Internal Attacks Research Articles

A Parallel Privacy-Preserving k-Means Clustering Algorithm for Encrypted Databases in Cloud Computing

With the development of cloud computing, interest in database outsourcing has recently increased. However, when the database is outsourced, there is a problem in that the information of the data owner is exposed to internal and external attackers. Therefore, in this paper, we propose decimal-based encryption operation protocols that support privacy preservation. The proposed protocols improve the operational efficiency compared with binary-based encryption operation protocols by eliminating the need for repetitive operations based on bit length. In addition, we propose a privacy-preserving k-means clustering algorithm using decimal-based encryption operation protocols. The proposed k-means clustering algorithm utilizes efficient decimal-based protocols that enhance the efficiency of the encryption operations. To provide high query processing performance, we also propose a parallel k-means clustering algorithm that supports thread-based parallel processing by using a random value pool. Meanwhile, a security analysis of both the proposed k-means clustering algorithm and the proposed parallel algorithm was performed to prove their data protection, query protection, and access pattern protection capabilities. Through our performance analysis, the proposed k-means clustering algorithm shows about 10~13 times better performance compared with the existing algorithms.

Application of sulfate ion fixation in internal sulfate attack: The gel containing barium salt

Sulfate attack is one of the most important factors affecting the durability of concrete, and the corrosion sources of internal sulfate attack (ISA) mostly come from the raw materials of concrete. Adding corrosion resistance admixtures to concrete is one of the most effective ways to prevent internal sulfate attack. By wrapping barium salt with SA gel, the modified barium chloride admixture (MB) is obtained which can exist stably in concrete and release barium ions to form barium sulfate in a sulfate environment. The modified barium salt has little effect on the cement workability and hydration process and even can improve the mechanical properties slightly. When the MB content reaches 6% (MB-6), the strength improvement effect is most obvious. Compared with the P-0, the 7d compressive strength of MB-6 is increased by 9.14%, and the 28d compressive strength is increased by 5.77%. It can be seen by SEM that the amount and crystal size of ettringite in the cement mixed with admixtures are reduced. The corrosion resistance of the matrix can be further improved by using a modified barium chloride-silica fume composite admixture (MB-F). After 90 days of internal sulfate attack, the strength of the MB-F even improves slightly. When the modified barium chloride to silica fume ratio is 4:6, the cement-based material achieved the best resistance to internal sulfate attack. In addition to MB, which can make sulfate ions fixation and inhibit the formation of ettringite, silica fume can also consume calcium hydroxide and generate more C-S-H gel, which compacts the weak part of the matrix and greatly improves the resistance to internal sulfate attack of cement-based materials. It provides the possibility for the application of modified barium salt in external sulfate attack.

Circular semi-quantum secret sharing based on hybrid single particle and GHZ-type states

Semi-quantum secret sharing (SQSS) is a branch of quantum cryptography, with the advantage of fewer quantum resources. In this paper, we present a SQSS protocol based on hybrid single particle and GHZ-type states. Our protocol adopts circular transport structure to share secret information between classical and quantum parties, employing the entanglement correlation and uncertainty of GHZ-type states to guarantee the security of the transmission. Security analysis indicates that the proposed SQSS protocol enables resilience against various potential attacks such as measure-resend, entangle-measure, internal, and Trojan attacks. Furthermore, our protocol is easy to generalize to multi-party scenarios without increasing the complexity of state preparation. Compared with some typical SQSS protocols, our protocol has higher qubit efficiency without the need for entanglement swapping operations. The proposed SQSS protocol has the potential for broader applications in practical scenarios.

ACCESS CONTROL BY SIGNATURE-KEYS TO PROVIDE PRIVACY FOR CLOUD

Privacy of data in subjects of cloud computing or big data is one of the most principal issues. The privacy methods studied in previous research showed that privacy infringement for cloud computing or big data happened because multi risks on data by external or internal attackers. An important risk to take into consideration when speaking of the privacy of the stored transactions is represented by the transactions’ information which is not in the owner’s control. Such a case is represented by the cloud servers that are administered by cloud providers which cannot be wholly trusted by the users with sensitive, private data such as business plans or private information. A simple method for protecting data privacy is by applying certain privacy techniques onto transactions’ data, followed by the upload of the modified data into the cloud. In this paper, we are proposing a case study that is built on levels containing three models: cloud’s architecture, transaction’s manager and clients. Moreover, we consider that our case study is based on the premise of zero trust among the three models, therefore all the transactions take place with third-parties and the data movements are realized going through various levels of security.

