Biblio

The “Internet of Things” (IoT) is internetworking of physical devices known as 'things', algorithms, equipment and techniques that allow communication with another device, equipment and software over the network. And with the advancement in data communication, every device must be connected via the Internet. For this purpose, we use resource-constrained sensor nodes for collecting data from homes, offices, hospitals, industries and data centers. But various vulnerabilities may ruin the functioning of the sensor nodes. Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized, secure routing protocol designed for the 6LoWPAN IoT network. It's a proactive routing protocol that works on the destination-oriented topology to perform safe routing. The Sinkhole is a networking attack that destroys the topology of the RPL protocol as the attacker node changes the route of all the traffic in the IoT network. In this paper, we have given a survey of Sinkhole attacks in IoT and proposed different methods for preventing and detecting these attacks in a low-power-based IoT network.

Due to the widespread use of the Internet of Things (IoT) in recent years, the need for IoT technologies to handle communications with the rest of the globe has grown dramatically. Wireless sensor networks (WSNs) play a vital role in the operation of the IoT. The creation of Internet of Things operating systems (OS), which can handle the newly constructed IoT hardware, as well as new protocols and procedures for all communication levels, all of which are now in development, will pave the way for the future. When compared to other devices, these gadgets require a comparatively little amount of electricity, memory, and other resources. This has caused the scientific community to become more aware of the relevance of IoT device operating systems as a result of their findings. These devices may be made more versatile and powerful by including an operating system that contains real-time capabilities, kernel, networking, and other features, among other things. IEEE 802.15.4 networks are linked together using IPv6, which has a wide address space and so enables more devices to connect to the internet using the 6LoWPAN protocol. It is necessary to address some privacy and security issues that have arisen as a result of the widespread use of the Internet, notwithstanding the great benefits that have resulted. For the Internet of Things operating systems, this research has provided a network security architecture that ensures secure communication by utilizing the Cooja network simulator in combination with the Contiki operating system and demonstrate and explained how the nodes can protect from the network layer and physical layer attacks. Also, this research has depicted the energy consumption results of each designated node type during the authentication and communication process. Finally, proposed a few further improvements for the architecture which will enhance the network layer protection.

The “Internet of Things (IoT)” is a term that describes physical sensors, processing software, power and other technologies to connect or interchange information between systems and devices through the Internet and other forms of communication. RPL protocol can efficiently establish network routes, communicate routing information, and adjust the topology. The 6LoWPAN concept was born out of the belief that IP should protect even the tiniest devices, and for low-power devices, minimal computational capabilities should be permitted to join IoT. The DIS-Flooding against RPL-based IoT with its mitigation techniques are discussed in this paper.

As a large number of sensor nodes as well as limited resources such as energy, memory, computing power, as well as bandwidth. Lossy linkages connect these nodes together. In early 2008,IETF working group looked into using current routing protocols for LLNs. Routing Over minimum power and Lossy networksROLL standardizes an IPv6 routing solution for LLNs because of the importance of LLNs in IoT.IPv6 Routing Protocol is based on the 6LoWPAN standard. RPL has matured significantly. The research community is becoming increasingly interested in it. The topology of RPL can be built in a variety of ways. It creates a topology in advance. Due to the lack of a complete review of RPL, in this paper a mobility management framework has been proposed along with experimental evaluation by applying parameters likePacket Delivery Ratio, throughput, end to end delay, consumed energy on the basis of the various parameters and its analysis done accurately. Finally, this paper can help academics better understand the RPL and engage in future research projects to improve it.

The aim of this paper is to describe the battery lifetime estimation and energy consumption model of the sensor nodes in TDMA wireless mesh sensor using merged data collecting (MDC) methods based on lithium thionyl chloride batteries. Defining the energy consumption of the nodes in wireless mesh networks is crucial for battery lifetime estimation. In this paper, we describe the timing, energy consumption, and battery lifetime estimation of the MDC method in the TDMA mesh sensor networks using flooding routing. For the battery life estimation, we made a semiempirical model that describes the energy consumption of the nodes with a real battery model. In this model, the low-level constraints are based on the measured energy consumption of the sensor nodes in different operation phases.

