Biblio

In this paper we propose an architecture for fully-reconfigurable, plug-and-play wireless sensor network testbed. The proposed architecture is able to reconfigure and support easy experimentation and testing of standard protocol stacks (i.e. uIPv4 and uIPv6) as well as non-standardized clean-slate protocol stacks (e.g. configured using RIME). The parameters of the protocol stacks can be remotely reconfigured through an easy to use RESTful API. Additionally, we are able to fully reconfigure clean-slate protocol stacks at run-time. The architecture enables easy set-up of the network - plug - by using a protocol that automatically sets up a multi-hop network (i.e. RPL protocol) and it enables reconfiguration and experimentation - play - by using a simple, RESTful interaction with each node individually. The reference implementation of the architecture uses a dual-stack Contiki OS with the ProtoStack tool for dynamic composition of services.

Distributed attacks originating from botnet-infected machines (bots) such as large-scale malware propagation campaigns orchestrated via spam emails can quickly affect other network infrastructures. As these attacks are made successful only by the fact that hundreds of infected machines engage in them collectively, their damage can be avoided if machines infected with a common botnet can be detected early rather than after an attack is launched. Prior studies have suggested that outgoing bot attacks are often preceded by other ``tell-tale'' malicious behaviour, such as communication with botnet controllers (C&C servers) that command botnets to carry out attacks. We postulate that observing similar behaviour occuring in a synchronised manner across multiple machines is an early indicator of a widespread infection of a single botnet, leading potentially to a large-scale, distributed attack. Intuitively, if we can detect such synchronised behaviour early enough on a few machines in the network, we can quickly contain the threat before an attack does any serious damage. In this work we present a measurement-driven analysis to validate this intuition. We empirically analyse the various stages of malicious behaviour that are observed in real botnet traffic, and carry out the first systematic study of the network behaviour that typically precedes outgoing bot attacks and is synchronised across multiple infected machines. We then implement as a proof-of-concept a set of analysers that monitor synchronisation in botnet communication to generate early infection and attack alerts. We show that with this approach, we can quickly detect nearly 80% of real-world spamming and port scanning attacks, and even demonstrate a novel capability of preventing these attacks altogether by predicting them before they are launched.

We investigate the coverage efficiency of a sensor network consisting of sensors with circular sensing footprints of different radii. The objective is to completely cover a region in an efficient manner through a controlled (or deterministic) deployment of such sensors. In particular, it is shown that when sensing nodes of two different radii are used for complete coverage, the coverage density is increased, and the sensing cost is significantly reduced as compared to the homogeneous case, in which all nodes have the same sensing radius. Configurations of heterogeneous disks of multiple radii to achieve efficient circle coverings are presented and analyzed.

Typical techniques for tracking vital signs require body contact and most of these techniques are intrusive in nature. Body-contact methods might irritate the patient's skin and he/she might feel uncomfortable while sensors are touching his/her body. In this study, we present a new wireless (non-contact) method for monitoring human vital signs (breathing and heartbeat). We have demonstrated for the first time1 that vitals signs can be measured wirelessly through visible light signal reflected from a human subject, also referred to as visible light sensing (VLS). In this method, the breathing and heartbeat rates are measured without any body-contact device, using only a simple photodetector and a light source (e.g., LED). The light signal reflected from human subject is modulated by the physical motions during breathing and heartbeats. Signal processing tools such as filtering and Fourier transform are used to convert these small variations in the received light signal power to vitals data.We implemented the VLS-based non-contact vital signs monitoring system by using an off-the-shelf light source, a photodetector and a signal acquisition and processing unit. We observed more than 94% of accuracy as compared to a contact-based FDA (The Food and Drug Administration) approved devices. Additional evaluations are planned to assess the performance of the developed vitals monitoring system, e.g., different subjects, environments, etc. Non-contact vitals monitoring system can be used in various areas and scenarios such as medical facilities, residential homes, security and human-computer-interaction (HCI) applications.

The security content automation includes configurations of large number of systems, installation of patches securely, verification of security-related configuration settings, compliance with security policies and regulatory requirements, and ability to respond quickly when new threats are discovered [1]. Although humans are important in information security management, humans sometimes introduce errors and inconsistencies in an organization due to manual nature of their tasks [2]. Security Content Automation Protocol was developed by the U.S. NIST to automate information security management tasks such as vulnerability and patch management, and to achieve continuous monitoring of security configurations in an organization. In this paper, SCAP is employed to develop an automated security configuration checklist for use in verifying Apple iOS device configuration against the defined security baseline to enforce policy compliance in an enterprise.

