Biblio

Mobile Ad-hoc Networks are dynamic in nature and do not have fixed infrastructure to govern nodes in the networks. The mission lies ahead in coordinating among such dynamically shifting nodes. The root problem of identifying and isolating misbehaving nodes that refuse to forward packets in multi-hop ad hoc networks is solved by the development of a comprehensive system called Audit-based Misbehavior Detection (AMD) that can efficiently isolates selective and continuous packet droppers. AMD evaluates node behavior on a per-packet basis, without using energy-expensive overhearing techniques or intensive acknowledgment schemes. Moreover, AMD can detect selective dropping attacks even in end-to-end encrypted traffic and can be applied to multi-channel networks. Game theoretical approaches are more suitable in deciding upon the reward mechanisms for which the mobile nodes operate upon. Rewards or penalties have to be decided by ensuring a clean and healthy MANET environment. A non-routine yet surprise alterations are well required in place in deciding suitable and safe reward strategies. This work focuses on integrating a Audit-based Misbehaviour Detection (AMD)scheme and an incentive based reputation scheme with game theoretical approach called Supervisory Game to analyze the selfish behavior of nodes in the MANETs environment. The proposed work GAMD significantly reduces the cost of detecting misbehavior nodes in the network.

Most of the countries evaluate their energy networks in terms of national security and define as critical infrastructure. Monitoring and controlling of these systems are generally provided by Industrial Control Systems (ICSs) and/or Supervisory Control and Data Acquisition (SCADA) systems. Therefore, this study focuses on the cyber-attack vectors on SCADA systems to research the threats and risks targeting them. For this purpose, TCP/IP based protocols used in SCADA systems have been determined and analyzed at first. Then, the most common cyber-attacks are handled systematically considering hardware-side threats, software-side ones and the threats for communication infrastructures. Finally, some suggestions are given.

Measurers are critical to a remote attestation (RA) system to verify the integrity of a remote untrusted host. Run-time measurers in a dynamic RA system sample the dynamic program state of the host to form evidence in order to establish trust by a remote system (appraiser). However, existing run-time measurers are tightly integrated with specific software. Such measurers need to be generated anew for each software, which is a manual process that is both challenging and tedious. In this paper we present a novel approach to decouple application-specific measurement policies from the measurers tasked with performing the actual run-time measurement. We describe MSRR (MeaSeReR), a novel general-purpose measurement framework that is agnostic of the target application. We show how measurement policies written per application can use MSRR, eliminating much time and effort spent on reproducing core measurement functionality. We describe MSRR's robust querying language, which allows the appraiser to accurately specify the what, when, and how to measure. We evaluate MSRR's overhead and demonstrate its functionality.

The dynamically changing landscape of DDoS threats increases the demand for advanced security solutions. The rise of massive IoT botnets enables attackers to mount high-intensity short-duration ”volatile ephemeral” attack waves in quick succession. Therefore the standard human-in-the-loop security center paradigm is becoming obsolete. To battle the new breed of volatile DDoS threats, the intrusion detection system (IDS) needs to improve markedly, at least in reaction times and in automated response (mitigation). Designing such an IDS is a daunting task as network operators are traditionally reluctant to act - at any speed - on potentially false alarms. The primary challenge of a low reaction time detection system is maintaining a consistently low false alarm rate. This paper aims to show how a practical FPGA-based DDoS detection and mitigation system can successfully address this. Besides verifying the model and algorithms with real traffic ”in the wild”, we validate the low false alarm ratio. Accordingly, we describe a methodology for determining the false alarm ratio for each involved threat type, then we categorize the causes of false detection, and provide our measurement results. As shown here, our methods can effectively mitigate the volatile ephemeral DDoS attacks, and accordingly are usable both in human out-of-loop and on-the-loop next-generation security solutions.

Virtual Machine Introspection (VMI) is an emerging family of techniques for extracting data from virtual machines without the use of active monitoring probes within the target machines themselves. In VMI based systems, the data is collected at the hypervisor-level by analyzing the state of virtual machines. This has the benefit of making collection harder to detect and block by malware as there is nothing in the machine indicating that monitoring is taking place. In this paper we present Nitro Web, a web-based monitoring system for virtual machines that uses virtual machine introspection for data collection. The platform is capable of detecting and visualizing system call activity taking place within virtual machines in real-time. The secondary purpose of this paper is to offer an introduction to Nitro virtual machine introspection framework that we have been involved in developing. In this paper, we reflect on how Nitro Framework can be used for building applications making use of VMI data.

Air-gap data is important for the security of computer systems. The injection of the computer virus is limited but possible, however data communication channel is necessary for the transmission of stolen data. This paper considers BFSK digital modulation applied to brightness changes of screen for unidirectional transmission of valuable data. Experimental validation and limitations of the proposed technique are provided.

