Biblio

Optical wireless communications (OWC) and its potential to solve physical layer security (PLS) issues are becoming important research areas in 6G communications systems. In this paper, an overview of PLS in visible light communications (VLC), is presented. Then, two new PLS techniques based on generalized space shift keying (GSSK) modulation with spatial constellation design (SCD) and non-orthogonal multiple access (NOMA) cooperative relaying are introduced. In the first technique, the PLS of the system is enhanced by the appropriate selection of a precoding matrix for randomly activated light emitting diodes (LEDs). With the aid of a legitimate user's (Bob's) channel state information (CSI) at the transmitter (CSIT), the bit error ratio (BER) of Bob is minimized while the BER performance of the potential eavesdroppers (Eves) is significantly degraded. In the second technique, superposition coding with uniform signaling is used at the transmitter and relays. The design of secure beamforming vectors at the relay nodes along with NOMA techniques is used to enhance PLS in a VLC system. Insights gained from the improved security levels of the proposed techniques are used to discuss how PLS can be further improved in future generation communication systems by using VLC.

Recent advances in generative adversarial networks have made detecting fake videos a challenging task. In this paper, we propose the application of the state-of-the-art attribution based confidence (ABC) metric for detecting deepfake videos. The ABC metric does not require access to the training data or training the calibration model on the validation data. The ABC metric can be used to draw inferences even when only the trained model is available. Here, we utilize the ABC metric to characterize whether a video is original or fake. The deep learning model is trained only on original videos. The ABC metric uses the trained model to generate confidence values. For, original videos, the confidence values are greater than 0.94.

Network port scans are a key first step to developing a true understanding of a network-facing attack surface. However in large-scale networks, the data resulting from such scans can be too numerous for Red Teams to process for manual and semiautomatic testing. Indiscriminate port scans can also compromise a Red Team seeking to quickly gain a foothold on a network. A large attack surface can even complicate Blue Team activities like threat hunting. In this paper we provide a cluster analysis methodology designed to group similar hosts to reduce security team workload and Red Team observability. We also measure the Internet-facing network attack surface of 13 organizations by clustering their hosts based on similarity. Through a case study we demonstrate how the output of our clustering technique provides new insight to both Red and Blue Teams, allowing them to quickly identify potential high-interest points on the attack surface.

Antisparse coding aims at spreading the information uniformly over representation coefficients and can be expressed as the solution of an ℓ∞-norm regularized problem. In this paper, we propose a new methodology, coined "safe squeezing", accelerating the computation of antisparse representations. The idea consists in identifying saturated entries of the solution via simple tests and compacting their contribution to achieve some form of dimensionality reduction. Numerical experiments show that the proposed approach leads to significant computational gain.

Network security policies contain requirements - including system and software features as well as expected and desired actions of human actors. In this paper, we present a framework for evaluation of textual network security policies as requirements documents to identify areas for improvement. Specifically, our framework concentrates on completeness. We use topic modeling coupled with expert evaluation to learn the complete list of important topics that should be addressed in a network security policy. Using these topics as a checklist, we evaluate (students) a collection of network security policies for completeness, i.e., the level of presence of these topics in the text. We developed three methods for topic recognition to identify missing or poorly addressed topics. We examine network security policies and report the results of our analysis: preliminary success of our approach.

A common way to characterize security enforcement mechanisms is based on the time at which they operate. Mechanisms operating before a program's execution are static mechanisms, and mechanisms operating during a program's execution are dynamic mechanisms. This paper introduces a different perspective and classifies mechanisms based on the granularity of program code that they monitor. Classifying mechanisms in this way provides a unified view of security mechanisms and shows that all security mechanisms can be encoded as dynamic mechanisms that operate at different levels of program code granularity. The practicality of the approach is demonstrated through a prototype implementation of a framework for enforcing security policies at various levels of code granularity on Java bytecode applications.

This paper has firstly introduced big data services and cloud computing model based on different process forms, and analyzed the authentication technology and security services of the existing big data to understand their processing characteristics. Operation principles and complexity of the big data services and cloud computing have also been studied, and summary about their suitable environment and pros and cons have been made. Based on the Cloud Computing, the author has put forward the Model of Big Data Cloud Computing based on Extended Subjective Logic (MBDCC-ESL), which has introduced Jφsang's subjective logic to test the data credibility and expanded it to solve the problem of the trustworthiness of big data in the cloud computing environment. Simulation results show that the model works pretty well.