Wireless Sensor Networks (WSNs) consist of low-cost, resource-constrained sensor nodes and a designated node called a sink which collects data from the sensor nodes. A WSN can be used in numerous applications such as subject tracking and monitoring, where it is often desirable to keep the location of the subject private. Without location privacy protection, an adversary can locate the subject. In this paper, we propose an algorithm that tries to keep the subject location private from a global adversary, which can see the entire network traffic, in an energy efficient way.

Continued advances in IoT technology have prompted new investigation into its usage for military operations, both to augment and complement existing military sensing assets and support next-generation artificial intelligence and machine learning systems. Under the emerging Internet of Battlefield Things (IoBT) paradigm, current operational conditions necessitate the development of novel security techniques, centered on establishment of trust for individual assets and supporting resilience of broader systems. To advance current IoBT efforts, a collection of prior-developed cybersecurity techniques is reviewed for applicability to conditions presented by IoBT operational environments (e.g., diverse asset ownership, degraded networking infrastructure, adversary activities) through use of supporting case study examples. The research techniques covered focus on two themes: (1) Supporting trust assessment for known/unknown IoT assets; (2) ensuring continued trust of known IoT assets and IoBT systems.

The systematic integration of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) into the supply chain to increase operational efficiency and quality has also introduced new complexities to the threat landscape. The myriad of sensors could increase data collection capabilities for businesses to facilitate process automation aided by Artificial Intelligence (AI) but without adopting an appropriate Security-by-Design framework, threat detection and response are destined to fail. The emerging concept of Smart Workplace incorporates many CPS (e.g. Robots and Drones) to execute tasks alongside Employees both of which can be exploited as Insider Threats. We introduce and discuss forensic-readiness, liability attribution and the ability to track moving Smart SPS Objects to support modern Digital Forensics and Incident Response (DFIR) within a defence-in-depth strategy. We present a framework to facilitate the tracking of object behaviour within Smart Controlled Business Environments (SCBE) to support resilience by enabling proactive insider threat detection. Several components of the framework were piloted in a company to discuss a real-life case study and demonstrate anomaly detection and the emerging of behavioural patterns according to objects' movement with relation to their job role, workspace position and nearest entry or exit. The empirical data was collected from a Bluetooth-based Proximity Monitoring Solution. Furthermore, a key strength of the framework is a federated Blockchain (BC) model to achieve forensic-readiness by establishing a digital Chain-of-Custody (CoC) and a collaborative environment for CPS to qualify as Digital Witnesses (DW) to support post-incident investigations.

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.

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.

Control theory and SDN (Software Defined Networking) are key components for NFV (Network Function Virtualization) deployment. However little has been done to use a control-theoretic approach for SDN and NFV management. In this demo, we describe a use case for NFV management using control theory and SDN. We use the management architecture of RINA (a clean-slate Recursive InterNetwork Architecture) to manage Virtual Network Function (VNF) instances over the GENI testbed. We deploy Snort, an Intrusion Detection System (IDS) as the VNF. Our network topology has source and destination hosts, multiple IDSes, an Open vSwitch (OVS) and an OpenFlow controller. A distributed management application running on RINA measures the state of the VNF instances and communicates this information to a Proportional Integral (PI) controller, which then provides load balancing information to the OpenFlow controller. The latter controller in turn updates traffic flow forwarding rules on the OVS switch, thus balancing load across the VNF instances. This demo demonstrates the benefits of using such a control-theoretic load balancing approach and the RINA management architecture in virtualized environments for NFV management. It also illustrates that the GENI testbed can easily support a wide range of SDN and NFV related experiments.

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

In the recent years, Wireless Sensor Networks (WSN) and its applications have obtained considerable momentum. However, security and power limits of these networks are still important matters as security and power limits remain an important problem in WSN. This paper contributes to provide a simulation-based analysis of the energy efficiency, accuracy and path length of static and dynamic wireless sensor networks for 5G environment. Results are analyzed and discussed to show the difference between these two types of sensor networks. The static networks more accurate than dynamic networks. Data move from source to destination in shortest path in dynamic networks compared to static ones.

