Biblio

Nowadays, Security is the biggest challenge in communication networks. Well defined security protocols not only solve the privacy and security issues but also help to reduce the implementation cost and simplify network's operation. Network society demands more reliable and secure network services as well as infrastructure. In communication networks, data theft, hacking, fraud, cyber warfare are serious security threats. Security as defined by experts is confirming protected communication amongst communication/computing systems and consumer applications in private and public networks, it is important for promising privacy, confidentiality, and protection of information. This paper highlights the security related issues and challenges in communication networks. We also present the holistic view for the underlaying physical layer including physical infrastructure attacks, jamming, interception, and eavesdropping. This research focused on improving the security measures and protocols in different communication networks.

Trust among humans affects the way we interact with each other. In autonomous systems, this trust is often predefined and hard-coded before the systems are deployed. However, when systems encounter unfolding situations, requiring them to interact with others, a notion of trust will be inevitable. In this paper, we discuss trust as a fundamental measure to enable an autonomous system to decide whether or not to interact with another system, whether biological or artificial. These decisions become increasingly important when continuously integrating with others during runtime.

The identification of spectrum opportunities is a pivotal requirement for efficient spectrum utilization in cognitive radio systems. Spectrum prediction offers a convenient means for revealing such opportunities based on the previously obtained occupancies. As spectrum occupancy states are correlated over time, spectrum prediction is often cast as a predictable time-series process using classical or deep learning-based models. However, this variety of methods exploits time-domain correlation and overlooks the existing correlation over frequency. In this paper, differently from previous works, we investigate a more realistic scenario by exploiting correlation over time and frequency through a 2D-long short-term memory (LSTM) model. Extensive experimental results show a performance improvement over conventional spectrum prediction methods in terms of accuracy and computational complexity. These observations are validated over the real-world spectrum measurements, assuming a frequency range between 832-862 MHz where most of the telecom operators in Turkey have private uplink bands.

For classical fault analysis, a transient fault is required to be injected during runtime, e.g., only at a specific round. Instead, Persistent Fault Analysis (PFA) introduces a powerful class of fault attacks that allows for a fault to be present throughout the whole execution. One limitation of original PFA as introduced by Zhang et al. at CHES'18 is that the adversary needs know (or brute-force) the faulty values prior to the analysis. While this was addressed at a follow-up work at CHES'20, the solution is only applicable to a single faulty value. Instead, we use the potency of Statistical Fault Analysis (SFA) in the persistent fault setting, presenting Statistical Persistent Fault Analysis (SPFA) as a more general approach of PFA. As a result, any or even a multitude of unknown faults that cause an exploitable bias in the targeted round can be used to recover the cipher's secret key. Indeed, the undesired faults in the other rounds that occur due the persistent nature of the attack converge to a uniform distribution as required by SFA. We verify the effectiveness of our attack against LED and AES.

By leveraging the advancements in Information and Communication Technologies (ICT), Smart Grid (SG) aims to modernize the traditional electric power grid towards efficient distribution and reliable management of energy in the electrical domain. The SG Advanced Metering Infrastructure (AMI) contains numerous smart meters, which are deployed throughout the distribution grid. However, these smart meters are susceptible to cyberthreats that aim to disrupt the normal operation of the SG. Cyberattacks can have various consequences in the smart grid, such as incorrect customer billing or equipment destruction. Therefore, these devices should operate on a trusted basis in order to ensure the availability, confidentiality, and integrity of the metering data. In this paper, we propose a Markov chain trust model that determines the Trust Value (TV) for each AMI device based on its behavior. Finally, numerical computations were carried out in order to investigate the reaction of the proposed model to the behavior changes of a device.

