proposed to be part of the upcoming Fifth Generation (5G) technology, integrated with Software Defined Networking (SDN), as key enabler of 5G. The technology of fog computing in 5G turned out to be an adequate solution for faster processing in delay sensitive application, such as VANETs, being a hybrid solution between fully centralized and fully distributed networks. In this paper, we propose a three-way integration between VANETs, SDN, and 5G for a resilient VANET security design approach, which strikes a good balance between network, mobility, performance and security features. We show how such an approach can secure VANETs from different types of attacks such as Distributed Denial of Service (DDoS) targeting either the controllers or the vehicles in the network, and how to trace back the source of the attack. Our evaluation shows the capability of the proposed system to enforce different levels of real-time user-defined security, while maintaining low overhead and minimal configuration.

Control plane distribution on Software Defined Networking enhances security, performance and scalability of the network. In this paper, we propose an efficient architecture for distribution of controllers. The main contributions of the proposed architecture are: i) A controller distributed areas to ensure security, performance and scalability of the network; ii) A single database maintained by a designated controller to provide consistency to the control plane; iii) An optimized heuristic for locating controllers to reduce latency in the control plane; iv) A resilient mechanism of choosing the designated controller to ensure the proper functioning of the network, even when there are failures. A prototype of the proposal was implemented and the placement heuristic was analyzed in real topologies. The results show that connectivity is maintained even in failure scenarios. Finally, we show that the placement optimization reduces the average latency of controllers. Our proposed heuristic achieves a fair distribution of controllers and outperforms the network resilience of other heuristics up to two times better.

Customer Edge Switching (CES) is an experimental Internet architecture that provides reliable and resilient multi-domain communications. It provides resilience against security threats because domains negotiate inbound and outbound policies before admitting new traffic. As CES and its signalling protocols are being prototyped, there is a need for independent testing of the CES architecture. Hence, our research goal is to develop an automated test framework that CES protocol designers and early adopters can use to improve the architecture. The test framework includes security, functional, and performance tests. Using the Robot Framework and STRIDE analysis, in this paper we present this automated security test framework. By evaluating sample test scenarios, we show that the Robot Framework and our CES test suite have provided productive discussions about this new architecture, in addition to serving as clear, easy-to-read documentation. Our research also confirms that test automation can be useful to improve new protocol architectures and validate their implementation.

We present a novel multimodal fusion model for affective content analysis, combining visual, audio and deep visual-sentiment descriptors from the media content with automated facial action measurements from naturalistic responses to the media. We collected a dataset of 48,867 facial responses to 384 media clips and extracted a rich feature set from the facial responses and media content. The stimulus videos were validated to be informative, inspiring, persuasive, sentimental or amusing. By combining the features, we were able to obtain a classification accuracy of 63% (weighted F1-score: 0.62) for a five-class task. This was a significant improvement over using the media content features alone. By analyzing the feature sets independently, we found that states of informed and persuaded were difficult to differentiate from facial responses alone due to the presence of similar sets of action units in each state (AU 2 occurring frequently in both cases). Facial actions were beneficial in differentiating between amused and informed states whereas media content features alone performed less well due to similarities in the visual and audio make up of the content. We highlight examples of content and reactions from each class. This is the first affective content analysis based on reactions of 10,000s of people.

Mission assurance requires effective, near-real time defensive cyber operations to appropriately respond to cyber attacks, without having a significant impact on operations. The ability to rapidly compute, prioritize and execute network-based courses of action (CoAs) relies on accurate situational awareness and mission-context information. Although diverse solutions exist for automatically collecting and analysing infrastructure data, few deliver automated analysis and implementation of network-based CoAs in the context of the ongoing mission. In addition, such processes can be operatorintensive and available tools tend to be specific to a set of common data sources and network responses. To address these issues, Defence Research and Development Canada (DRDC) is leading the development of the Automated Computer Network Defence (ARMOUR) technology demonstrator and cyber defence science and technology (S&T) platform. ARMOUR integrates new and existing off-the-shelf capabilities to provide enhanced decision support and to automate many of the tasks currently executed manually by network operators. This paper describes the cyber defence integration framework, situational awareness, and automated mission-oriented decision support that ARMOUR provides.

