Practical Security Research Articles

IDS-GAN: Stepping up Intrusion Detection Method using GAN Algorithm

Many computer network threats cause the security aspect to become the most critical problem. The intrusion detection system is a widely used practical security tool to prevent malicious traffic from penetrating networks and systems. To solve the issue, we construct a novel algorithm using Generative Adversarial Networks (GAN) to address the IDS security problem. In this paper, we propose an intrusion detection model using GAN by analyzing the extracted features of the network. To build our detection model, we collect the dataset, conduct pre-processing, train our model with several hyper-parameters to get the best accuracy, then test the model using the new data. Based on experimental results, the proposed model can produce a 0.00539 error rate and indicate a more accurate model to detect anomalies in the network traffic.

Proof-of-Principle Demonstration of Fully Passive Quantum Key Distribution.

Quantum key distribution (QKD) offers information-theoretic security based on the fundamental laws of physics. However, device imperfections, such as those in active modulators, may introduce side-channel leakage, thus compromising practical security. Attempts to remove active modulation, including passive decoy intensity preparation and polarization encoding, have faced theoretical constraints and inadequate security verification, thus hindering the achievement of a fully passive QKD scheme. Recent research [W. Wang et al., Phys. Rev. Lett. 130, 220801 (2023).PRLTAO0031-900710.1103/PhysRevLett.130.220801; 2V. Zapatero et al., Quantum Sci. Technol. 8, 025014 (2023).2058-956510.1088/2058-9565/acbc46] has systematically analyzed the security of a fully passive modulation protocol. Based on this, we utilize the gain-switching technique in combination with the postselection scheme and perform a proof-of-principle demonstration of a fully passive quantum key distribution with polarization encoding at channel losses of 7.2dB, 11.6dB, and 16.7dB. Our work demonstrates the feasibility of active-modulation-free QKD in polarization-encoded systems.

Measurement device-independent quantum key distribution with vector vortex modes under diverse weather conditions

Most quantum key distribution schemes exploiting orbital angular momentum-carrying optical beams are based on conventional set-ups, opening up the possibility of detector side-channel attacks. These optical beams also suffer from spatial aberrations due to atmospheric turbulence and unfavorable weather conditions. Consequently, we introduce a measurement device-independent quantum key distribution implemented with vector vortex modes. We study the transmission of vector vortex and scalar beams through a turbulent atmospheric link under diverse weather conditions such as rain or haze. We demonstrate that a maximum secure key transmission distance of 178 km can be achieved under clear conditions by utilizing the vector vortex beams, which have been mainly ignored in the literature. When raindrops have a diameter of 6 mm and fog particles have a radius of 0.5 upmum, the signals can reach 152 km and 160 km, respectively. Since these distances are comparable, this work sheds light into the feasibility of implementing measurement device-independent quantum key distribution using vector vortex modes under diverse weather conditions. Most significantly, this opens the door to practical secure quantum communications.

Hardware Security Risks and Threat Analyses in Advanced Manufacturing Industry

The advanced manufacturing industry (AMI) faces many unique challenges from the cyber-physical domain. Security threats are originated from two integral parts: software and hardware. Over the past decade, software security has been addressed extensively, but hardware security has not received enough attention. This work analyzes the security vulnerabilities of typical electronic devices deployed to AMI and proposes three attack models for sensing nodes, local storage and processing edge devices, and wired/wireless communication interfaces, respectively. Practical security attacks on hardware are demonstrated in this work to inspire the development of feasible countermeasures against hardware Trojans, fault injection attacks, and external signal interference. Moreover, this work highlights the unique security challenges posed by advanced manufacturing applications. To mitigate those security attacks in AMI, this work suggests guidelines for the defense method design that can effectively protect the hardware in AMI.

Unmanned Aerial Vehicles: An Overview

Unmanned aerial vehicles, commonly referred to as drones, as the name suggests can be operated without human presence on board. UAV commonalities are growing rapidly in today’s world as their practicality in solving real-world issues is beyond beneficial in terms of efficiency and sustainability. This paper will go into depth about the practical aspects, applications, open challenges, security threats and future scope in drone technology.

