Biblio

With the increasing application of Information and Communication Technologies (ICTs), cyberattacks have become more prevalent against Cyber-Physical Systems (CPSs) such as the modern power grids. Various methods have been proposed to model the cybersecurity threats, but so far limited studies have been focused on the defensive strategies subject to the limited security budget. In this paper, the power supply reliability is evaluated considering the strategic allocation of defense resources. Specifically, the optimal mixed strategies are formulated by the Stackelberg Security Game (SSG) to allocate the defense resources on multiple targets subject to cyberattacks. The cyberattacks against the intrusion-tolerant Supervisory Control and Data Acquisition (SCADA) system are mathematically modeled by Semi-Markov Process (SMP) kernel. The intrusion tolerance capability of the SCADA system provides buffered residence time before the substation failure to enhance the network robustness against cyberattacks. Case studies of the cyberattack scenarios are carried out to demonstrate the intrusion tolerance capability. Depending on the defense resource allocation scheme, the intrusion-tolerant SCADA system possesses varying degrees of self-healing capability to restore to the good state and prevent the substations from failure. If more defense resources are invested on the substations, the intrusion tolerant capability can be further enhanced for protecting the substations. Finally, the actuarial insurance principle is designed to estimate transmission companies' individual premiums considering correlated cybersecurity risks. The proposed insurance premium principle is designed to provide incentive for investments on enhancing the intrusion tolerance capability, which is verified by the results of case studies.

With the continuous popularization of smart terminals, Android and IOS systems are the most mainstream mobile operating systems in the market, and their application types and application numbers are constantly increasing. As an open system, the security issues of Android application emerge in endlessly, such as the reverse decompilation of installation package, malicious code injection, application piracy, interface hijacking, SMS hijacking and input monitoring. These security issues will also appear on mobile applications in the power industry, which will not only result in the embezzlement of applied knowledge copyrights but also lead to serious leakage of users' information and even economic losses. It may even result in the remote malicious control of key facilities, which will cause serious social issues. Under the background of the development of smart grid information construction, also with the application and promotion of power services in mobile terminals, information security protection for mobile terminal applications and interactions with the internal system of the power grid has also become an important research direction. While analyzing the risks faced by mobile applications, this article also enumerates and analyzes the necessary measures for risk resolution.

Cloud computing has become the main backend of the IT infrastructure as it provides ubiquitous and on-demand computing to serve to a wide range of users including end-users and high-performance demanding agencies. The users can allocate and free resources allocated for their Virtual Machines (VMs) as needed. However, with the rapid growth of interest in cloud computing systems, several issues have arisen especially in the domain of cybersecurity. It is a known fact that not only the malicious users can freely allocate VMs, but also they can infect victims' VMs to run their own tools that include cryptocurrency mining, ransomware, or cyberattacks against others. Even though there exist intrusion detection systems (IDS), running an IDS on every VM can be a costly process and it would require fine configuration that only a small subset of the cloud users are knowledgeable about. Therefore, to overcome this challenge, in this paper we present a VM introspection based allowlisting method to be deployed and managed directly by the cloud providers to check if there are any malicious software running on the VMs with minimum user intervention. Our middleware monitors the processes and if it detects unknown events, it will notify the users and/or can take action as needed.

The objective of the proposed research is to develop an IOT based security system with a two-point authentication. Human face recognition and fingerprint is a known method for access authentication. A combination of both technologies and integration of the system with IoT make will make the security system more efficient and reliable. Use of online platform google firebase is made for saving database and retrieving it in real-time. In this system access to the fingerprint (touch sensor) from mobile is proposed using an android app developed in android studio and authentication for the same is also proposed. On identification of both face and fingerprint together, access to door or locker is provided.