Mobile browsers have become one of the main mediators of our online activities. However, as web pages continue to increase in size and streaming media on-the-go has become commonplace, mobile data plan constraints remain a significant concern for users. As a result, data-saving features can be a differentiating factor when selecting a mobile browser. In this paper, we present a comprehensive exploration of the security and privacy threat that data-saving functionality presents to users. We conduct the first analysis of Android's data-saving browser (DSB) ecosystem across multiple dimensions, including the characteristics of the various browsers' infrastructure, their application and protocol-level behavior, and their effect on users' browsing experience. Our research unequivocally demonstrates that enabling data-saving functionality in major browsers results in significant degradation of the user's security posture by introducing severe vulnerabilities that are not otherwise present in the browser during normal operation. In summary, our experiments show that enabling data savings exposes users to (i) proxy servers running outdated software, (ii) man-in-the-middle attacks due to problematic validation of TLS certificates, (iii) weakened TLS cipher suite selection, (iv) lack of support of security headers like HSTS, and (v) a higher likelihood of being labelled as bots. While the discovered issues can be addressed, we argue that data-saving functionality presents inherent risks in an increasingly-encrypted Web, and users should be alerted of the critical savings-vs-security trade-off that they implicitly accept every time they enable such functionality.

In recent days, cloud computing is one of the emerging fields. It is a platform to maintain the data and privacy of the users. To process and regulate the data with high security, the access control methods are used. The cloud environment always faces several challenges such as robustness, security issues and so on. Conventional methods like Cipher text-Policy Attribute-Based Encryption (CP-ABE) are reflected in providing huge security, but still, the problem exists like the non-existence of attribute revocation and minimum efficient. Hence, this research work particularly on the attribute-based mechanism to maximize efficiency. Initially, an objective coined out in this work is to define the attributes for a set of users. Secondly, the data is to be re-encrypted based on the access policies defined for the particular file. The re-encryption process renders information to the cloud server for verifying the authenticity of the user even though the owner is offline. The main advantage of this work evaluates multiple attributes and allows respective users who possess those attributes to access the data. The result proves that the proposed Data sharing scheme helps for Revocation under a fine-grained attribute structure.

The autonomy of intelligent systems and their safety rely on their ability for local decision making based on collected environmental information. This is even more for cyber-physical systems running safety critical activities. While this intelligence is partial and fragmented, and cognitive techniques are of limited maturity, the decision function must produce results whose validity and scope must be weighted in light of the underlying assumptions, unavoidable uncertainty and hypothetical safety limitation. Besides the cognitive techniques dependability, it is about the assurance level of the decision self-making. Beyond the pure decision-making capabilities of the autonomous intelligent system, we need techniques that guarantee the system assurance required for the intended use. Security mechanisms for cognitive systems may be consequently tightly intricated. We propose a trustworthiness module which is part of the system and its resulting safety. In this paper, we briefly review the state of the art regarding the dependability of cognitive techniques, the assurance level definition in this context, and related engineering practices. We elaborate regarding the design of autonomous intelligent systems safety, then we discuss its security design and approaches for the mitigation of safety violations by the cognitive functions.

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.

Humans are majorly identified as the weakest link in cybersecurity. Tertiary institution students undergo lot of cybersecurity issues due to their constant Internet exposure, however there is a lack in literature with regards to tertiary institution students' cybersecurity behaviors. This research aimed at linking the factors responsible for tertiary institutions students' cybersecurity behavior, via validated cybersecurity factors, Perceived Vulnerability (PV); Perceived Barriers (PBr); Perceived Severity (PS); Security Self-Efficacy (SSE); Response Efficacy (RE); Cues to Action (CA); Peer Behavior (PBhv); Computer Skills (CS); Internet Skills (IS); Prior Experience with Computer Security Practices (PE); Perceived Benefits (PBnf); Familiarity with Cyber-Threats (FCT), thus exploring the relationship between the factors and the students' Cybersecurity Behaviors (CSB). A cross-sectional online survey was used to gather data from 450 undergraduate and postgraduate students from tertiary institutions within Klang Valley, Malaysia. Correlation Analysis was used to find the relationships existing among the cybersecurity behavioral factors via SPSS version 25. Results indicate that all factors were significantly related to the cybersecurity behaviors of the students apart from Perceived Severity. Practically, the study instigates the need for more cybersecurity training and practices in the tertiary institutions.

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.

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 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 recent years, integrated circuits (ICs) have become significant for various industries and their security has been given greater priority, specifically in the supply chain. Budgetary constraints have compelled IC designers to offshore manufacturing to third-party companies. When the designer gets the manufactured ICs back, it is imperative to test for potential threats like hardware trojans (HT). In this paper, a novel multi-level game-theoretic framework is introduced to analyze the interactions between a malicious IC manufacturer and the tester. In particular, the game is formulated as a non-cooperative, zero-sum, repeated game using prospect theory (PT) that captures different players' rationalities under uncertainty. The repeated game is separated into a learning stage, in which the defender learns about the attacker's tendencies, and an actual game stage, where this learning is used. Experiments show great incentive for the attacker to deceive the defender about their actual rationality by "playing dumb" in the learning stage (deception). This scenario is captured using hypergame theory to model the attacker's view of the game. The optimal deception rationality of the attacker is analytically derived to maximize utility gain. For the defender, a first-step deception mitigation process is proposed to thwart the effects of deception. Simulation results show that the attacker can profit from the deception as it can successfully insert HTs in the manufactured ICs without being detected.