In this work, communication secrecy in cooperative and multi-hop wireless communications for various radio frequencies are examined. Attenuation lines and ranges of both detection and ultimate secrecy regions were calculated for cooperative communication channel and multi-hop channel with various number of hops. From results, frequency ranges with the highest potential to apply bandwidth saving method known as frequency reuse were determined and compared to point-to-point channel. Frequencies with the highest attenuation were derived and their ranges of both detection and ultimate secrecy are calculated. Point-to-point, cooperative and multi-hop channels were compared in terms of ultimate secrecy ranges. Multi-hop channel measurements were made with different number of hops and the relation between the number of hops and communication security is examined. Ultimate secrecy ranges were calculated up to 1 Terahertz and found to be less than 13 meters between 550-565 GHz frequency range. Therefore, for short-range wireless communication systems such as indoor and in-device communication systems (board-to-board or chip-to-chip communications), it is shown that various bands in the Terahertz band can be used to reuse the same frequency in different locations to obtain high security and high bandwidth.

The rapid deployment of digital systems into all aspects of daily life requires embedding social constructs into the digital world. Because of the complexity of these systems, there is a need for technical support to understand their actions. Social concepts, such as explainability, accountability, and responsibility rely on a notion of actual causality. Encapsulated in the Halpern and Pearl’s (HP) definition, actual causality conveniently integrates into the socio-technical world if operationalized in concrete applications. To the best of our knowledge, theories of actual causality such as the HP definition are either applied in correspondence with domain-specific concepts (e.g., a lineage of a database query) or demonstrated using straightforward philosophical examples. On the other hand, there is a lack of explicit automated actual causality theories and operationalizations for helping understand the actions of systems. Therefore, this paper proposes a unifying framework and an interactive platform (Actual Causality Canvas) to address the problem of operationalizing actual causality for different domains and purposes. We apply this framework in such areas as aircraft accidents, unmanned aerial vehicles, and artificial intelligence (AI) systems for purposes of forensic investigation, fault diagnosis, and explainable AI. We show that with minimal effort, using our general-purpose interactive platform, actual causality reasoning can be integrated into these domains.

Ransomware have observed a steady growth over the years with several concerning trends that indicate efficient, targeted attacks against organizations and individuals alike. These opportunistic attackers indiscriminately target both public and private sector entities to maximize gain. In this article, we highlight the criticality of key management in ransomware's cryptosystem in order to facilitate building effective solutions against this threat. We introduce the ransomware kill chain to elucidate the path our adversaries must take to attain their malicious objective. We examine current solutions presented against ransomware in light of this kill chain and specify which constraints on ransomware are being violated by the existing solutions. Finally, we present the notion of memory attacks against ransomware's key management and present our initial experiments with dynamically extracting decryption keys from real-world ransomware. Results of our preliminary research are promising and the extracted keys were successfully deployed in subsequent data decryption.

In this study, it was aimed to recognize the emotional state from facial images using the deep learning method. In the study, which was approved by the ethics committee, a custom data set was created using videos taken from 20 male and 20 female participants while simulating 7 different facial expressions (happy, sad, surprised, angry, disgusted, scared, and neutral). Firstly, obtained videos were divided into image frames, and then face images were segmented using the Haar library from image frames. The size of the custom data set obtained after the image preprocessing is more than 25 thousand images. The proposed convolutional neural network (CNN) architecture which is mimics of LeNet architecture has been trained with this custom dataset. According to the proposed CNN architecture experiment results, the training loss was found as 0.0115, the training accuracy was found as 99.62%, the validation loss was 0.0109, and the validation accuracy was 99.71%.

In this paper, a novel Dynamic Chaotic Biometric Identity Isomorphic Elliptic Curve (DCBI-IEC) has been introduced for Image Encryption. The biometric digital identity is extracted from the user fingerprint image as fingerprint minutia data incorporated with the chaotic logistic map and hence, a new DCBDI-IEC has been suggested. DCBI-IEC is used to control the key schedule for all encryption and decryption processing. Statistical analysis, differential analysis and key sensitivity test are performed to estimate the security strengths of the proposed DCBI-IEC system. The experimental results show that the proposed algorithm is robust against common signal processing attacks and provides a high security level for image encryption application.

