Biblio

Due to the low power of electromagnetic radiation (EMR), EM convert channel has been widely considered as a short-range attack that can be easily mitigated by shielding. This paper overturns this common belief by demonstrating how covert EM signals leaked from typical laptops, desktops and servers are decoded from hundreds of meters away, or penetrate aggressive shield previously considered as sufficient to ensure emission security. We achieve this by designing EMLoRa – a super resilient EM covert channel that exploits memory as a LoRa-like radio. EMLoRa represents the first attempt of designing an EM covert channel using state-of-the-art spread spectrum technology. It tackles a set of unique challenges, such as handling complex spectral characteristics of EMR, tolerating signal distortions caused by CPU contention, and preventing adversarial detectors from demodulating covert signals. Experiment results show that EMLoRa boosts communication range by 20x and improves attenuation resilience by up to 53 dB when compared with prior EM covert channels at the same bit rate. By achieving this, EMLoRa allows an attacker to circumvent security perimeter, breach Faraday cage, and localize air-gapped devices in a wide area using just a small number of inexpensive sensors. To countermeasure EMLoRa, we further explore the feasibility of uncovering EMLoRa's signal using energy- and CNN-based detectors. Experiments show that both detectors suffer limited range, allowing EMLoRa to gain a significant range advantage. Our results call for further research on the countermeasure against spread spectrum-based EM covert channels.

The introduction of connected and autonomous vehicles enables new possibilities in vehicle routing: Knowing the origin and destination of each vehicle in the network can allow for coordinated real-time routing of the vehicles to optimize network performance. However, this relies on individual vehicles being “altruistic,” i.e., willing to accept alternative less-preferred routes. We conduct a study to compare different levels of agent altruism in decentralized vehicles coordination and the effect on the network-level traffic performance. This work introduces novel load-balancing scenarios of traffic flow in real-world cities for varied levels of agent altruism. We show evidence that the new decentralized optimization router is more effective with networks of high load.

Currently, the technology of wireless mesh networks is actively developing. In 2021, Gartner included mesh network technologies and the tasks to ensure their security in the TOP global trends. A large number of scientific works focus on the research and modeling the traffic transmission in such networks. At the same time, they often bring up the “bottle neck” problem, characteristic of individual mesh network nodes. To address the issue, the authors of the article propose using the data caching mechanism and placing the cache data straight on the routers. The mathematical model presented in the article allows building a route with the highest access speed to the requested content by the modified Dijkstra algorithm. Besides, if the mesh network cache lacks the required content, the routers with the Internet access are applied. Practically, the considered method of creating routes to the content, which has already been requested by the users in the mesh network, allows for the optimal efficient use of the router bandwidth capacity distribution and reduces the latency period.

In this paper, internet is among the basic necessities of life. Internet has changed each and everybody's lives. So confidentiality of messages is very important over the internet. Steganography is the science of sending secret messages between the sender and intended receiver. It is such a technique that makes the exchange of covert messages possible. Each time a carrier is to be used for achieving steganography. The carrier plays a major role in establishing covert communication channel. This survey paper introduces steganography and its carriers. This paper concentrates on network protocols to be used as a carrier of steganograms. There are a number of protocols available to do so in the networks. Network steganography describes various methods used for transmitting data over a network without it being detected. Most of the methods proposed for hiding data in a network do not offer an additional protection to the covert data as it is sent as plain text. This paper presents a framework that offers the protection to the covert data by encrypting it and compresses it for gain in efficiency.

This article analyzes the possibilities of using cognitive approaches in forming expert assessments for solving information security problems. The experts use the contextual approach by A.Yu. Khrennikov’s as a basic model for the mathematical description of the quantum decision-making method. In the cognitive view, expert assessments are proposed to be considered as conditional probabilities with regard to the fulfillment of a set of certain conditions. However, the conditions in this approach are contextual, but not events like in Boolean algebra.

