Biblio

This paper presents a solution to a multiple-model based stochastic active fault diagnosis problem over the infinite-time horizon. A general additive detection cost criterion is considered to reflect the objectives. Since the system state is unknown, the design consists of a perfect state information reformulation and optimization problem solution by approximate dynamic programming. An adaptive particle filter state estimation algorithm based on the efficient sample size is proposed to maintain the estimate quality while reducing computational costs. A reduction of information statistics of the state is carried out using non-resampled particles to make the solution feasible. Simulation results illustrate the effectiveness of the proposed design.

This paper proposes a design method of a support tool for detection and diagnosis of failures in discrete event systems (DES). The design of this diagnoser goes through three phases: an identification phase and finding paths and temporal parameters of the model describing the two modes of normal and faulty operation, a detection phase provided by the comparison and monitoring time operation and a location phase based on the combination of the temporal evolution of the parameters and thresholds exceeded technique. Our contribution lays in the application of this technique in the presence of faults arising simultaneously, sensors and actuators. The validation of the proposed approach is illustrated in a filling system through a simulation.

Situational awareness during sophisticated cyber attacks on the power grid is critical for the system operator to perform suitable attack response and recovery functions to ensure grid reliability. The overall theme of this paper is to identify existing practical issues and challenges that utilities face while monitoring substations, and to suggest potential approaches to enhance the situational awareness for the grid operators. In this paper, we provide a broad discussion about the various gaps that exist in the utility industry today in monitoring substations, and how those gaps could be addressed by identifying the various data sources and monitoring tools to improve situational awareness. The paper also briefly describes the advantages of contextualizing and correlating substation monitoring alerts using expert systems at the control center to obtain a holistic systems-level view of potentially malicious cyber activity at the substations before they cause impacts to grid operation.

This paper introduces SONA (Spatiotemporal system Organized for Natural Analysis), a tabletop and tangible controller system for exploring geotagged information, and more specifically, CCTV. SONA's goal is to support a more natural method of interacting with data. Our new interactions are placed in the context of a physical security environment, closed circuit television (CCTV). We present a three-layered detail on demand set of view filters for CCTV feeds on a digital map. These filters are controlled with a novel tangible device for direct interaction. We validate SONA's tangible controller approach with a user study comparing SONA with the existing CCTV multi-screen method. The results of the study show that SONA's tangible interaction method is superior to the multi-screen approach, both in terms of quantitative results, and is preferred by users.

A lack of awareness surrounding secure online behaviour can lead to end-users, and their personal details becoming vulnerable to compromise. This paper describes an ongoing research project in the field of usable security, examining the relationship between end-user-security behaviour, and the use of affective feedback to educate end-users. Part of the aforementioned research project considers the link between categorical information users reveal about themselves online, and the information users believe, or report that they have revealed online. The experimental results confirm a disparity between information revealed, and what users think they have revealed, highlighting a deficit in security awareness. Results gained in relation to the affective feedback delivered are mixed, indicating limited short-term impact. Future work seeks to perform a long-term study, with the view that positive behavioural changes may be reflected in the results as end-users become more knowledgeable about security awareness.

Traditional deception-based cyber defenses often undertake reactive strategies that utilize decoy systems or services for attack detection and information gathering. Unfortunately, the effectiveness of these defense mechanisms has been largely constrained by the low decoy fidelity, the poor scalability of decoy platform, and the static decoy configurations, which allow the attackers to identify and bypass the deployed decoys. In this paper, we develop a decoy-enhanced defense framework that can proactively protect critical servers against targeted remote attacks through deception. To achieve both high fidelity and good scalability, our system follows a hybrid architecture that separates lightweight yet versatile front-end proxies from back-end high-fidelity decoy servers. Moreover, our system can further invalidate the attackers' reconnaissance through dynamic proxy address shuffling. To guarantee service availability, we develop a transparent connection translation strategy to maintain existing connections during shuffling. Our evaluation on a prototype implementation demonstrates the effectiveness of our approach in defeating attacker reconnaissance and shows that it only introduces small performance overhead.

Edge Computing is a scheme to improve the performance, latency and security guidelines for IoT applications. However, edge deployment of an application also comes with additional complexity in management, an increased attack surface for security vulnerability, and could potentially result in a more expensive solution. As a result, the conditions under which an edge deployment of IoT applications delivers a better solution is not always obvious. Metrics which would be able to predict whether or not an IoT application is suitable for edge deployment can provide useful insights to address this question. In this paper, we examine the key performance indicators for IoT applications, namely the responsiveness, scalability and cost models for different types of IoT applications. Our analysis identifies that network centrality of an IoT application is a key characteristic which determines whether or not an IoT application is a good candidate for edge deployment. We discuss the different measures of network centrality that can be used to characterize applications, and the relative performance of edge deployment compared to centralized deployment for various IoT applications.