In the recent years, Wireless Sensor Networks (WSN) and its applications have obtained considerable momentum. However, security and power limits of these networks are still important matters as security and power limits remain an important problem in WSN. This paper contributes to provide a simulation-based analysis of the energy efficiency, accuracy and path length of static and dynamic wireless sensor networks for 5G environment. Results are analyzed and discussed to show the difference between these two types of sensor networks. The static networks more accurate than dynamic networks. Data move from source to destination in shortest path in dynamic networks compared to static ones.

Medical Internet of Things (MIoT) offers innovative solutions to a healthier life, making radical changes in people's lives. Healthcare providers are enabled to continuously and remotely monitor their patients for many medial issues outside hospitals and healthcare providers' offices. MIoT systems and applications lead to increase availability, accessibility, quality and cost-effectiveness of healthcare services. On the other hand, MIoT devices generate a large amount of diverse real-time data, which is highly sensitive. Thus, securing medical data is an essential requirement when developing MIoT architectures. However, the MIoT architectures being developed in the literature have many security issues. To address the challenge of data security in MIoT, the integration of fog computing and MIoT is studied as an emerging and appropriate solution. By data security, it means that medial data is stored in fog nodes and transferred to the cloud in a secure manner to prevent any unauthorized access. In this paper, we propose a design for a secure fog-cloud based architecture for MIoT.

The growing trend toward information technology increases the amount of data travelling over the network links. The problem of detecting anomalies in data streams has increased with the growth of internet connectivity. Software-Defined Networking (SDN) is a new concept of computer networking that can adapt and support these growing trends. However, the centralized nature of the SDN design is challenged by the need for an efficient method for traffic monitoring against traffic anomalies caused by misconfigured devices or ongoing attacks. In this paper, we propose a new model for traffic behavior monitoring that aims to ensure trusted communication links between the network devices. The main objective of this model is to confirm that the behavior of the traffic streams matches the instructions provided by the SDN controller, which can help to increase the trust between the SDN controller and its covered infrastructure components. According to our preliminary implementation, the behavior monitoring unit is able to read all traffic information and perform a validation process that reports any mismatching traffic to the controller.

Due to the increase in design complexity and cost of VLSI chips, a number of design houses outsource manufacturing and import designs in a way to reduce the cost. This results in a decrease of the authenticity and security of the manufactured product. Since product development involves outside sources, circuit designers can not guarantee that their hardware has not been altered. It is often possible that attackers include additional hardware in order to gain privileges over the original circuit or cause damage to the product. These added circuits are called ``Hardware Trojans''. In this paper, we investigate introducing necessary modules needed for detection of hardware Trojans. We also introduce necessary programmable logic fabric that can be used in the implementation of the hardware assertion checkers. Our target is to utilize the provided programable fabric in a System on Chip (SoC) and optimize the hardware assertion to cover the detection of most hardware trojans in each core of the target SoC.

Border areas are generally considered as places where great deal of violence, intrusion and cohesion between several parties happens. This often led to danger for the life of employees, soldiers and common man working or living in border areas. Further geographical conditions like mountains, snow, forest, deserts, harsh weather and water bodies often lead to difficult access and monitoring of border areas. Proposed system uses thermal imaging camera (FLIR) for detection of various objects and infiltrators. FLIR is assigned an IP address and connected through local network to the control center. Software code captures video and subsequently the intrusion detection. A motor controlled spotlight with infrared and laser gun is used to illuminate under various conditions at the site. System also integrates sound sensor to detect specific sounds and motion sensors to sense suspicious movements. Based on the decision, a buzzer and electric current through fence for further protection can be initiated. Sensors are be integrated through IoT for an efficient control of large border area and connectivity between sites.

A Mobile ad hoc Network (MANET) is a self-configure, dynamic, and non-fixed infrastructure that consists of many nodes. These nodes communicate with each other without an administrative point. However, due to its nature MANET becomes prone to many attacks such as DoS attacks. DoS attack is a severe as it prevents legitimate users from accessing to their authorised services. Monitoring, Detection, and rehabilitation (MrDR) method is proposed to detect DoS attacks. MrDR method is based on calculating different trust values as nodes can be trusted or not. In this paper, we evaluate the MrDR method which detect DoS attacks in MANET and compare it with existing method Trust Enhanced Anonymous on-demand routing Protocol (TEAP) which is also based on trust concept. We consider two factors to compare the performance of the proposed method to TEAP method: packet delivery ratio and network overhead. The results confirm that the MrDR method performs better in network performance compared to TEAP method.