3D reconstruction has piqued the interest of many disciplines, and many researchers have spent the last decade striving to improve on latest automated three-dimensional reconstruction systems. Three Dimensional models can be utilized to tackle a wide range of visualization problems as well as other activities. In this paper, we have implemented a method of Digital Surface Map (DSM) generation from Aerial images using Conditional Generative Adversarial Networks (c-GAN). We have used Seg-net architecture of Convolutional Neural Network (CNN) to segment the aerial images and then the U-net generator of c-GAN generates final DSM. The dataset we used is ISPRS Potsdam-Vaihingen dataset. We also review different stages if 3D reconstruction and how Deep learning is now being widely used to enhance the process of 3D data generation. We provide binary cross entropy loss function graph to demonstrate stability of GAN and CNN. The purpose of our approach is to solve problem of DSM generation using Deep learning techniques. We put forth our method against other latest methods of DSM generation such as Semi-global Matching (SGM) and infer the pros and cons of our approach. Finally, we suggest improvements in our methods that might be useful in increasing the accuracy.

In order to solve the problem of difficult verification of query results in searchable encryption, we used the idea of Shamir-secret sharing, combined with game theory, to construct a randomly verifiable multi-cloud server searchable encryption scheme to achieve the correctness of the query results in the cloud storage environment verify. Firstly, we using the Shamir-secret sharing technology, the encrypted data is stored on each independent server to construct a multi-cloud server model to realize the secure distributed storage and efficient query of data. Secondly, combined with game theory, a game tree of query server and verification server is constructed to ensure honesty while being efficient, and solve the problem of difficulty in returning search results to verify under the multi-cloud server model. Finally, security analysis and experimental analysis show that this solution effectively protects data privacy while significantly reducing retrieval time.

Traditionally, Industrial Control Systems (ICS) have been operated as air-gapped networks, without a necessity to connect directly to the Internet. With the introduction of the Internet of Things (IoT) paradigm, along with the cloud computing shift in traditional IT environments, ICS systems went through an adaptation period in the recent years, as the Industrial Internet of Things (IIoT) became popular. ICS systems, also called Cyber-Physical-Systems (CPS), operate on physical devices (i.e., actuators, sensors) at the lowest layer. An anomaly that effect this layer, could potentially result in physical damage. Due to the new attack surfaces that came about with IIoT movement, precise, accurate, and prompt intrusion/anomaly detection is becoming even more crucial in ICS. This paper proposes a novel method for real-time intrusion/anomaly detection based on a cyber-physical system network traffic. To evaluate the proposed anomaly detection method's efficiency, we run our implementation against a network trace taken from a Secure Water Treatment Testbed (SWAT) of iTrust Laboratory at Singapore.

This paper proposes a configurable architecture of butterfly unit (BU) supporting number theoretic transform (NTT) and inverse NTT (INTT) accelerators in the ring learning with error based homomorphic encryption. The proposed architecture is fully pipelined and carefully optimized the critical path delay. To compare with related works, several BU designs of different bit-size specific primes are synthesized and successfully placed-and-routed on the Xilinx Zynq UltraScale+ ZCU102 FPGA platform. Implementation results show that the proposed BU designs achieve 3× acceleration with more efficient resource utilization compared with previous works. Thus, the proposed BU architecture is worthwhile to develop NTTINTT accelerators in advanced homomorphic encryption systems.

In this work a data provenance system for grid-oriented applications is presented. The proposed Keyless Infrastructure Security Solution (KISS) provides mechanisms to store and maintain digital data fingerprints that can later be used to validate and assert data provenance using a time-based, hash tree mechanism. The developed solution has been designed to satisfy the stringent requirements of the modern power grid including execution time and storage necessities. Its applicability has been tested using a lab-scale, proof-of-concept deployment that secures an energy management system against the attack sequence observed on the 2016 Ukrainian power grid cyberattack. The results demonstrate a strong potential for enabling data provenance in a wide array of applications, including speed-sensitive applications such as those found in control room environments.