Reliable operation of power systems is a primary challenge for the system operators. With the advancement in technology and grid automation, power systems are becoming more vulnerable to cyber-attacks. The main goal of adversaries is to take advantage of these vulnerabilities and destabilize the system. This paper describes a game-theoretic approach to attacker / defender modeling in power systems. In our models, the attacker can strategically identify the subset of substations that maximize damage when compromised. However, the defender can identify the critical subset of substations to protect in order to minimize the damage when an attacker launches a cyber-attack. The algorithms for these models are applied to the standard IEEE-14, 39, and 57 bus examples to identify the critical set of substations given an attacker and a defender budget.

Cyber-Physical Systems (CPS), such as Water Distribution Networks (WDNs), deploy digital devices to monitor and control the behavior of physical processes. These digital devices, however, are susceptible to cyber and physical attacks, that may alter their functionality, and therefore the integrity of their measurements/actions. In practice, industrial control systems utilize simple control laws, which rely on various sensor measurements and algorithms which are expected to operate normally. To reduce the impact of a potential failure, operators may deploy redundant components; this however may not be useful, e.g., when a cyber attack at a PLC component occurs. In this work, we address the problem of reducing vulnerability to cyber-physical attacks in water distribution networks. This is achieved by augmenting the graph which describes the information flow from sensors to actuators, by adding new connections and algorithms, to increase the number of redundant cyber components. These, in turn, increase the \textitcyber-physical security level, which is defined in the present paper as the number of malicious attacks a CPS may sustain before becoming unable to satisfy the control requirements. A proof-of-concept of the approach is demonstrated over a simple WDN, with intuition on how this can be used to increase the cyber-physical security level of the system.

In this paper, an novel active safety monitoring system is constructed for a class of nonlinear discrete-time systems. The considered nonlinear system is subjected to unknown inputs, external disturbances, and possible unknown deception attacks, simultaneously. In order to secure the safety of control systems, an active attack estimator composed of state/output estimator, attack detector and attack/attacker action estimator is constructed to monitor the system running status. The analysis and synthesis of attack estimator is performed in the H∞performance optimization manner. The off-line calculation and on-line application of active attack estimator are summarized simultaneously. The effectiveness of the proposed results is finally verified by an numerical example.

The diagnosis of performance issues in cloud environments is a challenging problem, due to the different levels of virtualization, the diversity of applications and their interactions on the same physical host. Moreover, because of privacy, security, ease of deployment and execution overhead, an agent-less method, which limits its data collection to the physical host level, is often the only acceptable solution. In this paper, a precise host-based method, to recover wait state for the processes inside a given Virtual Machine (VM), is proposed. The virtual Process State Detection (vPSD) algorithm computes the state of processes through host kernel tracing. The state of a virtual Process (vProcess) is displayed in an interactive trace viewer (Trace Compass) for further inspection. Our proposed VM trace analysis algorithm has been open-sourced for further enhancements and for the benefit of other developers. Experimental evaluations were conducted using a mix of workload types (CPU, Disk, and Network), with different applications like Hadoop, MySQL, and Apache. vPSD, being based on host hypervisor tracing, brings a lower overhead (around 0.03%) as compared to other approaches.

Data privacy and security is a leading concern for providers and customers of cloud computing, where Virtual Machines (VMs) can co-reside within the same underlying physical machine. Side channel attacks within multi-tenant virtualized cloud environments are an established problem, where attackers are able to monitor and exfiltrate data from co-resident VMs. Virtualization services have attempted to mitigate such attacks by preventing VM-to-VM interference on shared hardware by providing logical resource isolation between co-located VMs via an internal virtual network. However, such approaches are also insecure, with attackers capable of performing network channel attacks which bypass mitigation strategies using vectors such as ARP Spoofing, TCP/IP steganography, and DNS poisoning. In this paper we identify a new vulnerability within the internal cloud virtual network, showing that through a combination of TAP impersonation and mirroring, a malicious VM can successfully redirect and monitor network traffic of VMs co-located within the same physical machine. We demonstrate the feasibility of this attack in a prominent cloud platform - OpenStack - under various security requirements and system conditions, and propose countermeasures for mitigation.