This paper presents an energy consumption model of the sensor nodes in TDMA wireless mesh sensor network using merged data collecting (MDC) methods. Defining the energy consumption of the nodes in wireless mesh networks is crucial for battery lifetime estimation. In this paper, we describe the semiempirical model of the energy consumption of MDC method in the TDMA mesh sensor networks using flooding routing. In the model the low-level constraints are based on the measured energy consumption of the sensor nodes in the different operation phases.

Mesh networks based on the wireless local area network (WLAN) technology, as specified by the standards amendment IEEE 802.11s, provide for a flexible and low-cost interconnection of devices and embedded systems for various use cases. To assess the real-world performance of WLAN mesh networks and potential optimization strategies, suitable testbeds and measurement tools are required. Designed for highly automated transport-layer throughput and latency measurements, the software FLExible Network Tester (Flent) is a promising candidate. However, so far Flent does not integrate information specific to IEEE 802.11s networks, such as peer link status data or mesh routing metrics. Consequently, we propose Flent extensions that allow to additionally capture IEEE 802.11s information as part of the automated performance tests. For the functional validation of our extensions, we conduct Flent measurements in a mesh mobility scenario using the network emulation framework Mininet-WiFi.

This paper investigates the robustness of the received signal strength (RSS)-based physical layer authentication (PLA) for wireless mesh networks, through experimental results. Specifically, we develop a secure wireless mesh networking framework and apply the RSS-based PLA scheme, with the aim to perform continuous authentication. The mesh setup comprises three Raspberry-PI4 computing nodes (acting as Alice, Bob, and Eve) and a server. The server role is to perform the initial authentication when a new node joins the mesh network. After that, the legitimate nodes in the mesh network perform continuous authentication, by leveraging the RSS feature of wireless signals. In particular, Bob tries to authenticate Alice in the presence of Eve. The performance of the presented framework is quantified through extensive experimental results in an outdoor environment, where various nodes' positions, relative distances, and pedestrian speeds scenarios are considered. The obtained results demonstrate the robustness of the underlying model, where an authentication rate of 99% for the static case can be achieved. Meanwhile, at the pedestrian speed, the authentication rate can drop to 85%. On the other hand, the detection rate improves when the distance between the legitimate and wiretap links is large (exceeds 20 meters) or when Alice and Eve are moving in different mobility patterns.

How can high-level directives concerning risk, cybersecurity and compliance be operationalized in the central nervous system of any organization above a certain complexity? How can the effectiveness of technological solutions for security be proven and measured, and how can this technology be aligned with the governance and financial goals at the board level? These are the essential questions for any CEO, CIO or CISO that is concerned with the wellbeing of the firm. The concept of Zero Trust (ZT) approaches information and cybersecurity from the perspective of the asset to be protected, and from the value that asset represents. Zero Trust has been around for quite some time. Most professionals associate Zero Trust with a particular architectural approach to cybersecurity, involving concepts such as segments, resources that are accessed in a secure manner and the maxim “always verify never trust”. This paper describes the current state of the art in Zero Trust usage. We investigate the limitations of current approaches and how these are addressed in the form of Critical Success Factors in the Zero Trust Framework developed by ON2IT ‘Zero Trust Innovators’ (1). Furthermore, this paper describes the design and engineering of a Zero Trust artefact that addresses the problems at hand (2), according to Design Science Research (DSR). The last part of this paper outlines the setup of an empirical validation trough practitioner oriented research, in order to gain a broader acceptance and implementation of Zero Trust strategies (3). The final result is a proposed framework and associated technology which, via Zero Trust principles, addresses multiple layers of the organization to grasp and align cybersecurity risks and understand the readiness and fitness of the organization and its measures to counter cybersecurity risks.

Artificial intelligence(AI) is used in decision support systems which learn and perceive features as a function of the number of layers and the weights computed during training. Due to their inherent black box nature, it is insufficient to consider accuracy, precision and recall as metrices for evaluating a model's performance. Domain knowledge is also essential to identify features that are significant by the model to arrive at its decision. In this paper, we consider a use case of face mask recognition to explain the application and benefits of XAI. Eight models used to solve the face mask recognition problem were selected. GradCAM Explainable AI (XAI) is used to explain the state-of-art models. Models that were selecting incorrect features were eliminated even though, they had a high accuracy. Domain knowledge relevant to face mask recognition viz., facial feature importance is applied to identify the model that picked the most appropriate features to arrive at the decision. We demonstrate that models with high accuracies need not be necessarily select the right features. In applications requiring rapid deployment, this method can act as a deciding factor in shortlisting models with a guarantee that the models are looking at the right features for arriving at the classification. Furthermore, the outcomes of the model can be explained to the user enhancing their confidence on the AI model being deployed in the field.