Dynamic searchable symmetric encryption (DSSE) that enables a client to perform searches and updates on encrypted data has been intensively studied in cloud computing. Recently, forward privacy and backward privacy has engaged significant attention to protect DSSE from the leakage of updates. However, the research in this field almost focused on keyword-level updates. That is, the client needs to know the keywords of the documents in advance. In this paper, we proposed a document-level update scheme, DBP, which supports immediate deletion while guaranteeing forward privacy and backward privacy. Compared with existing forward and backward private DSSE schemes, our DBP scheme has the following merits: 1) Practicality. It achieves deletion based on document identifiers rather than document/keyword pairs; 2) Efficiency. It utilizes only lightweight primitives to realize backward privacy while supporting immediate deletion. Experimental evaluation on two real datasets demonstrates the practical efficiency of our scheme.

While a huge number of IoT devices are connecting to the cyber physical systems, the demand for security of these devices are increasing. Due to the demand, world-wide competition for lightweight cryptography oriented towards small devices have been held. Although tamper resistance against illegal attacks were evaluated in the competition, there is no evaluation for embedded malicious circuits such as hardware Trojan.To achieve security evaluation for embedded malicious circuits, this study proposes an implementation method of hardware Trojan for Elephant which is one of the finalists in the competition. And also, the implementation overhead of hardware Trojans and the security risk of hardware Trojan are evaluated.

Data security and privacy have become an important problem while big data systems are growing dramatically fast in various application fields. Paillier additive homomorphic cryptosystem is widely used in information security fields such as big data security, communication security, cloud computing security, and artificial intelligence security. However, how to improve its computational performance is one of the most critical problems in practice. In this paper, we propose two modifications to improve the performance of the Paillier cryptosystem. Firstly, we introduce a key generation method to generate the private key with low Hamming weight, and this can be used to accelerate the decryption computation of the Paillier cryptosystem. Secondly, we propose an acceleration method based on Hensel lifting in the Paillier cryptosystem. This method can obtain a faster and improved decryption process by showing the mathematical analysis of the decryption algorithm.

This paper studies the physical layer security of four reactive relay selection methods (optimum relay selection, opportunist relay selection enhancement, suboptimal relay selection enhancement and partial relay selection enhancement) in a cooperative cognitive radio network including one pair of primary users, one eavesdropper, multiple relays and secondary users with perfect and imperfect channel state information (CSI) at receivers. In addition, we consider existing a direct link from a secondary source (S) to secondary destination receivers (D) and eavesdroppers (E). The secrecy outage probability, outage probability, intercept probability and reliability are calculated to verify the four relay selection methods with the fading channels by using Monte Carlo simulation. The results show that the loss of secrecy outage probability when remaining direct links from S to D and S to E. Additionally, the results also show that the trade-off between secrecy outage probability and the intercept probability and the optimum relay selection method outperforms other methods.

Since the personal data protection law is on the way of many countries, how to use data mining method to secure sensitive information has become a challenge for enterprises. To make sure every employee follows company's data protection strategy, it may take lots of time and cost to seek and scan thousands of folders and files in user equipment, ensuring that the file contents meet IT security policies. Hence, this paper proposed a lightweight, pattern-based detection system, PIDS, which is expecting to enable an affordable data leakage prevention with essential cost and high efficiency in small business enterprise. For verification and evaluation, PIDS has been deployed on more than 100,000 PCs of collaboration enterprises, and the feedback shows that the system is able to approach its original design functionality for finding violated or sensitive contents efficiently.

In this paper, we propose a zero-distortion blind reversible robust scheme for watermarking relational databases called PKMark. Data owner can declare the copyright of the databases or pursue the infringement by extracting the water-mark information embedded in the database. PKMark is mainly based on the primary key attribute of the tuple. So it does not depend on the type of the attribute, and can provide high-precision numerical attributes. PKMark uses RSA encryption on the watermark before embedding the watermark to ensure the security of the watermark information. Then we use RSA to sign the watermark cipher text so that the owner can verify the ownership of the watermark without disclosing the watermark. The watermark embedding and extraction are based on the hash value of the primary key, so the scheme has blindness and reversibility. In other words, the user can obtain the watermark information or restore the original database without comparing it to the original database. Our scheme also has almost excellent robustness against addition attacks, deletion attacks and alteration attacks. In addition, PKMark is resistant to additive attacks, allowing different users to embed multiple watermarks without interfering with each other, and it can indicate the sequence of watermark embedding so as to indicate the original copyright owner of the database. This watermarking scheme also allows data owners to detect whether the data has been tampered with.