Numerous event-based probing methods exist for cloud computing environments allowing a hypervisor to gain insight into guest activities. Such event-based probing has been shown to be useful for detecting attacks, system hangs through watchdogs, and for inserting exploit detectors before a system can be patched, among others. Here, we illustrate how to use such probing for trustworthy logging and highlight some of the challenges that existing event-based probing mechanisms do not address. Challenges include ensuring a probe inserted at given address is trustworthy despite the lack of attestation available for probes that have been inserted dynamically. We show how probes can be inserted to ensure proper logging of every invocation of a probed instruction. When combined with attested boot of the hypervisor and guest machines, we can ensure the output stream of monitored events is trustworthy. Using these techniques we build a trustworthy log of certain guest-system-call events. The log powers a cloud-tuned Intrusion Detection System (IDS). New event types are identified that must be added to existing probing systems to ensure attempts to circumvent probes within the guest appear in the log. We highlight the overhead penalties paid by guests to increase guarantees of log completeness when faced with attacks on the guest kernel. Promising results (less that 10% for guests) are shown when a guest relaxes the trade-off between log completeness and overhead. Our demonstrative IDS detects common attack scenarios with simple policies built using our guest behavior recording system.

Thanks to advancement of vehicle technologies, Unmanned Aerial Vehicle (UAV) now widely spread over practical services and applications affecting daily life of people positively. Especially, multiple heterogeneous UAVs with different capabilities should be considered since UAVs can play an important role in Internet of Things (IoT) environment in which the heterogeneity and the multi domain of UAVs are indispensable. Also, a concept of barrier-coverage has been proved as a promising one applicable to surveillance and security. In this paper, we present collision-free reinforced barriers by heterogeneous UAVs to support multi domain. Then, we define a problem which is to minimize maximum movement of UAVs on condition that a property of collision-free among UAVs is assured while they travel from current positions to specific locations so as to form reinforced barriers within multi domain. Because the defined problem depends on how to locate UAVs on barriers, we develop a novel approach that provides a collision-free movement as well as a creation of virtual lines in multi domain. Furthermore, we address future research topics which should be handled carefully for the barrier-coverage by heterogeneous UAVs.

Internet-connected embedded systems have limited capabilities to defend themselves against remote hacking attacks. The potential effects of such attacks, however, can have a significant impact in the context of the Internet of Things, industrial control systems, smart health systems, etc. Embedded systems cannot effectively utilize existing software-based protection mechanisms due to limited processing capabilities and energy resources. We propose a novel hardware-based monitoring technique that can detect if the embedded operating system or any running application deviates from the originally programmed behavior due to an attack. We present an FPGA-based prototype implementation that shows the effectiveness of such a security approach.

Cyber-physical systems (CPS) are interconnections of heterogeneous hardware and software components (e.g., sensors, actuators, physical systems/processes, computational nodes and controllers, and communication subsystems). Increasing network connectivity of CPS computational nodes facilitates maintenance and on-demand reprogrammability and reduces operator workload. However, such increasing connectivity also raises the potential for cyber-attacks that attempt unauthorized modifications of run-time parameters or control logic in the computational nodes to hamper process stability or performance. In this paper, we analyze the effectiveness of real-time monitoring using digital and analog side channels. While analog side channels might not typically provide sufficient granularity to observe each iteration of a periodic loop in the code in the CPS device, the temporal averaging inherent to side channel sensory modalities enables observation of persistent changes to the contents of a computational loop through their resulting effect on the level of activity of the device. Changes to code can be detected by observing readings from side channel sensors over a period of time. Experimental studies are performed on an ARM-based single board computer.

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.

One challenge for engineered cyber physical systems (CPSs) is the possibility for a malicious intruder to change the data transmitted across the cyber channel as a means to degrade the performance of the physical system. In this paper, we consider a data injection attack on a cyber physical system. We propose a hybrid framework for detecting the presence of an attack and operating the plant in spite of the attack. Our method uses an observer-based detection mechanism and a passivity balance defense framework in the hybrid architecture. By switching the controller, passivity and exponential stability are established under the proposed framework.