Privacy preservation is one of the greatest concerns when data is shared between different organizations. On the one hand, releasing data for research purposes is inevitable. On the other hand, sharing this data can jeopardize users' privacy. An effective solution, for the sharing organizations, is to use anonymization techniques to hide the users' sensitive information. One of the most popular anonymization techniques is k-Anonymization in which any data record is indistinguishable from at least k-1 other records. However, one of the fundamental challenges in choosing the value of k is the trade-off between achieving a higher privacy and the information loss associated with the anonymization. In this paper, the problem of choosing the optimal anonymization level for k-anonymization, under possible attacks, is studied when multiple organizations share their data to a common platform. In particular, two common types of attacks are considered that can target the k-anonymization technique. To this end, a novel game-theoretic framework is proposed to model the interactions between the sharing organizations and the attacker. The problem is formulated as a static game and its different Nash equilibria solutions are analytically derived. Simulation results show that the proposed framework can significantly improve the utility of the sharing organizations through optimizing the choice of k value.

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.

Vendors who wish to provide software or services to large corporations and governments must often obtain numerous certificates of compliance. Each certificate asserts that the software satisfies a compliance regime, like SOC or the PCI DSS, to protect the privacy and security of sensitive data. The industry standard for obtaining a compliance certificate is an auditor manually auditing source code. This approach is expensive, error-prone, partial, and prone to regressions. We propose continuous compliance to guarantee that the codebase stays compliant on each code change using lightweight verification tools. Continuous compliance increases assurance and reduces costs. Continuous compliance is applicable to any source-code compliance requirement. To illustrate our approach, we built verification tools for five common audit controls related to data security: cryptographically unsafe algorithms must not be used, keys must be at least 256 bits long, credentials must not be hard-coded into program text, HTTPS must always be used instead of HTTP, and cloud data stores must not be world-readable. We evaluated our approach in three ways. (1) We applied our tools to over 5 million lines of open-source software. (2) We compared our tools to other publicly-available tools for detecting misuses of encryption on a previously-published benchmark, finding that only ours are suitable for continuous compliance. (3) We deployed a continuous compliance process at AWS, a large cloud-services company: we integrated verification tools into the compliance process (including auditors accepting their output as evidence) and ran them on over 68 million lines of code. Our tools and the data for the former two evaluations are publicly available.

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.

We present ClearTrack, a system that tracks meta-data for each primitive value in Java programs to detect and nullify a range of vulnerabilities such as integer overflow/underflow and SQL/command injection vulnerabilities. Contributions include new techniques for eliminating false positives associated with benign integer overflows and underflows, new metadata-aware techniques for detecting and nullifying SQL/command command injection attacks, and results from an independent evaluation team. These results show that 1) ClearTrack operates successfully on Java programs comprising hundreds of thousands of lines of code (including instrumented jar files and Java system libraries, the majority of the applications comprise over 3 million lines of code), 2) because of computations such as cryptography and hash table calculations, these applications perform millions of benign integer overflows and underflows, and 3) ClearTrack successfully detects and nullifies all tested integer overflow and underflow and SQL/command injection vulnerabilities in the benchmark applications.

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.

We consider different models of malicious multiple access channels, especially for binary adder channel and for A-channel, and show how they can be used for the reformulation of digital fingerprinting coding problems. In particular, we propose a new model of multimedia fingerprinting coding. In the new model, not only zeroes and plus/minus ones but arbitrary coefficients of linear combinations of noise-like signals for forming watermarks (digital fingerprints) can be used. This modification allows dramatically increase the possible number of users with the property that if t or less malicious users create a forge digital fingerprint then a dealer of the system can find all of them with zero-error probability. We show how arisen problems are related to the compressed sensing problem.

Network coding (NC) can significantly increase network performance and make lossy networks more reliable. Since the middle nodes modify the packets during their path to destination, integrity of the original packets cannot be checked using classical methods (MACs, Signatures, etc). Though, pollution attacks are the most common threat to network coded systems, where an infected node can inject the data flow of a network with a number of false packets and ban the receiver from properly decoding the packets. A lot of work in the security of NC in resisting pollution attacks has been investigated in recent years, majority have the same security parameter 1/q. A Homomorphic MAC scheme is presented earlier to resist pollution attacks with a security level 1/qˆl, In this paper, we will show that the mentioned scheme is subject to known-plaintext attacks. This is due to that part of the key can be revealed in an initial process. Also, the whole key could be revealed if the key is used more than once. Then, a modification to the mentioned scheme is proposed to overcome this issue. Besides, the MAC length is adjustable according to the required security level and not variable according to the vector's length which will accordingly increase the performance and efficiency of the scheme.

In this work, novel metaheuristic algorithms are proposed to address the network coding (NC)-based routing and spectrum allocation (RSA) problem in elastic optical networks, aiming to increase the level of security against eavesdropping attacks for the network's confidential connections. A modified simulated annealing, a genetic algorithm, as well as a combination of the two techniques are examined in terms of confidentiality and spectrum utilization. Performance results demonstrate that using metaheuristic techniques can improve the performance of NC-based RSA algorithms and thus can be utilized in real-world network scenarios.