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decision-making, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a single explanation, which fails to account for the diversity of human thoughts and experiences in language. This paper thus addresses this gap, by proposing a generative XAI framework, INTERACTION (explain aNd predicT thEn queRy with contextuAl CondiTional varIational autO-eNcoder). Our novel framework presents explanation in two steps: (step one) Explanation and Label Prediction; and (step two) Diverse Evidence Generation. We conduct intensive experiments with the Transformer architecture on a benchmark dataset, e-SNLI [1]. Our method achieves competitive or better performance against state-of-the-art baseline models on explanation generation (up to 4.7% gain in BLEU) and prediction (up to 4.4% gain in accuracy) in step one; it can also generate multiple diverse explanations in step two.

This work proposed a unified approach to increase the explainability of the predictions made by Convolution Neural Networks (CNNs) on medical images using currently available Explainable Artificial Intelligent (XAI) techniques. This method in-cooperates multiple techniques such as LISA aka Local Interpretable Model Agnostic Explanations (LIME), integrated gradients, Anchors and Shapley Additive Explanations (SHAP) which is Shapley values-based approach to provide explanations for the predictions provided by Blackbox models. This unified method increases the confidence in the black-box model’s decision to be employed in crucial applications under the supervision of human specialists. In this work, a Chest X-ray (CXR) classification model for identifying Covid-19 patients is trained using transfer learning to illustrate the applicability of XAI techniques and the unified method (LISA) to explain model predictions. To derive predictions, an image-net based Inception V2 model is utilized as the transfer learning model.

Many studies have been conducted to detect various malicious activities in cyberspace using classifiers built by machine learning. However, it is natural for any classifier to make mistakes, and hence, human verification is necessary. One method to address this issue is eXplainable AI (XAI), which provides a reason for the classification result. However, when the number of classification results to be verified is large, it is not realistic to check the output of the XAI for all cases. In addition, it is sometimes difficult to interpret the output of XAI. In this study, we propose a machine learning model called classification verifier that verifies the classification results by using the output of XAI as a feature and raises objections when there is doubt about the reliability of the classification results. The results of experiments on malicious website detection and malware detection show that the proposed classification verifier can efficiently identify misclassified malicious activities.

Distributed wind is an electric energy resource segment with strong potential to be deployed in many applications, but special consideration of resilience and cybersecurity is needed to address the unique conditions associated with distributed wind. Distributed wind is a strong candidate to help meet renewable energy and carbon-free energy goals. However, care must be taken as more systems are installed to ensure that the systems are reliable, resilient, and secure. The physical and communications requirements for distributed wind mean that there are unique cybersecurity considerations, but there is little to no existing guidance on best practices for cybersecurity risk management for distributed wind systems specifically. This research develops an architecture for managing cyber risks associated with distributed wind systems through resilience functions. The architecture takes into account the configurations, challenges, and standards for distributed wind to create a risk-focused perspective that considers threats, vulnerabilities, and consequences. We show how the resilience functions of identification, preparation, detection, adaptation, and recovery can mitigate cyber threats. We discuss common distributed wind architectures and interconnections to larger power systems. Because cybersecurity cannot exist independently, the cyber-resilience architecture must consider the system holistically. Finally, we discuss risk assessment recommendations with special emphasis on what sets distributed wind systems apart from other distributed energy resources (DER).

Federated Learning (FL) is a multiparty learning computing approach that can aid privacy-preservation machine learning. However, FL has several potential security and privacy threats. First, the existing FL requires a central coordinator for the learning process which brings a single point of failure and trust issues for the shared trained model. Second, during the learning process, intentionally unreliable model updates performed by Byzantine colluding parties can lower the quality and convergence of the shared ML models. Therefore, discovering verifiable local model updates (i.e., integrity or correctness) and trusted parties in FL becomes crucial. In this paper, we propose a resilient and verifiable FL algorithm based on a reputation scheme to cope with unreliable parties. We develop a selection algorithm for task publisher and blockchain-based multiparty learning architecture approach where local model updates are securely exchanged and verified without the central party. We also proposed a novel auditing scheme to ensure our proposed approach is resilient up to 50% Byzantine colluding attack in a malicious scenario.