This paper focuses on exploitable cyber vulnerabilities in industrial control systems (ICS) and on a new approach of resiliency against them. Even with numerous metrics and methods for intrusion detection and mitigation strategy, a complete detection and deterrence of cyber-attacks for ICS is impossible. Countering the impact and consequence of possible malfunctions caused by such attacks in the safety-critical ICS's, this paper proposes new controller architecture to fail-operate even under compromised situations. The proposed new ICS is realized with diversification of hardware/software and unidirectional communication in alerting suspicious infiltration to upper-level management. Equipped with control bus monitoring, this operation-basis approach of infiltration detection would become a truly cyber-resilient ICS. The proposed system is tested in a lab hardware experimentation setup and on a cybersecurity test bed, DeterLab, for validation.

Life-cycle management of stateful VNF services is a complicated task, especially when automated resiliency and scaling should be handled in a secure manner, without service degradation. We present FlowSNAC, a resilient and scalable VNF service for user authentication and service deployment. FlowSNAC consists of both stateful and stateless components, some of that are SDN-based and others that are NFVs. We describe how it adapts to changing conditions by automatically updating resource allocations through a series of intermediate steps of traffic steering, resource allocation, and secure state transfer. We conclude by highlighting some of the lessons learned during implementation, and their wider consequences for the architecture of SDN/NFV management and orchestration systems.

A growing need for scalable solutions for both machine learning and interactive analytics exists in the area of cyber-security. Machine learning aims at segmentation and classification of log events, which leads towards optimization of the threat monitoring processes. The tools for interactive analytics are required to resolve the uncertain cases, whereby machine learning algorithms are not able to provide a convincing outcome and human expertise is necessary. In this paper we focus on a case study of a security operations platform, whereby typical layers of information processing are integrated with a new database engine dedicated to approximate analytics. The engine makes it possible for the security experts to query massive log event data sets in a standard relational style. The query outputs are received orders of magnitude faster than any of the existing database solutions running with comparable resources and, in addition, they are sufficiently accurate to make the right decisions about suspicious corner cases. The engine internals are driven by the principles of information granulation and summary-based processing. They also refer to the ideas of data quantization, approximate computing, rough sets and probability propagation. In the paper we study how the engine's parameters can influence its performance within the considered environment. In addition to the results of experiments conducted on large data sets, we also discuss some of our high level design decisions including the choice of an approximate query result accuracy measure that should reflect the specifics of the considered threat monitoring operations.

Security in virtualised environments is becoming increasingly important for institutions, not only for a firm's own on-site servers and network but also for data and sites that are hosted in the cloud. Today, security is either handled globally by the cloud provider, or each customer needs to invest in its own security infrastructure. This paper proposes a Virtual Security Operation Center (VSOC) that allows to collect, analyse and visualize security related data from multiple sources. For instance, a user can forward log data from its firewalls, applications and routers in order to check for anomalies and other suspicious activities. The security analytics provided by the VSOC are comparable to those of commercial security incident and event management (SIEM) solutions, but are deployed as a cloud-based solution with the additional benefit of using big data processing tools to handle large volumes of data. This allows us to detect more complex attacks that cannot be detected with todays signature-based (i.e. rules) SIEM solutions.

Wireless sensor network is a low cost network to solve many of the real world problems. These sensor nodes used to deploy in the hostile or unattended areas to sense and monitor the atmospheric situations such as motion, pressure, sound, temperature and vibration etc. The sensor nodes have low energy and low computing power, any security scheme for wireless sensor network must not be computationally complex and it should be efficient. In this paper we introduced a secure routing protocol for WSNs, which is able to prevent the network from DDoS attack. In our methodology we scan the infected nodes using the proposed algorithm and block that node from any further activities in the network. To protect the network we use intrusion prevention scheme, where specific nodes of the network acts as IPS node. These nodes operate in their radio range for the region of the network and scan the neighbors regularly. When the IPS node find a misbehavior node which is involves in frequent message passing other than UDP and TCP messages, IPS node blocks the infected node and also send the information to all genuine sender nodes to change their routes. All simulation work has been done using NS 2.35. After simulation the proposed scheme gives feasible results to protect the network against DDoS attack. The performance parameters have been improved after applying the security mechanism on an infected network.