Considerations, Advances, and Challenges Associated with the Use of Specific Emitter Identification in the Security of Internet of Things Deployments: A Survey

Initially introduced almost thirty years ago for the express purpose of providing electronic warfare systems the capabilities to detect, characterize, and identify radar emitters, Specific Emitter Identification (SEI) has recently received a lot of attention within the research community as a physical layer technique for securing Internet of Things (IoT) deployments. This attention is largely due to SEI’s demonstrated success in passively and uniquely identifying wireless emitters using traditional machine learning and the success of Deep Learning (DL) within the natural language processing and computer vision areas. SEI exploits distinct and unintentional features present within an emitter’s transmitted signals. These distinctive and unintentional features are attributed to slight manufacturing and assembly variations within and between the components, sub-systems, and systems comprising an emitter’s Radio Frequency (RF) front end. Although sufficient to facilitate SEI, these features do not hinder normal operations such as detection, channel estimation, timing, and demodulation. However, despite the plethora of SEI publications, it has remained largely a focus of academic endeavors, primarily focusing on proof-of-concept demonstration and little to no use in operational networks for various reasons. The focus of this survey is a review of SEI publications from the perspective of its use as a practical, effective, and usable IoT security mechanism; thus, we use IoT requirements and constraints (e.g., wireless standard, nature of their deployment) as a lens through which each reviewed paper is analyzed. Previous surveys have not taken such an approach and have only used IoT as motivation, a setting, or a context. In this survey, we consider operating conditions, SEI threats, SEI at scale, publicly available data sets, and SEI considerations that are dictated by the fact that it is to be employed by IoT devices or IoT infrastructure.

Moving Target Defense for the cloud/edge Telco environments

The Internet of Things (IoT) paradigm has been one of the main contributors, in recent years, to the growth in the number of connected equipment. This fact has predominantly contributed to IoT being constrained by the 5th Generation Mobile Network (5G) progress and the promises this technology brings. However, this can be a double-edged sword. On the one hand, it will benefit from those progresses, but on the other, it will also be impacted by any security risk associated with 5G. One of the more serious security problems associated with it is the new wave of virtualization and softwarization of networks and analogous appliances, brought to light by paradigms such as Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC). Considering these predicaments, we propose a state-of-the-art Moving Target Defense (MTD) approach that defends Cloud-based Network Functions (CNFs) launched within MEC and NFV environments. Furthermore, our mechanism follows the famous Everything as a Service (XaaS) ideology, allowing any CNF provider to use this protection system, working agonistically. In the end, we created a Proof of Concept (PoC) of our proposed methodology, which we then used to conduct an extensive practical security analysis against the multiple phases of the Intrusion Kill Chain. Our final results have proven that our MTD as a Service (MTDaaS) approach can effectively delay and, in some cases, stop an attacker from achieving its objectives when trying to attack a CNF, even if the related vulnerability is a zero-day.

A lightweight-X-authentication protocol over automotive gateway

One of the most dynamic industries nowadays is the automotive industry. It has dramatically changed from safe, comfortable, and equipped with many entertainment systems to connected autonomous vehicles during the last decade. Although the “Connected Autonomous vehicles” trend dramatically influences the economy, it increases automation and connectivity. Consequently, vehicles can be considered an Internet of Things (IoT) network. Accordingly, the cyberattack surface is more significant, with many vulnerabilities. Considering the fundamental security weakness of automotive onboard networks, besides their superimposing, we firmly believe that a unified security solution is required to resolve the current automotive security problem. This paper proposes an automotive security solution called Generic Automotive Security Suit (GASS). (GASS) is designed to be a practical security solution deployed in the complicated automotive environment. In addition, it defines a standard secure inter-communication between onboard networks. We also introduce the Lightweight Generic Authentication Protocol (LXAP) as the building block of the GASS. We address different security scenarios which face the industry nowadays. The new proposed system does not affect the complexity level of the automotive network. In addition, our simulation results have shown that the LXAP enables security preservation over vehicles’ complex networks.