To improve customer experience, datacenter operators offer support for simplifying application and resource management. For example, running workloads of workflows on behalf of customers is desirable, but requires increasingly more sophisticated autoscaling policies, that is, policies that dynamically provision resources for the customer. Although selecting and tuning autoscaling policies is a challenging task for datacenter operators, so far relatively few studies investigate the performance of autoscaling for workloads of workflows. Complementing previous knowledge, in this work we propose the first comprehensive performance study in the field. Using trace-based simulation, we compare state-of-the-art autoscaling policies across multiple application domains, workload arrival patterns (e.g., burstiness), and system utilization levels. We further investigate the interplay between autoscaling and regular allocation policies, and the complexity cost of autoscaling. Our quantitative study focuses not only on traditional performance metrics and on state-of-the-art elasticity metrics, but also on time-and memory-related autoscaling-complexity metrics. Our main results give strong and quantitative evidence about previously unreported operational behavior, for example, that autoscaling policies perform differently across application domains and allocation and provisioning policies should be co-designed.

The indoor air quality in industrial workplace buildings, e.g. air temperature, humidity and levels of carbon dioxide (CO2), play a critical role in the perceived levels of workers' comfort and in reported medical health. CO2 can act as an oxygen displacer, and in confined spaces humans can have, for example, reactions of dizziness, increased heart rate and blood pressure, headaches, and in more serious cases loss of consciousness. Specialized organizations can be brought in to monitor the work environment for limited periods. However, new low cost wireless sensor network (WSN) technologies offer potential for more continuous and autonomous assessment of industrial workplace air quality. Central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. This paper presents a case study that monitors air quality that is collected with WSN technologies. We discuss the potential BD problems. The case trials are from two workshops that are part of a large on-shore logistics base a regional shipping industry in Norway. This small case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. We also identify other potential applications of WSN technologies and visualization of BD in the workplace environments; for example, for monitoring of other substances for worker safety in high risk industries and for quality of goods in supply chain management.

Smart Grid cybersecurity is one of the key ingredients for successful and wide scale adaptation of the Smart Grid by utilities and governments around the world. The implementation of the Smart Grid relies mainly on the highly distributed sensing and communication functionalities of its components such as Wireless Sensor Networks (WSNs), Phasor Measurement Units (PMUs) and other protection devices. This distributed nature and the high number of connected devices are the main challenges for implementing cybersecurity in the smart grid. As an example, the North American Electric Reliability Corporation (NERC) issued the Critical Infrastructure Protection (CIP) standards (CIP-002 through CIP-009) to define cybersecurity requirements for critical power grid infrastructure. However, NERC CIP standards do not specify cybersecurity for different communication technologies such as WSNs, fiber networks and other network types. Implementing security mechanisms in WSNs is a challenging task due to the limited resources of the sensor devices. WSN security mechanisms should not only focus on reducing the power consumption of the sensor devices, but they should also maintain high reliability and throughput needed by Smart Grid applications. In this paper, we present a WSN cybersecurity mechanism suitable for smart grid monitoring application. Our mechanism can detect and isolate various attacks in a smart grid environment, such as denial of sleep, forge and replay attacks in an energy efficient way. Simulation results show that our mechanism can outperform existing techniques while meeting the NERC CIP requirements.

Robots operating alongside humans in field environments have the potential to greatly increase the situational awareness of their human teammates. A significant challenge, however, is the efficient conveyance of what the robot perceives to the human in order to achieve improved situational awareness. We believe augmented reality (AR), which allows a human to simultaneously perceive the real world and digital information situated virtually in the real world, has the potential to address this issue. We propose to demonstrate that augmented reality can be used to enable human-robot cooperative search, where the robot can both share search results and assist the human teammate in navigating to a search target.

The growing prevalence of cyber threats in the world are affecting every network user. Numerous security monitoring systems are being employed to protect computer networks and resources from falling victim to cyber-attacks. There is a pressing need to have an efficient security monitoring system to monitor the large network datasets generated in this process. A large network datasets representing Malware attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides to the weight of each attribute and forms a scoring system by annotating the log history. We adopted a special semi supervise method to classify cyber security log into attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally train the neural network classifier multilayer perceptron (MLP) base on the manually labelled data. By doing so, our results is very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection. The method of including Artificial Intelligence (AI) and Analyst Intuition (AI) is also known as AI2. The classification results are encouraging in segregating the types of attacks.

With the extensive application of cloud computing technology developing, security is of paramount importance in Cloud Computing. In the cloud computing environment, surveys have been provided on several intrusion detection techniques for detecting intrusions. We will summarize some literature surveys of various attack taxonomy, which might cause various threats in cloud environment. Such as attacks in virtual machines, attacks on virtual machine monitor, and attacks in tenant network. Besides, we review massive existing solutions proposed in the literature, such as misuse detection techniques, behavior analysis of network traffic, behavior analysis of programs, virtual machine introspection (VMI) techniques, etc. In addition, we have summarized some innovations in the field of cloud security, such as CloudVMI, data mining techniques, artificial intelligence, and block chain technology, etc. At the same time, our team designed and implemented the prototype system of CloudI (Cloud Introspection). CloudI has characteristics of high security, high performance, high expandability and multiple functions.