Mobile tracking is a key challenge that has been investigated from both practical and theoretical aspects. This paper proposes an anti-theft mobile phone security system using basic input/output system (BIOS). This mobile phone security system allows us to determine the position of mobile device. The proposed security system is based on hardware implementation technique in which mobile is designed in such a way that a mobile can be traced out even if battery and Subscriber Identity Module (SIM) are plug-out. Furthermore, we also consider the usage of BIOS and its importance in our daily life. Our proposed solution will help the designers in improving the device security.

Servers in a network are typically assigned a static identity. Static assignment of identities is a cornerstone for adversaries in finding targets. Moving Target Defense (MTD) mutates the environment to increase unpredictability for an attacker. On another side, Software Defined Networks (SDN) facilitate a global view of a network through a central control point. The potential of SDN can not only make network management flexible and convenient, but it can also assist MTD to enhance attack surface obfuscation. In this paper, we propose an effective framework for the prevention, detection, and mitigation of flooding-based Denial of Service (DoS) attacks. Our framework includes a light-weight SDN assisted MTD strategy for network reconnaissance protection and an efficient approach for tackling DoS attacks using Software Defined-Internet Exchange Point (SD-IXP). To assess the effectiveness of the MTD strategy and DoS mitigation scheme, we set two different experiments. Our results confirm the effectiveness of our framework. With the MTD strategy in place, at maximum, barely 16% reconnaissance attempts were successful while the DoS attacks were accurately detected with false alarm rate as low as 7.1%.

Software Defined Networking (SDN) has proved to be a promising approach for creating next generation software based network ecosystems. It has provided us with a centralized network provision, a holistic management plane and a well-defined level of abstraction. But, at the same time brings forth new security and management challenges. Research in the field of SDN is primarily focused on reconfiguration, forwarding and network management issues. However in recent times the interest has moved to tackling security and maintenance issues. This work is based on providing a means to mitigate security challenges in an SDN environment from a DDoS attack based point of view. This paper introduces a Multi-Agent based intrusion prevention and mitigation architecture for SDN. Thus allowing networks to govern their behavior and take appropriate measures when the network is under attack. The architecture is evaluated against filter based intrusion prevention architectures to measure efficiency and resilience against DDoS attacks and false policy based attacks.

Securing Internet of Things (IoT) systems is a challenge because of its multiple points of vulnerability. A spate of recent hacks and security breaches has unveiled glaring vulnerabilities in the IoT. Due to the computational and memory requirement constraints associated with anomaly detection algorithms in core networks, commercial in-line (part of the direct line of communication) Anomaly Detection Systems (ADSs) rely on sampling-based anomaly detection approaches to achieve line rates and truly-inline anomaly detection accuracy in real-time. However, packet sampling is inherently a lossy process which might provide an incomplete and biased approximation of the underlying traffic patterns. Moreover, commercial routers uses proprietary software making them closed to be manipulated from the outside. As a result, detecting malicious packets on the given network path is one of the most challenging problems in the field of network security. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and mitigate DDoS attacks. Unlike sampling-based approaches for anomaly detection and limitation of proprietary software at routers, we use the SDN infrastructure to relax the sampling-based ADS constraints and collect traffic flow statistics which are maintained at each SDN-enabled switch to achieve high detection accuracy. In order to implement our idea, we discuss how to mitigate DDoS attacks using the features of SDN infrastructure.

Distributed Denial of Service (DDoS) attack has been bringing serious security concerns on banks, finance incorporation, public institutions, and data centers. Also, the emerging wave of Internet of Things (IoT) raises new concerns on the smart devices. Software Defined Networking (SDN) and Network Functions Virtualization (NFV) have provided a new paradigm for network security. In this paper, we propose a new method to efficiently prevent DDoS attacks, based on a SDN/NFV framework. To resolve the problem that normal packets are blocked due to the inspection on suspicious packets, we developed a threshold-based method that provides a client with an efficient, fast DDoS attack mitigation. In addition, we use open source code to develop the security functions in order to implement our solution for SDN-based network security functions. The source code is based on NETCONF protocol [1] and YANG Data Model [2].