Intensity Tomography Method for Secure and High-Performance Quantum Key Distribution

Decoy-state method has greatly filled in loopholes from multi-photon pulses in the field of quantum key distribution (QKD) and it has been employed in almost all practical QKD systems in recent years. However, security and performance of the practical decoy-state QKDs are still limited by inaccurate modulations such as random intensity fluctuation and correlated intensity fluctuation. In this work, an intensity tomography method is proposed as a countermeasure against these fluctuations. It has an ability to tightly bound the unknown density matrices of photon-number state and accurately calibrate the fluctuations by measuring the decoy states with a local single-photon detector. We verified the tomography method by experiments and simulations. The results indicated that our method can efficiently mitigate the flaws from these fluctuations to improve the protocol performance and practical security. Noting that these fluctuations are inevitable in practical QKDs, our method would pave the avenue for QKD's practical applications.

Membership Mappings for Practical Secure Distributed Deep Learning

<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Problem</u> : We consider the problem of privacy-preserving distributed deep learning where data privacy is protected by fully homomorphic encryption. <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aim</u> : The aim is to develop a method for practical and scalable distributed deep learning with fully homomorphic encrypted data. The method must address the issue arising from the large computational cost associated with fully homomorphic encrypted data to offer a practical and scalable solution. <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods</u> : An approach that leverages fuzzy-based membership-mappings for data representation learning is considered for distributed deep learning with fully homomorphic encrypted data. The method introduces globally convergent and robust variational membership-mappings to build local deep models. The local models are combined in a robust and flexible manner by means of fuzzy attributes to build a global model such that the global model can be homomorphically evaluated in an efficient manner. <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results</u> : The membership-mappings based privacy-preserving distributed deep learning method is accurate, practical, and scalable. This is verified through numerous experiments which include demonstrations using MNIST and Freiburg Groceries datasets, and a biomedical application related to the detection of mental stress on individuals. <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion</u> : The study develops globally convergent and robust variational membership-mappings for their application to accurate, practical, and scalable privacy-preserving distributed deep learning.

Practical security analysis of a continuous-variable source-independent quantum random number generator based on heterodyne detection.

Heterodyne-based continuous-variable source-independent quantum random number generator (CV-SI-QRNG) can produce true random numbers without any assumptions on source. However, practical implementations always contain imperfections, which will greatly influence the extractable randomness and even open loopholes for eavesdroppers to steal information about the final output. In this work, based on the theoretical model, we systematically analyzed the effects of imperfect implementations on the practical security of heterodyne-based CV-SI-QRNG. The influences of local oscillator (LO) fluctuation under imbalanced heterodyne detection are first analyzed. The simulation results show that the lower bound of extractable randomness will be overestimated without considering the influence of LO fluctuation, which will threaten the practical security of CV-SI-QRNG system. Moreover, we analyze the effects of the degree of imbalance and the magnitude of LO fluctuation on evaluating the extractable randomness. Finally, we investigate the impact of an imperfect phase modulator on the practical security of CV-SI-QRNG and find it will reduce the extractable randomness. Our analysis reveals that one should carefully consider the imperfections in the actual implementations of CV-SI-QRNGs.

Data security in smart devices: Advancement, constraints and future recommendations

AbstractNetwork security protocols are implemented to address network security challenges. Computer networks and applications have advanced and developed significantly in recent years, but consumers ‘excitement for network technology and high‐tech devices has been dampened by continual exposure to data security vulnerabilities. As of now, some individuals refuse to use smart devices due to concerns about the authenticity, confidentiality and integrity of data security leaks. This not only prompts Internet service providers to follow market protection mechanisms but also requires software developers to apply appropriate security protocols to protect computer network security. These applications’ dependability and integrity are dependent not just on the effectiveness of cryptographic algorithms, but also on key management protocols. Understanding network security protocols and implementing high‐quality standards to govern the transmission of data in the network are critical components of guaranteeing network security. The article explores data security, primarily at the application layer, various attack methods for different network security protocols and highlights the potential security implications. The study also looks at the corresponding, practical security measures and future research prospects for certain kinds of attacks. Finally, some technical challenges that remain unsolved at the time of writing are summarised, and future trends in cybersecurity are discussed.