The use of biometrics in security applications may be vulnerable to several challenges of hacking. Thus, the emergence of cancellable biometrics becomes a suitable solution to this problem. This paper presents a one-way cancellable biometric transform that depends on 3D chaotic maps for face and fingerprint encryption. It aims to avoid cloning of original biometrics and allow the templates used by each user in different applications to be variable. The permutations achieved with the chaotic maps guarantee high security of the biometric templates, especially with the 3D implementation of the encryption algorithm. In addition, the paper presents a hardware implementation for this framework. The proposed algorithm also achieves good performance in the presence of low and moderate levels of noise. An experimental version of the proposed cancellable biometric system has been applied on FPGA model. The obtained results achieve a powerful performance of the proposed cancellable biometric system.

Fog computing is a new distributed computing paradigm that extends the cloud to the network edge. Fog computing aims at improving quality of service, data access, networking, computation and storage. However, the security and privacy issues persist, even if many cloud solutions were proposed. Indeed, Fog computing introduces new challenges in terms of security and privacy, due to its specific features such as mobility, geo-distribution and heterogeneity etc. Blockchain is an emergent concept bringing efficiency in many fields. In this paper, we propose a new access control scheme based on blockchain technology for the fog computing with fault tolerance in the context of the Internet of Things. Blockchain is used to provide secure management authentication and access process to IoT devices. Each network entity authenticates in the blockchain via the wallet, which allows a secure communication in decentralized environment, hence it achieves the security objectives. In addition, we propose to establish a secure connection between the users and the IoT devices, if their attributes satisfy the policy stored in the blockchain by smart contract. We also address the blockchain transparency problem by the encryption of the users attributes both in the policy and in the request. An authorization token is generated if the encrypted attributes are identical. Moreover, our proposition offers higher scalability, availability and fault tolerance in Fog nodes due to the implementation of load balancing through the Min-Min algorithm.

The ever-growing threats of cascading failures due to cyber-attacks pose a significant challenge to power grid security. A wrong system state estimate caused by a false data injection attack could lead to a wrong control actions and take the system into a more insecure operating condition. As a consequence, an attack-resilient failure mitigation strategy needs to be developed to correctly determine control actions to prevent the propagation of cascades. In this paper, a recovery-based model predictive control methodology is developed to eliminate power system component violations following coordinated cyber-physical attacks where physical attacks are masked by targeted false data injection attacks. Specifically, to address the problem of wrong system state estimation with compromised data, a developed methodology recovers the incorrect states from historical data rather than utilizing the tampered data, and thus allowing control centers to identify proper control actions. Additionally, instead of using a one-step method to optimize control actions, the recovery-based model predictive control methodology scheme incorporates the effect of controls over a finite time horizon and the attack detection delay to make appropriate control decisions. Case studies, performed on IEEE 30-bus and Illinois 200-bus systems, show that the developed recovery-based model predictive control methodology scheme is robust to coordinated attacks and efficient in mitigating cascades.

FPGAs are becoming a common sight in cloud environments and new usage paradigms, such as FPGA-as-a-Service, have emerged. This development poses a challenge to traditional FPGA security models, as these are assuming trust between the user and the hardware owner. Currently, the user cannot keep bitstream nor data protected from the hardware owner in an FPGA-as-a-service setting. This paper proposes a security model where the chip manufacturer takes the role of root-of-trust to remedy these security problems. We suggest that the chip manufacturer creates a Public Key Infrastructure (PKI), used for user bitstream protection and data encryption, on each device. The chip manufacturer, rather than the hardware owner, also controls certain security-related peripherals. This allows the user to take control over a predefined part of the programmable logic and set up a protected enclave area. Hence, all user data can be provided in encrypted form and only be revealed inside the enclave area. In addition, our model enables secure and concurrent multi-tenant usage of remote FPGAs. To also consider the needs of the hardware owner, our solution includes bitstream certification and affirming that uploaded bitstreams have been vetted against maliciousness.