With the advancement of network physical social system (npss), a large amount of data privacy has become the targets of hacker attacks. Due to the complex and changeable attack methods of hackers, network security threats are becoming increasingly severe. As an important type of active defense, honeypots use the npss as a carrier to ensure the security of npss. However, traditional honeynet structures are relatively fixed, and it is difficult to trap hackers in a targeted manner. To bridge this gap, this paper proposes a recommendation method for LSTM prediction trap components based on attention mechanism. Its characteristic lies in the ability to predict hackers' attack interest, which increases the active trapping ability of honeynets. The experimental results show that the proposed prediction method can quickly and effectively predict the attacking behavior of hackers and promptly provide the trapping components that hackers are interested in.

With the development of cloud computing, remote attestation of virtual machines has received extensive attention. However, the current schemes mainly concentrate on the single prover, and the attestation of a large-scale virtualization environment will cause TPM bottleneck and network congestion, resulting in low efficiency of attestation. This paper proposes CloudTA, an extensible remote attestation architecture. CloudTA groups all virtual machines on each cloud server and introduces an integrity measurement group (IMG) to measure virtual machines and generate trusted evidence by a group. Subsequently, the cloud server reports the physical platform and VM group's trusted evidence for group verification, reducing latency and improving efficiency. Besides, CloudTA designs a hybrid high concurrency communication framework for supporting remote attestation of large-scale virtual machines by combining active requests and periodic reports. The evaluation results suggest that CloudTA has good efficiency and scalability and can support remote attestation of ten thousand virtual machines.

As the China government already putting forward the strategic objectives to peak carbon dioxide emissions before 2030 and achieve carbon neutrality before 2060, social consensus of green low carbon has promoted the development of integrated energy services. As an emerging format, integrated energy services break the trade and technical barriers between different varieties of energy. As a carrier of integrated energy services, integrated energy service companies still have many problems in their own optimized operation. This paper studies the risk mechanism of energy chain considering the risk preference of energy service companies, and analyses the correlation between the risk preference of energy service companies and the risk of energy chain. Based on set pair theory and interval number, the paper establishes an energy chain risk assessment model to overcome the shortcomings of traditional evaluation methods, which is able to characterize risk appetite and uncertainties. Finally, the results of simulation and tests verify the effectiveness of the proposed method of the novel.

Due to the large number of terminal nodes and wide deployment of power IOT, it is vulnerable to attacks such as physical hijacking, communication link theft and replay. In order to sense and measure the security risks and threats of massive power IOT terminals in real time, a security threat assessment for power IOT terminals based on knowledge graph was proposed. Firstly, the basic data, operation data and alarm threat data of power IOT terminal equipment are extracted and correlated, and the power IOT terminal based on knowledge graph is constructed. Then, the real-time monitoring data of the power IOT terminal is preprocessed. Based on the knowledge graph of the power IOT terminal, the safety analysis and operation analysis of the terminal are carried out, and the threat index of the power IOT terminal is perceived in real time. Finally, security operation and maintenance personnel make disposal decisions on the terminals according to the threat index of power IOT terminals to ensure the safe and stable operation of power IOT terminal nodes. The experimental results show that compared with the traditional IPS, the method can effectively detect the security threat of the power IOT terminal and reduce the alarm vulnerability rate.