Peer-to-peer (P2P) systems such as BitTorrent and Bitcoin are susceptible to serious attacks from byzantine nodes that join as peers. Due to well-known impossibility results for designing P2P primitives in unrestricted byzantine settings, research has explored many adversarial models with additional assumptions, ranging from mild (such as pre-established PKI) to strong (such as the existence of common random coins). One such widely-studied model is the general-omission model, which yields simple protocols with good efficiency, but has been considered impractical or unrealizable since it artificially limits the adversary only to omitting messages.In this work, we study the setting of a synchronous network wherein peer nodes have CPUs equipped with a recent trusted computing mechanism called Intel SGX. In this model, we observe that the byzantine adversary reduces to the adversary in the general-omission model. As a first result, we show that by leveraging SGX features, we eliminate any source of advantage for a byzantine adversary beyond that gained by omitting messages, making the general-omission model realizable. Our evaluation of 1000 nodes running on 40 DeterLab machines confirms theoretical efficiency claim.

Non-orthogonal multiple access (NOMA) is emerging as a popular radio access technique to serve multiple users under the same resource block to improve spectral efficiency in 5G and 6G communication. But the resource sharing in NOMA causes concerns on data security. Since power domain NOMA exploits the difference in channel properties for bandwidth-efficient communication, it is feasible to ensure data confidentiality in NOMA communication through physical layer security techniques. In this work, we propose to ensure resistance against internal eavesdropping in NOMA communication through a secret key derived from channel randomness. A unique secret key is derived from the channel of each NOMA user; which is used to randomize the data of the respective user before superposition coding (SC) to prevent internal eavesdropping. The simulation results show that the proposed system provides very good security against internal eavesdropping in NOMA.

A wireless communication system with rotating linearly polarized antennas is built and tested as a method for increasing physical layer security. Controlling the linear polarization angle from 0° to 180° yields bit error rates greater than 20% for 40° of rotation.

Recently, there has been significant enthusiasms in exploiting physical (PHY-) layer characteristics for secure wireless communication. However, most existing PHY-layer security paradigms are information theoretical methodologies, which are infeasible to real and practical systems. In this paper, we propose a weighted fractional Fourier transform (WFRFT) pre-coding scheme to enhance the security of wireless transmissions against eavesdropping. By leveraging the concept of WFRFT, the proposed scheme can easily change the characteristics of the underlying radio signals to complement and secure upper-layer cryptographic protocols. We demonstrate a running prototype based on the LTE-framework. First, the compatibility between the WFRFT pre-coding scheme and the conversational LTE architecture is presented. Then, the security mechanism of the WFRFT pre-coding scheme is demonstrated. Experimental results validate the practicability and security performance superiority of the proposed scheme.

Zero-day attack is a critical network attack. The zero-day attack period (ZDAP) is the period from the release of malware/exploit until a patch becomes available. IDS/IPS cannot effectively block zero-day attacks because they use pattern-based signatures in general. This paper proposes a Prophetic Defender (PD) by which ZDAP can be minimized. Prior to actual attack, hackers scan networks to identify hosts with vulnerable ports. If this port scanning can be detected early, zero-day attacks will become detectable. PD architecture makes use of a honeypot-based pseudo server deployed to detect malicious port scans. A port-scanning honeypot was operated by us in 6 years from 2009 to 2015. By analyzing the 6-year port-scanning log data, we understand that PD is effective for detecting and blocking zero-day attacks. The block rate of the proposed architecture is 98.5%.