End-to-end encryption is now ubiquitous on the internet. By securing network communications with TLS, parties can insure that in-transit data remains inaccessible to collection and analysis. In the IoT domain however, end-to-end encryption can paradoxically decrease user privacy, as many IoT devices establish encrypted communications with the manufacturer's cloud backend. The content of these communications remains opaque to the user and in several occasions IoT devices have been discovered to exfiltrate private information (e.g., voice recordings) without user authorization. In this paper, we propose Inspection-Friendly TLS (IF-TLS), an IoT-oriented, TLS-based middleware protocol that preserves the encryption offered by TLS while allowing traffic analysis by middleboxes under the user's control. Differently from related efforts, IF-TLS is designed from the ground up for the IoT world, adding limited complexity on top of TLS and being fully controllable by the residential gateway. At the same time it provides flexibility, enabling the user to offload traffic analysis to either the gateway itself, or cloud-based middleboxes. We implemented a stable, Python-based prototype IF-TLS library; preliminary results show that performance overhead is limited and unlikely to affect quality-of-experience.

Throughout the Department of Defense, there are ongoing efforts to increase cybersecurity and improve data transfer in unmanned robotic systems (UxS). This paper explores the performance of the Robot Operating System (ROS) 2, which is built with the Data Distribution Service (DDS) standard as a middleware. Based on how quality of service (QoS) parameters are defined in the robotic middleware interface, it is possible to implement strict delivery requirements to different nodes on a dynamic nodal network with multiple unmanned systems connected. Through this research, different scenarios with varying QoS settings were implemented and compared to baseline values to help illustrate the impact of latency and throughput on data flow. DDS security settings were also enabled to help understand the cost of overhead and performance when secured data is compared to plaintext baseline values. Our experiments were performed using a basic ROS 2 network consisting of two nodes (one publisher and one subscriber). Our experiments showed a measurable latency and throughput change between different QoS profiles and security settings. We analyze the trends and tradeoffs associated with varying QoS and security settings. This paper provides performance data points that can be used to help future researchers and developers make informative choices when using ROS 2 for UxS.

In order to significantly improve the reliable interaction and fast processing when TT&C(Tracking, Telemetry and Command) Resource Scheduling and Management System (TRSMS) communicate with external systems which are diverse, multiple directional and high concurrent, this paper designs and implements a highly concurrent and secure middleware for TT&C Resource Automatic Scheduling Interface (TRASI). The middleware designs memory pool, data pool, thread pool and task pool to improve the efficiency of concurrent processing, uses the rule dictionary, communication handshake and wait retransmission mechanism to ensure the data interaction security and reliability. This middleware can effectively meet the requirements of TRASI for data exchange with external users and system, significantly improve the data processing speed and efficiency, and promote the information technology and automation level of Aerospace TT&C Network Management Center (TNMC).

Blackhole (BH) attacks are one of the most serious threats in mobile ad-hoc networks. A BH is a security attack in which a malicious node absorbs data packets and sends fake routing information to neighboring nodes. BH attacks are widely studied. However, existing defense methods wrongfully assume that BH attacks cannot overcome the most common defense approaches. A new wave of BH attacks is known as smart BH attacks. In this study, we used a highly aggressive type of BH attack that can predict sequence numbers to overcome traditional detection methods that set a threshold to sequence numbers. To protect the network from this type of BH attack, we propose a detection-and-prevention method that uses local information shared with neighboring nodes. Our experiments show that the proposed method successfully detects and contains even smart BH threats. Consequently, the attack success rate decreases.

Mobile Ad-hoc Network (MANET) is a dynamic network between mobile nodes for sharing of information and is popular for its infrastructure-less design. Due to the lack of central governing body, however, various security threats come forward in MANETs in comparison to its infrastructure based counterparts. Blackhole attack is one of the most challenging security issues present in MANETs. Blackhole attack reduces network efficiency considerably by disrupting the flow of data between source and destination. In this paper, we propose an algorithm which is based on the technique of changing the sequence number present in control packets, in particular the Route Reply Packets (RREP) in widely used Ad-Hoc On Demand Distance Vector (AODV) routing protocol, in order to identify the blackhole nodes and thereby to minimize the data loss by discarding the route with such Blackhole nodes. Simulation results show that the proposed algorithm outperforms the legacy Intrusion Detection System (IDS) provisioned for AODV.