Software-defined networks offer a promising framework for the implementation of cross-layer data-centric security policies in military systems. An important aspect of the design process for such advanced security solutions is the thorough experimental assessment and validation of proposed technical concepts prior to their deployment in operational military systems. In this paper, we describe an OpenFlow-based testbed, which was developed with a specific focus on validation of SDN security mechanisms - including both the mechanisms for protecting the software-defined network layer and the cross-layer enforcement of higher level policies, such as data-centric security policies. We also present initial experimentation results obtained using the testbed, which confirm its ability to validate simulation and analytic predictions. Our objective is to provide a sufficiently detailed description of the configuration used in our testbed so that it can be easily re-plicated and re-used by other security researchers in their experiments.

Protecting data confidentiality and integrity has become increasingly important in modern software. Sometimes, access control mechanisms come short and solutions on the application-level are needed. An approach can rely on enforcing information security using some features provided by certain programming languages. Several different solutions addressing this problem have been presented in literature, and entire new languages or libraries have been built from scratch. Some of them use type systems to let the compiler check for vulnerable code. In this way we are able to rule out those implementations which do not meet a certain security requirement. In this paper we use Haskell's type system to enforce three key properties of information security: non-interference and flexible declassification policies, strict input validation, and secure computations on untainted and trusted values. We present a functional lightweight library for applications with data integrity and confidentiality issues. Our contribute relies on a compile time enforcing of the aforementioned properties. Our library is wholly generalized and might be adapted for satisfying almost every security requirement.

Signature extraction is a critical preprocessing step in forensic log analysis because it enables sophisticated analysis techniques to be applied to logs. Currently, most signature extraction frameworks either use rule-based approaches or handcrafted algorithms. Rule-based systems are error-prone and require high maintenance effort. Hand-crafted algorithms use heuristics and tend to work well only for specialized use cases. In this paper we present a novel approach to extract signatures from forensic logs that is based on a neural language model. This language model learns to identify mutable and non-mutable parts in a log message. We use this information to extract signatures. Neural language models have shown to work extremely well for learning complex relationships in natural language text. We experimentally demonstrate that our model can detect which parts are mutable with an accuracy of 86.4%. We also show how extracted signatures can be used for clustering log lines.

With Software Defined Networking (SDN) the control plane logic of forwarding devices, switches and routers, is extracted and moved to an entity called SDN controller, which acts as a broker between the network applications and physical network infrastructure. Failures of the SDN controller inhibit the network ability to respond to new application requests and react to events coming from the physical network. Despite of the huge impact that a controller has on the network performance as a whole, a comprehensive study on its failure dynamics is still missing in the state of the art literature. The goal of this paper is to analyse, model and evaluate the impact that different controller failure modes have on its availability. A model in the formalism of Stochastic Activity Networks (SAN) is proposed and applied to a case study of a hypothetical controller based on commercial controller implementations. In case study we show how the proposed model can be used to estimate the controller steady state availability, quantify the impact of different failure modes on controller outages, as well as the effects of software ageing, and impact of software reliability growth on the transient behaviour.

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.

Several defect prediction models proposed are effective when historical datasets are available. Defect prediction becomes difficult when no historical data exist. Cross-project defect prediction (CPDP), which uses projects from other sources/companies to predict the defects in the target projects proposed in recent studies has shown promising results. However, the performance of most CPDP approaches are still beyond satisfactory mainly due to distribution mismatch between the source and target projects. In this study, a credibility theory based Naïve Bayes (CNB) classifier is proposed to establish a novel reweighting mechanism between the source projects and target projects so that the source data could simultaneously adapt to the target data distribution and retain its own pattern. Our experimental results show that the feasibility of the novel algorithm design and demonstrate the significant improvement in terms of the performance metrics considered achieved by CNB over other CPDP approaches.