A long time ago Industrial Control Systems were in a safe place due to the use of proprietary technology and physical isolation. This situation has changed dramatically and the systems are nowadays often prone to severe attacks executed from remote locations. In many cases, intrusions remain undetected for a long time and this allows the adversary to meticulously prepare an attack and maximize its destructiveness. The ability to detect an attack in its early stages thus has a high potential to significantly reduce its impact. To this end, we propose a holistic, multi-layered, security monitoring and mitigation framework spanning the physical- and cyber domain. The comprehensiveness of the approach demands for scalability measures built-in by design. In this paper we present how scalability is addressed by an architecture that enforces geographically decentralized data reduction approaches that can be dynamically adjusted to the currently perceived context. A specific focus is put on a robust and resilient solution to orchestrate dynamic configuration updates. Experimental results based on a prototype implementation show the feasibility of the approach.

In this paper, we present a framework for graph-based representation of relation between sensors and alert types in a security alert sharing platform. Nodes in a graph represent either sensors or alert types, while edges represent various relations between them, such as common type of reported alerts or duplicated alerts. The graph is automatically updated, stored in a graph database, and visualized. The resulting graph will be used by network administrators and security analysts as a visual guide and situational awareness tool in a complex environment of security alert sharing.

Comparing with the traditional grid, energy internet will collect data widely and connect more broader. The analysis of electrical data use of Non-intrusive Load Monitoring (NILM) can infer user behavior privacy. Consideration both data security and availability is a problem must be addressed. Due to its rigid and provable privacy guarantee, Differential Privacy has proverbially reached and applied to privacy preserving data release and data mining. Because of its high sensitivity, increases the noise directly will led to data unavailable. In this paper, we propose a differentially private mechanism to protect energy internet privacy. Our focus is the aggregated data be released by data owner after added noise in disaggregated data. The theoretically proves and experiments show that our scheme can achieve the purpose of privacy-preserving and data availability.

One of the biggest problems of today's internet technologies is cyber attacks. In this paper whether DDoS attacks will be determined by deep packet inspection. Initially packets are captured by listening of network traffic. Packet filtering was achieved at desired number and type. These packets are recorded to database to be analyzed, daily values and average values are compared by known attack patterns and will be determined whether a DDoS attack attempts in real time systems.

Organizations are exposed to various cyber-attacks. When a component is exploited, the overall computed damage is impacted by the number of components the network includes. This work is focuses on estimating the Target Distribution characteristic of an attacked network. According existing security assessment models, Target Distribution is assessed by using ordinal values based on users' intuitive knowledge. This work is aimed at defining a formula which enables measuring quantitatively the attacked components' distribution. The proposed formula is based on the real-time configuration of the system. Using the proposed measure, firms can quantify damages, allocate appropriate budgets to actual real risks and build their configuration while taking in consideration the risks impacted by components' distribution. The formula is demonstrated as part of a security continuous monitoring system.

In this paper, we evaluate the usefulness of metrics that assess susceptibility to cascading blackouts. The metrics are computed using a matrix of Line Outage Distribution Factors (LODF, or DFAX matrix). The metrics are compared for several base cases with different load levels of the Western Interconnection (WI). A case corresponding to the September 8, 2011 pre-blackout state is used to compute these metrics and relate them to the origin of the cascading blackout. The correlation between the proposed metrics is determined to check redundancy. The analysis is also used to find vulnerable and critical hot spots in the power system.

Timely and adequate response on the computer security incidents depends on the accurate monitoring of the security situation. The paper investigates the task of refinement of the attack models in the form of attack graphs. It considers some challenges of attack graph generation and possible solutions, including: inaccuracies in specifying the pre- and postconditions of attack actions, processing of cycles in graphs to apply the Bayesian methods for attack graph analysis, mapping of incidents on attack graph nodes, and automatic countermeasure selection for the nodes under the risk. The software prototype that implements suggested solutions is briefly specified. The influence of the modifications on the security monitoring is shown on a case study, and the results of experiments are described.

Wireless sensor networks have achieved the substantial research interest in the present time because of their unique features such as fault tolerance, autonomous operation etc. The coverage maximization while considering the resource scarcity is a crucial problem in the wireless sensor networks. The approaches which address these problems and maximize the network lifetime are considered prominent. The node scheduling is such mechanism to address this issue. The scheduling strategy which addresses the target coverage problem based on coverage probability and trust values is proposed in Energy Efficient Coverage Protocol (EECP). In this paper the optimized decision rules is obtained by using the rough set theory to determine the number of active nodes. The results show that the proposed extension results in the lesser number of decision rules to consider in determination of node states in the network, hence it improves the network efficiency by reducing the number of packets transmitted and reducing the overhead.