Measurement-Device-Independent Quantum Key Distribution Based on Decoherence-Free Subspaces with Logical Bell State Analyzer.

Measurement-device-independent quantum key distribution (MDI-QKD) enables two legitimate users to generate shared information-theoretic secure keys with immunity to all detector side attacks. However, the original proposal using polarization encoding is sensitive to polarization rotations stemming from birefringence in fibers or misalignment. To overcome this problem, here we propose a robust QKD protocol without detector vulnerabilities based on decoherence-free subspaces using polarization-entangled photon pairs. A logical Bell state analyzer is designed specifically for such encoding. The protocol exploits common parametric down-conversion sources, for which we develop a MDI-decoy-state method, and requires neither complex measurements nor a shared reference frame. We have analyzed the practical security in detail and presented a numerical simulation under various parameter regimes, showing the feasibility of the logical Bell state analyzer along with the potential that double communication distance can be achieved without a shared reference frame.

Determination of maximum additional load for EV charging station considering practical security limits

Nowadays, the allocation of electric vehicle charging stations (EVCS) for the charging of electric vehicles (EVs) is essential, as random allocation may cause significant energy loss for the radial distribution system (RDS). Uncoordinated charging of EVs dominates the load profile of the RDS. Although a single EV has a negligible impact on the system, the combined effect of charging an EV cluster may violate the voltage security constraints of the RDS. Therefore, to avoid such circumstances, the loadability limit of each node of the RDS must be determined. Previously, conventional analytical methods were used to calculate the maximum loadability of the RDS node, where voltage magnitudes become less than the critical voltage limit. However, these approaches are not acceptable for the power industry, as it may push the system towards a blackout. Therefore, the appropriate capacity of the EVCS can only be determined if the voltage security limit of each node of the RDS follows its critical limit. Hence, in this paper, a modified forward-backward sweep (MFBS) algorithm is formulated to find the maximum additional load (MAL) of each node of the RDS considering non-unity power factor during the EV charging process. This algorithm may help the EVCS to determine its capacity or optimal number of charging ports directly during installation at an optimal location of RDS. For validation, the 33-bus and 69-bus RDSs have been used, and it can be seen that the EVCSs have been successfully installed in the optimal location of the test systems mentioned above without violating the voltage security limit of each node of the RDS.

A Secure Scheme for Network Coding with Deep Learning in Industrial Internet of Things

This paper proposes a network coding scheme that utilizes neural network coding and steganography technique for Industrial Internet of Things (IIoT). This proposal gives an efficient error-correcting and secure network coding strategy simultaneously for general non-multicast network for the first time. The trivial concatenation of the above two schemes will not work, and we adopt a smart skill to fuse the neural network coding scheme and the steganography scheme seamlessly. The whole neural network model includes the network coding part and the steganography part. The first skill is making the network coding part converge in advance, and then we make the steganography part converged. The second skill is delicately designing the neural network structure and parameters to make the whole model work efficiently. The new proposal can correct more errors and confront more eavesdroppers than state of the art method in network coding. Besides, the transmission information rate is three, which is bigger than one, which is the most information rate of traditional channel code based on Shannon theory. We use a steganography neural network model to encode three images into one image and at last, the three images can be recovered simultaneously. The new scheme will give a practical error-correcting and secure network coding strategy, and then decrease the energy consume in the communication, which will benefit IIoT.

Energy Security: New Threats and Solutions

The article presents the conceptual features of energy security management under a radically changed context, increasing crisis phenomena, and threats of various natures. The authors substantiate the claim that energy security is a complex category, which expresses the ability of the fuel and energy complex of the region to supply the required amount and range of energy resources to the domestic market at stable and reasonable prices; to promptly mitigate unexpected fluctuations in demand for fuel and energy resources; and to ensure uninterrupted energy supply and energy carrier parameters in real time. Based on an analysis of scientific publications and practical energy security models, the authors developed theoretical provisions, methodological principles, and management tools for energy security that meet modern requirements. In particular, the authors developed the terminological apparatus and identified the types and forms of modern energy threats and risks. The authors analyzed the impact of structural shifts in the electric power industry on the cost of electricity. The authors proposed a set of measures to neutralize negative scenarios in the field of energy security, which had appeared because of geopolitical factors, structural changes in the economy, and high volatility in energy prices. In addition, the authors considered the impact of the transition to low-carbon energy production on energy security, and developed the organizational and technical concept of the energy transition, which aimed to provide energy systems and individual energy facilities with properties of self-protection from emerging threats. The results of the study are of practical interest in the development of regional energy policy, plans, and specific actions that aim to ensure energy security in a turbulent global environment.