The Internet of Things (IoT) is more and more present in fundamental aspects of our societies and personal life. Billions of objects now have access to the Internet. This networking capability allows for new beneficial services and applications. However, it is also the entry-point for a wide variety of cyber-attacks that target these devices. The security measures present in real IoT systems lag behind those of the standard Internet. Security is sometimes completely absent. Moving Target Defense (MTD) is a 10-year-old cyber-defense paradigm. It proposes to randomize components of a system. Reasonably, an attacker will have a higher cost attacking an MTD-version of a system compared with a static-version of it. Even if MTD has been successfully applied to standard systems, its deployment for IoT is still lacking. In this paper, we propose a generic MTD framework suitable for IoT systems: IANVS (pronounced Janus). Our framework has a modular design. Its components can be adapted according to the specific constraints and requirements of a particular IoT system. We use it to instantiate two concrete MTD strategies. One that targets the UDP port numbers (port-hopping), and another a CoAP resource URI. We implement our proposal on real hardware using Pycom LoPy4 nodes. We expose the nodes to a remote Denial-of-Service attack and evaluate the effectiveness of the IANVS-based port-hopping MTD proposal.

The dynamic and adaptable characteristics of mobile ad hoc networks have made it a significant field for deploying various applications in wireless sensor networks. Increasing popularity of the portable devices is the main reason for the development of mobile ad hoc networks. Furthermore, the network does not require a fixed architecture and it is easy to deploy. This type of network is highly vulnerable to cyber-attacks as the nodes communicate with each other through a Wireless medium. The most critical attack in ad hoc network is jellyfish attack. In this research we have proposed a Direct Trust-based Detection Algorithm to detect and prevent jellyfish attack in MANET.

With the development of positioning technology and the popularity of mobile devices, location-based services have been widely deployed. To use the services, users must provide the server accurate location information, during which the attacker tends to infer sensitive information from intercepting queries. In this paper, we model the road network as an edge cluster graph with its location semantics considered. Then, we propose the Circle First Structure Optimization (CFSO) algorithm which generates an anonymous set by adding optimal adjacent locations. Furthermore, we introduce controllable randomness and propose the Attack-Resilient (AR) algorithm to enhance the anti-attack ability. Meanwhile, to reduce the system overhead, our algorithms build the anonymous set quickly and take the structure of the anonymous set into account. Finally, we conduct experiments on a real map and the results demonstrate a higher anonymity success rate and a stronger anti-attack capability with less system overhead.

The applications for wide area monitoring, protection, and control systems (WAMPC) at the control center, help with providing resilient, efficient, and secure operation of the transmission system of the smart grid. The increased proliferation of phasor measurement units (PMUs) in this space has inspired many prudent applications to assist in the process of decision making in the control centers. Machine learning (ML) based decision support systems have become viable with the availability of abundant high-resolution wide area operational PMU data. We propose a deep neural network (DNN) based supervisory protection and event diagnosis system and demonstrate that it works with very high degree of confidence. The system introduces a supervisory layer that processes the data streams collected from PMUs and detects disturbances in the power systems that may have gone unnoticed by the local monitoring and protection system. Then, we investigate compromise of the insights of this ML based supervisory control by crafting adversaries that corrupt the PMU data via minimal coordinated manipulation and identification of the spatio-temporal regions in the multidimensional PMU data in a way that the DNN classifier makes wrong event predictions.

Underwater Acoustic Sensor Networks (UASNs) are often deployed in hostile environments, and they face many security threats. Moreover, due to the harsh characteristics of the underwater environment, UASNs are vulnerable to malicious attacks. One of the most dangerous security threats is the eavesdropping attack, where an adversary silently collects the information exchanged between the sensor nodes. Although careful assignment of transmission power levels and optimization of data flow paths help alleviate the extent of eavesdropping attacks, the network lifetime can be negatively affected since routing could be established using sub-optimal paths in terms of energy efficiency. In this work, two optimization models are proposed where the first model minimizes the potential eavesdropping risks in the network while the second model maximizes the network lifetime under a certain level of an eavesdropping risk. The results show that network lifetimes obtained when the eavesdropping risks are minimized significantly shorter than the network lifetimes obtained without considering any eavesdropping risks. Furthermore, as the countermeasures against the eavesdropping risks are relaxed, UASN lifetime is shown to be prolonged, significantly.