Explainable Artificial Intelligence (XAI), i.e., the development of more transparent and interpretable AI models, has gained increased traction over the last few years. This is due to the fact that, in conjunction with their growth into powerful and ubiquitous tools, AI models exhibit one detrimental characteristic: a performance-transparency trade-off. This describes the fact that the more complex a model's inner workings, the less clear it is how its predictions or decisions were achieved. But, especially considering Machine Learning (ML) methods like Reinforcement Learning (RL) where the system learns autonomously, the necessity to understand the underlying reasoning for their decisions becomes apparent. Since, to the best of our knowledge, there exists no single work offering an overview of Explainable Reinforcement Learning (XRL) methods, this survey attempts to address this gap. We give a short summary of the problem, a definition of important terms, and offer a classification and assessment of current XRL methods. We found that a) the majority of XRL methods function by mimicking and simplifying a complex model instead of designing an inherently simple one, and b) XRL (and XAI) methods often neglect to consider the human side of the equation, not taking into account research from related fields like psychology or philosophy. Thus, an interdisciplinary effort is needed to adapt the generated explanations to a (non-expert) human user in order to effectively progress in the field of XRL and XAI in general.

Blockchain platforms are coming into use for processing critical transactions among participants who have not established mutual trust. Many blockchains are programmable, supporting smart contracts, which maintain persistent state and support transactions that transform the state. Unfortunately, bugs in many smart contracts have been exploited by hackers. Obsidian is a novel programming language with a type system that enables static detection of bugs that are common in smart contracts today. Obsidian is based on a core calculus, Silica, for which we proved type soundness. Obsidian uses typestate to detect improper state manipulation and uses linear types to detect abuse of assets. We integrated a permissions system that encodes a notion of ownership to allow for safe, flexible aliasing. We describe two case studies that evaluate Obsidian’s applicability to the domains of parametric insurance and supply chain management, finding that Obsidian’s type system facilitates reasoning about high-level states and ownership of resources. We compared our Obsidian implementation to a Solidity implementation, observing that the Solidity implementation requires much boilerplate checking and tracking of state, whereas Obsidian does this work statically.

Context:

The ‘as code’ suffix in infrastructure as code (IaC) refers to applying software engineering activities, such as version control, to maintain IaC scripts. Without the application of these activities, defects that can have serious consequences may be introduced in IaC scripts. A systematic investigation of the development anti-patterns for IaC scripts can guide practitioners in identifying activities to avoid defects in IaC scripts. Development anti-patterns are recurring development activities that relate with defective IaC scripts.

Goal:

The goal of this paper is to help practitioners improve the quality of infrastructure as code (IaC) scripts by identifying development activities that relate with defective IaC scripts.

Methodology:

We identify development anti-patterns by adopting a mixed-methods approach, where we apply quantitative analysis with 2,138 open source IaC scripts and conduct a survey with 51 practitioners.

Findings:

We observe five development activities to be related with defective IaC scripts from our quantitative analysis. We identify five development anti-patterns namely, ‘boss is not around’, ‘many cooks spoil’, ‘minors are spoiler’, ‘silos’, and ‘unfocused contribution’.

Conclusion:

Our identified development anti-patterns suggest the importance of ‘as code’ activities in IaC because these activities are related to quality of IaC scripts.

We present an integrated method for safety assessment of automated driving systems which covers the aspects of functional safety and safety of the intended functionality (SOTIF), including identification and quantification of hazardous scenarios. The proposed method uses and combines established exploration and analytical tools for hazard analysis and risk assessment in the automotive domain, while adding important enhancements to enable their applicability to the uncharted territory of safety analyses for automated driving. The method is tailored to support existing safety processes mandated by the standards ISO 26262 and ISO/PAS 21448 and complements them where necessary. It has been developed in close cooperation with major German automotive manufacturers and suppliers within the PEGASUS project (https://www.pegasusprojekt.de/en). Practical evaluation has been carried out by applying the method to the PEGASUS Highway-Chauffeur, a conceptual automated driving function considered as a common reference system within the project.

Cyber-Physical Systems (CPSs) play an increasingly significant role in many critical applications. These valuable applications attract various sophisticated attacks. This paper considers a stealthy estimation attack, which aims to modify the state estimation of the CPSs. The intelligent attackers can learn defense strategies and use clandestine attack strategies to avoid detection. To address the issue, we design a Chi-square detector in a Digital Twin (DT), which is an online digital model of the physical system. We use a Signaling Game with Evidence (SGE) to find the optimal attack and defense strategies. Our analytical results show that the proposed defense strategies can mitigate the impact of the attack on the physical estimation and guarantee the stability of the CPSs. Finally, we use an illustrative application to evaluate the performance of the proposed framework.