Nowadays with the huge technological development, the reliance on technology has become enormous. Wireless Sensor Networks (WSN) is an example of using the Internet and communication between the patient and the hospital. Easy use of such networks helps to increase the quality of communication between patient and hospital. With the development of technology increased risk in use. Any change in this data between the patient and the hospital may cause false data that may harm the patient. In this paper, a secure protocol is designed to ensure the confidentiality, integrity, and availability of data transfer between the hospital and the patient, depending on the AES and RC4 algorithms.

Modern infrastructure is heavily reliant on systems with interconnected computational and physical resources, named Cyber-Physical Systems (CPSs). Hence, building resilient CPSs is a prime need and continuous monitoring of the CPS operational health is essential for improving resilience. This paper presents a framework for calculating and monitoring of health in CPSs using data driven techniques. The main advantages of this data driven methodology is that the ability of leveraging heterogeneous data streams that are available from the CPSs and the ability of performing the monitoring with minimal a priori domain knowledge. The main objective of the framework is to warn the operators of any degradation in cyber, physical or overall health of the CPS. The framework consists of four components: 1) Data acquisition and feature extraction, 2) state identification and real time state estimation, 3) cyber-physical health calculation and 4) operator warning generation. Further, this paper presents an initial implementation of the first three phases of the framework on a CPS testbed involving a Microgrid simulation and a cyber-network which connects the grid with its controller. The feature extraction method and the use of unsupervised learning algorithms are discussed. Experimental results are presented for the first two phases and the results showed that the data reflected different operating states and visualization techniques can be used to extract the relationships in data features.

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.

The goal of this letter is to explore the extent to which the vulnerabilities plaguing the Internet, particularly susceptibility to distributed denial-of-service (DDoS) attacks, impact the Cloud. DDoS has been known to disrupt Cloud services, but could it do worse by permanently damaging server and switch hardware? Services are hosted in data centers with thousands of servers generating large amounts of heat. Heating, ventilation, and air-conditioning (HVAC) systems prevent server downtime due to overheating. These are remotely managed using network management protocols that are susceptible to network attacks. Recently, Cloud providers have experienced outages due to HVAC malfunctions. Our contributions include a network simulation to study the feasibility of such an attack motivated by our experiences of such a security incident in a real data center. It demonstrates how a network simulator can study the interplay of the communication and thermal properties of a network and help prevent the Cloud provider's worst nightmare: meltdown of the data center as a result of a DDoS attack.

Infrastructure-based Vehicular Networks can be applied in different social contexts, such as health care, transportation and entertainment. They can easily take advantage of the benefices provided by wireless mesh networks (WMNs) to mobility, since WMNs essentially support technological convergence and resilience, required for the effective operation of services and applications. However, infrastructure-based vehicular networks are prone to attacks such as ARP packets flooding that compromise mobility management and users' network access. Hence, this work proposes MIRF, a secure mobility scheme based on reputation and filtering to mitigate flooding attacks on mobility management. The efficiency of the MIRF scheme has been evaluated by simulations considering urban scenarios with and without attacks. Analyses show that it significantly improves the packet delivery ratio in scenarios with attacks, mitigating their intentional negative effects, as the reduction of malicious ARP requests. Furthermore, improvements have been observed in the number of handoffs on scenarios under attacks, being faster than scenarios without the scheme.

Monitoring large areas using sensors is fundamental in a number of applications, including electric power grid, traffic networks, and sensor-based pollution control systems. However, the number of sensors that can be deployed is often limited by financial or technological constraints. This problem is further complicated by the presence of strategic adversaries, who may disable some of the deployed sensors in order to impair the operator's ability to make predictions. Assuming that the operator employs a Gaussian-process-based regression model, we formulate the problem of attack-resilient sensor placement as the problem of selecting a subset from a set of possible observations, with the goal of minimizing the uncertainty of predictions. We show that both finding an optimal resilient subset and finding an optimal attack against a given subset are NP-hard problems. Since both the design and the attack problems are computationally complex, we propose efficient heuristic algorithms for solving them and present theoretical approximability results. Finally, we show that the proposed algorithms perform exceptionally well in practice using numerical results based on real-world datasets.