To secure a guest operating system running on a virtual machine (VM), a monitoring method using hardware breakpoints by a virtual machine monitor is required. However, debug registers are visible to guest operating systems; thus, malicious programs on a guest operating system can detect or disable the monitoring method. This paper presents a method to hide access to debug registers from programs running on a VM. Our proposed method detects programs' access to debug registers and disguises the access as having succeeded. The register's actual value is not visible or modifiable to programs, so the monitoring method is hidden. This paper presents the basic design and evaluation results of our method.

Cloud-based infrastructures have grown in popularity over the last decade leveraging virtualisation, server, storage, compute power and network components to develop flexible applications. The requirements for instantaneous deployment and reduced costs have led the shift from virtual machine deployment to containerisation, increasing the overall flexibility of applications and increasing performances. However, containers require a fully fleshed operating system to execute, increasing the attack surface of an application. Unikernels, on the other hand, provide a lightweight memory footprint, ease of application packaging and reduced start-up times. Moreover, Unikernels reduce the attack surface due to the self-contained environment only enabling low-level features. In this work, we provide an exhaustive description of the unikernel ecosystem; we demonstrate unikernel vulnerabilities and further discuss the security implications of Unikernel-enabled environments through different use-cases.

We focus on policy-aware data centers (PADCs), wherein virtual machine (VM) traffic traverses a sequence of middleboxes (MBs) for security and performance purposes, and propose two new VM placement and migration problems. We first study PAL: policy-aware virtual machine placement. Given a PADC with a data center policy that communicating VM pairs must satisfy, the goal of PAL is to place the VMs into the PADC to minimize their total communication cost. Due to dynamic traffic loads in PADCs, however, above VM placement may no longer be optimal after some time. We thus study PAM: policy-aware virtual machine migration. Given an existing VM placement in the PADC and dynamic traffic rates among communicating VMs, PAM migrates VMs in order to minimize the total cost of migration and communication of the VM pairs. We design optimal, approximation, and heuristic policyaware VM placement and migration algorithms. Our experiments show that i) VM migration is an effective technique, reducing total communication cost of VM pairs by 25%, ii) our PAL algorithms outperform state-of-the-art VM placement algorithm that is oblivious to data center policies by 40-50%, and iii) our PAM algorithms outperform the only existing policy-aware VM migration scheme by 30%.

{With the widespread use of cloud computing, energy consumption of cloud data centers is increasing which mainly comes from IT equipment and cooling equipment. This paper argues that once the number of virtual machines on the physical machines reaches a certain level, resource competition occurs, resulting in a performance loss of the virtual machines. Unlike most papers, we do not impose placement constraints on virtual machines by giving a CPU cap to achieve the purpose of energy savings in cloud data centers. Instead, we use the measure of performance loss to weigh. We propose a reinforcement learning-based virtual machine placement strategy(RLVMP) for energy savings in cloud data centers. The strategy considers the weight of virtual machine performance loss and energy consumption, which is finally solved with the greedy strategy. Simulation experiments show that our strategy has a certain improvement in energy savings compared with the other algorithms.

Cloud computing dominates the information communication and technology landscape despite the presence of lingering security issues such as the interdependency problem. The latter is a co-residence conundrum where the attacker successfully compromises his target virtual machine by first exploiting the weakest (in terms of security) virtual machine that is hosted in the same server. To tackle this issue, we propose a novel virtual machine allocation policy that is based on the attacker's efficiency and coverage. By default, our allocation policy considers all legitimate users as attackers and then proceeds to host the users' virtual machines to the server where their efficiency and/or coverage are the smallest. Our simulation results show that our proposal performs better than the existing allocation policies that were proposed to tackle the same issue, by reducing the attacker's possibilities to zero and by using between 30 - 48% less hosts.