Research on Teaching Reform and Practice of Discrete Mathematics Course in Information Security Major

A proposed teaching reform idea is developed by examining the talent training approach of "Discrete Mathematics" within the information security curriculum and the unique features of the course's knowledge system. This new approach aligns with the characteristics of applied information security specialty and emphasizes the cultivation of practical information security talents. The proposed teaching reform idea involves integrating engineering ethics and MOOC teaching reform elements to expand the teaching approach and to develop a new course teaching mode.

Practical Labs for Teaching SDN Security

The rapid adoption of Software Defined Networking (SDN) in the industry has exposed certain security risks today some of which are unique to its paradigm. Security issues around the use-cases that expose these risks are fundamentally aligned with the networking and cybersecurity concepts that are taught at the graduate level in academia. In this paper, we present a number of lab activities on SDN security that are inspired from practical use-cases in SDN deployments. The goal of this effort is to help students give a shape to their thought process about the practical security implications of SDN deployments and gain valuable practical domain knowledge in securing an environment with such deployments.

Information Leakage Through Passive Timing Attacks on RSA Decryption System

A side channel attack is a means of security attacks that tries to restore secret information by analyzing side-information such as electromagnetic wave, heat, electric energy and running time that are unintentionally emitted from a computer system. The side channel attack that focuses on the running time of a cryptosystem is specifically named a “timing attack”. Timing attacks are relatively easy to carry out, and particularly threatening for tiny systems that are used in smart cards and IoT devices because the system is so simple that the processing time would be clearly observed from the outside of the card/device. The threat of timing attacks is especially serious when an attacker actively controls the input to a target program. Countermeasures are studied to deter such active attacks, but the attacker still has the chance to learn something about the concealed information by passively watching the running time of the target program. The risk of passive timing attacks can be measured by the mutual information between the concealed information and the running time. However, the computation of the mutual information is hardly possible except for toy examples. This study focuses on three algorithms for RSA decryption, derives formulas of the mutual information under several assumptions and approximations, and calculates the mutual information numerically for practical security parameters.

Antibacterial Halloysite‐Modified Chitosan/Polyvinylpyrrolidone Nanofibers for Ultrasensitive Self‐Powered Motion Monitoring

AbstractHigh flexibility, porosity, and antibacterial activity are extremely desired for wearable health monitoring, which is beneficial to simultaneously promote wearing comfort and safety. In this study, an antibacterial nanofibers‐based triboelectric generator (AN‐TENG) composed of the flexible chitosan/polyvinylpyrrolidone modified with halloysite nanotubes (CTS/PVP/HNTs) nanofibers and cube‐arrays structured Ecoflex film is proposed for simultaneously energy harvesting and self‐powered human motion monitoring. The open‐circuit voltage (280 V), short‐circuit current (3.98 μA), and transferred charge (51 nC) of the CTS/PVP/HNTs nanofibers TENG at the optimal compound concentration are increased by 90.8%, 86.92%, and 96.2%, respectively, compared to the CTS/PVP nanofibers one (size: 3 cm × 3 cm, mechanical force: 10 N @1 Hz), revealing good real‐time monitoring ability for human wrist, elbow, and finger motion. An antibacterial test is carried out to evaluate the antibacterial activity and the antibacterial rate of the nanofibers against Escherichia coli (ATCC 8739) and Staphylococcus aureus (ATCC 6538) based on the current national standard GB/T 31402–2015, indicating good antibacterial properties of the nanofibers. This research offers an ingenious strategy to establish an antibacterial nanofibers‐based TENG for self‐powered motion monitoring and energy harvesting and offers a new insight to improve the practical security of wearable electronic devices.