The paper considers an expert system that provides an assessment of the state of information security in authorities and organizations of various forms of ownership. The proposed expert system allows to evaluate the state of compliance with the requirements of both organizational and technical measures to ensure the protection of information, as well as the level of compliance with the requirements of the information protection system in general. The expert assessment method is used as a basic method for assessing the state of information protection. The developed expert system provides a significant reduction in routine operations during the audit of information security. The results of the assessment are presented quite clearly and provide an opportunity for the leadership of the authorities and organizations to make informed decisions to further improve the information protection system.

In recent years, with the progress of data analysis methods utilizing artificial intelligence (AI) technology, concepts of smart cities collecting data from IoT devices and creating values by analyzing it have been proposed. However, making sure that the data is not tampered with is of the utmost importance. One way to do this is to utilize blockchain technology to record and trace the history of the data. Park and Kim proposed ensuring the trustworthiness of the data by utilizing an IoT device with a trusted execution environment (TEE). Also, Guan et al. proposed authenticating an IoT device and mediating data using a TEE. For the authentication, they use the physically unclonable function of the IoT device. Usually, IoT devices suffer from the lack of resources necessary for creating transactions for the blockchain ledger. In this paper, we present a secure protocol in which a TEE acts as a proxy to the IoT devices and creates the necessary transactions for the blockchain. We use an authenticated encryption method on the data transmission between the IoT device and TEE to authenticate the device and ensure the integrity and confidentiality of the data generated by the IoT devices.

Distributed banking platforms and services forgo centralized banks to process financial transactions. For example, M-Pesa provides distributed banking service in the developing regions so that the people without a bank account can deposit, withdraw, or transfer money. The current distributed banking systems lack the transparency in monitoring and tracking of distributed banking transactions and thus do not support auditing of distributed banking transactions for accountability. To address this issue, this paper proposes a blockchain-based distributed banking (BDB) scheme, which uses blockchain technology to leverage its built-in properties to record and track immutable transactions. BDB supports distributed financial transaction processing but is significantly different from cryptocurrencies in its design properties, simplicity, and computational efficiency. We implement a prototype of BDB using smart contract and conduct experiments to show BDB's effectiveness and performance. We further compare our prototype with the Ethereum cryptocurrency to highlight the fundamental differences and demonstrate the BDB's superior computational efficiency.

Industrial control systems (ICSs) are used in various infrastructures and industrial plants for realizing their control operation and ensuring their safety. Concerns about the cybersecurity of industrial control systems have raised due to the increased number of cyber-attack incidents on critical infrastructures in the light of the advancement in the cyber activity of ICSs. Nevertheless, the operation of the industrial control systems is bind to vital aspects in life, which are safety, economy, and security. This paper presents a semi-supervised, hybrid attack detection approach for industrial control systems by combining Isolation Forest and Convolutional Neural Network (CNN) models. The proposed framework is developed using the normal operational data, and it is composed of a feature extraction model implemented using a One-Dimensional Convolutional Neural Network (1D-CNN) and an isolation forest model for the detection. The two models are trained independently such that the feature extraction model aims to extract useful features from the continuous-time signals that are then used along with the binary actuator signals to train the isolation forest-based detection model. The proposed approach is applied to a down-scaled industrial control system, which is a water treatment plant known as the Secure Water Treatment (SWaT) testbed. The performance of the proposed method is compared with the other works using the same testbed, and it shows an improvement in terms of the detection capability.

In a previous article [1] we proposed a layered framework to support the assessment of the security risks associated with the use of autonomous vehicles in military operations and determine how to manage these risks appropriately. We established consistent terminology and defined the problem space, while exploring the first layer of the framework, namely risks from the mission assurance perspective. In this paper, we develop the second layer of the framework. This layer focuses on the risk assessment of the vehicles themselves and on producing a highlevel security design adequate for the mission defined in the first layer. To support this process, we also define a reference model for autonomous vehicles to use as a common basis for the assessment of risks and the design of the security controls.