Internet of Medical Things (IoMT) are getting popular in the smart healthcare domain. These devices are resource-constrained and are vulnerable to attack. As the IoMTs are connected to the healthcare network infrastructure, it becomes the primary target of the adversary due to weak security and privacy measures. In this regard, this paper proposes a security architecture for smart healthcare network infrastructures. The architecture uses various security components or services that are developed and deployed as virtual network functions. This makes the security architecture ready for future network frameworks such as OpenMANO. Besides, in this security architecture, only authenticated and trusted IoMTs serve the patients along with an encryption-based communication protocol, thus creating a secure, privacy-preserving and trusted healthcare network infrastructure.

ATM transactions are verified using two-factor authentication. The PIN is one of the factors (something you know) and the ATM Card is the other factor (something you have). Therefore, banks make significant investments on PIN Mailers and HSMs to preserve the security and confidentiality in the generation, validation, management and the delivery of the PIN to their customers. Moreover, banks install surveillance cameras inside ATM cubicles as a physical security measure to prevent fraud and theft. However, in some cases, ATM PIN-Pad and the PIN entering process get revealed through the surveillance camera footage itself. We demonstrate that visibility of forearm movements is sufficient to infer PINs with a significant level of accuracy. Video footage of the PIN entry process simulated in an experimental setup was analyzed using two approaches. The human observer-based approach shows that a PIN can be guessed with a 30% of accuracy within 3 attempts whilst the computer-assisted analysis of footage gave an accuracy of 50%. The results confirm that ad-hoc installation of surveillance cameras can weaken ATM PIN security significantly by potentially exposing one factor of a two-factor authentication system. Our investigation also revealed that there are no guidelines, standards or regulations governing the placement of surveillance cameras inside ATM cubicles in Sri Lanka.

In the Ubiquitous Electric Internet of Things (UEIoT), the terminals are very easy to be accessed and attacked by attackers due to the lack of effective monitoring and safe isolation methods. Therefore, in the implementation of UEIoT, the security protection of terminals is particularly important. Therefore, this paper proposes a dual-system design scheme for terminal active immunity based on trusted computing. In this scheme, the terminal node in UEIoT is composed of two parts: computing part and trusted protection part. The computing component and the trusted protection component are logically independent of each other, forming a trusted computing active immune dual-system structure with both computing and protection functions. The Trusted Network Connection extends the trusted state of the terminal to the network, thus providing a solution for terminal secure access in the UEIoT.

The proliferation of mobile augmented reality applications and the toolkits to create them have serious implications for user privacy. In this paper, we explore how malicious AR app developers can leverage capabilities offered by commercially available AR libraries, and describe how edge computing can be used to address this privacy problem.

Ultra-Reliable Low Latency Communications (URLLC) has been always a vital component of many industrial applications. The paper proposes a new wireless URLLC solution called RETIS, which is suitable for factory automation and fast process control applications, where low latency, low jitter, and high data exchange rates are mandatory. In the paper, we describe the communication protocol as well as the hardware structure of the network nodes for implementing the required functionality. Many techniques enabling fast, reliable wireless transmissions are used - short Transmission Time Interval (TTI), TimeDivision Multiple Access (TDMA), MIMO, optional duplicated data transfer, Forward Error Correction (FEC), ACK mechanism. Preliminary tests show that reliable endto-end latency down to 350 μs and packet exchange rate up to 4 kHz can be reached (using quadruple MIMO and standard IEEE 802.15.4 PHY at 250 kbit/s).

A QPSK-assisted MIMO equalization is investigated to compensate bandwidth limitation for 800-Gb/s/λ DP-256QAM systems with only 25G-class optics. Compared with conventional MIMO equalization, the proposed equalization scheme exhibits 1.8-dB OSNR improvement at 15% FEC limit.