Objective measures are ubiquitous in the formulation, design and implementation of deep space missions. Tour durations, flyby altitudes, propellant budgets, power consumption, and other metrics are essential to developing and managing NASA missions. But beyond the simple metrics of cost and workforce, it has been difficult to identify objective, quantitative measures that assist in evaluating choices made during formulation or implementation phases in terms of their impact on flight operations. As part of the development of the Europa Clipper Mission system, a set of operations metrics have been defined along with the necessary design information and software tooling to calculate them. We have applied these methods and metrics to help assess the impact to the flight team on the six options for the Clipper Tour that are currently being vetted for selection in the fall of 2021. To generate these metrics, the Clipper MOS team first designed the set of essential processes by which flight operations will be conducted, using a standard approach and template to identify (among other aspects) timelines for each process, along with their time constraints (e.g., uplinks for sequence execution). Each of the resulting 50 processes is documented in a common format and concurred by stakeholders. Process timelines were converted into generic schedules and workforce-loaded using COTS scheduling software, based on the inputs of the process authors and domain experts. Custom code was generated to create an operations schedule for a specific portion of Clipper's prime mission, with instances of a given process scheduled based on specific timing rules (e.g., process X starts once per week on Thursdays) or relative to mission events (e.g., sequence generation process begins on a Monday, at least three weeks before each Europa closest approach). Over a 5-month period, and for each of six Clipper candidate tours, the result was a 20,000+ line, workforce-loaded schedule that documents all of the process-driven work effort at the level of individual roles, along with a significant portion of the level-of-effort work. Post-processing code calculated the absolute and relative number of work hours during a nominal 5 day / 40 hour work week, the work effort during 2nd and 3rd shift, as well as 1st shift on weekends. The resultant schedules and shift tables were used to generate objective measures that can be related to both human factors and to operational risk and showed that Clipper tours which utilize 6:1 resonant (21.25 day) orbits instead of 4:1 resonant (14.17 day) orbits during the first dozen or so Europa flybys are advantageous to flight operations. A similar approach can be extended to assist missions in more objective assessments of a number of mission issues and trades, including tour selection and spacecraft design for operability.

With the increasing number of catastrophic weather events and resulting disruption in the energy supply to essential loads, the distribution grid operators’ focus has shifted from reliability to resiliency against high impact, low-frequency events. Given the enhanced automation to enable the smarter grid, there are several assets/resources at the disposal of electric utilities to enhances resiliency. However, with a lack of comprehensive resilience tools for informed operational decisions and planning, utilities face a challenge in investing and prioritizing operational control actions for resiliency. The distribution system resilience is also highly dependent on system attributes, including network, control, generating resources, location of loads and resources, as well as the progression of an extreme event. In this work, we present a novel multi-stage resilience measure called the Anticipate-Withstand-Recover (AWR) metrics. The AWR metrics are based on integrating relevant ‘system characteristics based factors’, before, during, and after the extreme event. The developed methodology utilizes a pragmatic and flexible approach by adopting concepts from the national emergency preparedness paradigm, proactive and reactive controls of grid assets, graph theory with system and component constraints, and multi-criteria decision-making process. The proposed metrics are applied to provide decision support for a) the operational resilience and b) planning investments, and validated for a real system in Alaska during the entirety of the event progression.

The risk of detecting incidents in the field of computer technology in Maritime transport is considered. The structure of the computer incident investigation system and its functions are given. The system of conducting investigations of computer incidents on sea transport is considered. A possible algorithm for investigating the incident using the tools of forensic science and an algorithm for transmitting the received data for further processing are presented.

This paper proposes a lightweight digital signing scheme for supporting document signing on mobile devices connected to cloud computing. We employ elliptic curve (ECC) digital signature algorithm (ECDSA) for key pair generation done at mobile device and introduce outsourced proxy (OSP) to decrypt the encrypted file and compute hash value of the files stored in the cloud system. In our model, a mobile client invokes fixed-sized message digests to be signed with a private key stored in the device and produces the digital signature. Then, the signature is returned to the proxy for embedding it onto the original file. To this end, the trust between proxy and mobile devices is guaranteed by PKI technique. Based on the lightweight property of ECC and the modular design of our OSP, our scheme delivers the practical solution that allows mobile users to create their own digital signatures onto documents in a secure and efficient way. We also present the implementation details including system development and experimental evaluation to demonstrate the efficiency of our proposed system.

Efficient low-power accelerators for Convolutional Neural Networks (CNNs) largely benefit from quantization and approximation, which are typically applied layer-wise for efficient hardware implementation. In this work, we present a novel strategy for efficient combination of these concepts at a deeper level, which is at each channel or kernel. We first apply layer-wise, low bit-width, linear quantization and truncation-based approximate multipliers to the CNN computation. Then, based on a state-of-the-art resiliency analysis, we are able to apply a kernel-wise approximation and quantization scheme with negligible accuracy losses, without further retraining. Our proposed strategy is implemented in a specialized framework for fast design space exploration. This optimization leads to a boost in estimated power savings of up to 34% in residual CNN architectures for image classification, compared to the base quantized architecture.