Securing Internet of Things is a challenge because of its multiple points of vulnerability. In particular, Distributed Denial of Service (DDoS) attacks on IoT devices pose a major security challenge to be addressed. In this paper, we propose a DNS query-based DDoS attack mitigation system using Software-Defined Networking (SDN) to block the network traffic for DDoS attacks. With some features provided by SDN, we can analyze traffic patterns and filter suspicious network flows out. To show the feasibility of the proposed system, we particularly implemented a prototype with Dirichlet process mixture model to distinguish benign traffic from malicious traffic and conducted experiments with the dataset collected from real network traces. We demonstrate the effectiveness of the proposed method by both simulations and experiment data obtained from the real network traffic traces.

Software-Defined Networking (SDN) allows for fast reactions to security threats by dynamically enforcing simple forwarding rules as counter-measures. However, in classic SDN all the intelligence resides at the controller, with the switches only capable of performing stateless forwarding as ruled by the controller. It follows that the controller, in addition to network management and control duties, must collect and process any piece of information required to take advanced (stateful) forwarding decisions. This threatens both to overload the controller and to congest the control channel. On the other hand, stateful SDN represents a new concept, developed both to improve reactivity and to offload the controller and the control channel by delegating local treatments to the switches. In this paper, we adopt this stateful paradigm to protect end-hosts from Distributed Denial of Service (DDoS). We propose StateSec, a novel approach based on in-switch processing capabilities to detect and mitigate DDoS attacks. StateSec monitors packets matching configurable traffic features (e.g., IP src/dst, port src/dst) without resorting to the controller. By feeding an entropy-based algorithm with such monitoring features, StateSec detects and mitigates several threats such as (D)DoS and port scans with high accuracy. We implemented StateSec and compared it with a state-of-the-art approach to monitor traffic in SDN. We show that StateSec is more efficient: it achieves very accurate detection levels, limiting at the same time the control plane overhead.

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.

With the advent of smart devices and lowering prices of sensing devices, adoption of Internet of Things (IoT) is gaining momentum. These IoT devices come with greater threat of being attacked or compromised that could lead to Denial of Service (DoS) and Distributed Denial of Service (DDoS). The high volume of IoT devices with high level of heterogeneity, magnify the possibility of security threats. So far, there is no protocol to guarantee the security of IoT devices. But to enable resilience, continuous monitoring is required along with adaptive decision making. These challenges can be addressed with the help of Software Defined Networking (SDN) which can effectively handle the security threats to the IoT devices in dynamic and adaptive manner without any burden on the IoT devices. In this paper, we propose an SDN-based secure IoT framework called SoftThings to detect abnormal behaviors and attacks as early as possible and mitigate as appropriate. Machine Learning is used at the SDN controller to monitor and learn the behavior of IoT devices over time. We have conducted experiments on Mininet emulator. Initial results show that this framework is capable to detect attacks on IoT with around 98% precision.

In the era of Big Data, software systems can be affected by its growing complexity, both with respect to functional and non-functional requirements. As more and more people use software applications over the web, the ability to recognize if some of this traffic is malicious or legitimate is a challenge. The traffic load of security controllers, as well as the complexity of security rules to detect attacks can grow to levels where current solutions may not suffice. In this work, we propose a hierarchical distributed architecture for security control in order to partition responsibility and workload among many security controllers. In addition, our architecture proposes a more simplified way of defining security rules to allow security to be enforced on an operational level, rather than a development level.

The increased number of cyber attacks makes the availability of services a major security concern. One common type of cyber threat is distributed denial of service (DDoS). A DDoS attack is aimed at disrupting the legitimate users from accessing the services. It is easier for an insider having legitimate access to the system to deceive any security controls resulting in insider attack. This paper proposes an Early Detection and Isolation Policy (EDIP)to mitigate insider-assisted DDoS attacks. EDIP detects insider among all legitimate clients present in the system at proxy level and isolate it from innocent clients by migrating it to attack proxy. Further an effective algorithm for detection and isolation of insider is developed with the aim of maximizing attack isolation while minimizing disruption to benign clients. In addition, concept of load balancing is used to prevent proxies from getting overloaded.

Nowadays the adoption of IoT solutions is gaining high momentum in several fields, including energy, home and environment monitoring, transportation, and manufacturing. However, cybersecurity attacks to low-cost end-user devices can severely undermine the expected deployment of IoT solutions in a broad range of scenarios. To face these challenges, emerging software-based networking features can introduce new security enablers, providing further scalability and flexibility required to cope with massive IoT. In this paper, we present a novel framework aiming to exploit SDN/NFV-based security features and devise new efficient integration with existing IoT security approaches. The potential benefits of the proposed framework is validated in two case studies. Finally, a feasibility study is presented, accounting for potential interactions with open-source SDN/NFV projects and relevant standardization activities.