Payment systems are a critical component of everyday life in our society. While in many situations payments are still slow, opaque, siloed, expensive or even fail, users expect them to be fast, transparent, cheap, reliable and global. Recent technologies such as distributed ledgers create opportunities for near-real-time, cheaper and more transparent payments. However, in order to achieve a global payment system, payments should be possible not only within one ledger, but also across different ledgers and geographies.In this paper we propose Secure Payments with Overlay Networks (SPON), a service that enables global payments across multiple ledgers by combining the transaction exchange provided by the Interledger protocol with an intrusion-tolerant overlay of relay nodes to achieve (1) improved payment latency, (2) fault-tolerance to benign failures such as node failures and network partitions, and (3) resilience to BGP hijacking attacks. We discuss the design goals and present an implementation based on the Interledger protocol and Spines overlay network. We analyze the resilience of SPON and demonstrate through experimental evaluation that it is able to improve payment latency, recover from path outages, withstand network partition attacks, and disseminate payments fairly across multiple ledgers. We also show how SPON can be deployed to make the communication between different ledgers resilient to BGP hijacking attacks.

Physical protection system (PPS) is developed to protect the assets or facilities against threats. A systematic analysis of the capabilities and intentions of potential threat capabilities is needed resulting in a so-called Design Basis Threat (DBT) document. A proper development of DBT is important to identify the system requirements that are required for adequately protecting a system and to optimize the resources needed for the PPS. In this paper we propose a model-based systems engineering approach for developing a DBT based on feature models. Based on a domain analysis process, we provide a metamodel that defines the key concepts needed for developing DBT. Subsequently, a reusable family feature model for PPS is provided that includes the common and variant properties of the PPS concepts detection, deterrence and response. The configuration processes are modeled to select and analyze the required features for implementing the threat scenarios. Finally, we discuss the integration of the DBT with the PPS design process.

X-ray radiography has been used to diagnose a wide variety of experiments at the Z facility including inertial confinement fusion capsule implosions, the growth of the magneto-Rayleigh-Taylor instability in solid liners, and the development of helical structures in axially magnetized liner implosions. In these experiments, the Z Beamlet laser (1 kJ, 1 ns) was used to generate the x-ray source. An alternate x-ray source is desirable in experiments where the Z Beamlet laser is used for another purpose (e.g., preheating the fuel in magnetized liner inertial fusion experiments) or when multiple radiographic lines of sight are necessary.

As Internet of Things(IOT) devices become intelli-gent, more powerful computing capability is required. Multi-core processors are widely used in IoT devices because they provide more powerful computing capability while ensuring low power consumption. Therefore, it requires the operating system on IoT devices to support and optimize the scheduling algorithm for multi-core processors. Nowadays, microkernel-based operating systems, such as QNX Neutrino RTOS and HUAWEI Harmony OS, are widely used in IoT devices because of their real-time and security feature. However, research on multi-core scheduling for microkernel operating systems is relatively limited, especially for load balancing mechanisms. Related research is still mainly focused on the traditional monolithic operating systems, such as Linux. Therefore, this paper proposes a low-latency, high- performance, and high real-time centralized global dynamic multi-core load balancing method for the microkernel operating system. It has been implemented and tested on our own microkernel operating system named Mginkgo. The test results show that when there is load imbalance in the system, load balancing can be performed automatically so that all processors in the system can try to achieve the maximum throughput and resource utilization. And the latency brought by load balancing to the system is very low, about 4882 cycles (about 6.164us) triggered by new task creation and about 6596 cycles (about 8.328us) triggered by timing. In addition, we also tested the improvement of system throughput and CPU utilization. The results show that load balancing can improve the CPU utilization by 20% under the preset case, while the CPU utilization occupied by load balancing is negligibly low, about 0.0082%.

This paper proposes an efficient face template protection system based on homomorphic encryption. By developing a message packing method suitable for the calculation of the squared Euclidean distance, the proposed system computes the squared Euclidean distance between facial features by a single homomorphic multiplication. Our experimental results show the transaction time of the proposed system is about 14 times faster than that of the existing face template protection system based on homomorphic encryption presented in BIOSIG2020.