On Cooperative Federated Defense to Secure Multi-Access Edge Computing

The research of cyber security for Multi-access Edge Computing (MEC) has not yet received a great interest. Specifically, the attack detection issue is considered as a major concern since the MEC network, which handles attractive information, is prone to several internal and external network attacks. Recently, the Federated Learning (FL) and Generative Adversarial Network (GAN) have been used to detect and prevent attacks from targeting the wireless mobile networks. In this article, we present the state of the art of MEC attack detection and defense frameworks which incorporate FL and GAN algorithms. Moreover, we propose a new cyber defense framework based on a Federated Generative Adversarial Network (FedGAN) algorithm and non-cooperative game to improve over time the precision of attack detection.

ESUAV-NI: Endogenous Security Framework for UAV Perception System Based on Neural Immunity

Unmanned aerial vehicles (UAV) represent an essential component of advanced intelligent equipment that can be used as an aerial perception system by installing various sensors such as vision, hearing, touch, taste and smell to achieve intelligently integrated perception of environments. However, these perception system with environmental information may be threatened by various internal and external attacks, causing a great challenge to the security of UAV. The original security system relied on an expert knowledge base to prevent attacks, but the weaknesses of lacking proactivity and flexibility are gradually exposed. The strong resistance and survivability of biological systems can be used to fill this capability gap and provide new ideas for the security of UAV perception system. Therefore, an endogenous security framework (ESUAV-NI) based on Neural System (NS) and Immune System (IS) is proposed in this study. Through breeding artificial intelligence (AI) vaccines and distributed neural hierarchical control, achieving security protection for UAV perception system. Moreover, we evaluated the AI vaccine breeding approach in the ESUAV-NI by conducting extensive experiments on internal threats and external aerial imagery camouflage data, respectively. The results show that the proposed approach has superior performance for UAV perception system.

Cloud-based mathematical models for self-organizing swarms of UAVs: design and analysis

Unmanned aerial vehicle (UAV) swarms have gained significant attention for their potential applications in various fields. The effective coordination and control of UAV swarms require the development of robust mathematical models that can capture their complex dynamics. The paper introduces mathematical models and relevant paradigms based on the design and analysis of self-organizing swarms of UAVs. The logical and technological construction of the model relies on the theorems developed by authors for obtaining full information exchange during the swarm quasi-random walk. The suggested rotor-router model interprets the discrete-time walk accompanied by the deterministic evolution of configurations of rotors randomly placed on the vertices of the swarm graph. The recommended optimal and fault-tolerant gossip/broadcast schemes support the resilience of swarm to internal failures and external attacks, and cryptographic protocols approve the security. The proposed cloud network topology serves as the implementation framework for the model, encompassing various connectivity options to ensure the expected behavior of the UAV swarms.

Security Issues and Defensive Approaches in Deep Learning Frameworks

Abstract: Deep learning frameworks are instrumental in advancing artificial intelligence, showcasing vast potential across diverse applications. Despite their transformative impact, security concerns pose significant risks, impeding widespread adoption. Malicious internal or external attacks on these frameworks can have far-reaching consequences for society. Our research delves into the intricacies of deep learning algorithms, conducting a thorough analysis of vulnerabilities and potential attacks. To address these challenges, we propose a comprehensive classification system for security issues and corresponding defensive approaches within deep learning frameworks. By establishing clear connections between specific attacks and their defenses, we aim to enhance the robustness of these frameworks. In addition to theoretical considerations, our study extends to real-world applications, exploring a case involving the practical implications of deep learning security issues. Looking ahead, we discuss future directions and open issues within the realm of deep learning frameworks, aspiring to inspire further developments. We anticipate that our research will not only contribute valuable insights but also attract attention from both academic and industrial domains, fostering a collective commitment to fortifying the security of deep learning frameworks.

High Temperature Corrosion of Inconel 625 and Pure Nickel in Contact with Fluoride Melts

Considering the fact that fossil fuels are not inexhaustible, and they pollute the environment, the automotive industry is transitioning from internal combustion to electric engines. As a result, batteries are becoming a strategic component of cars and thus battery ingredients are considered as important raw materials. Therefore, these batteries need to be recycled as well, since the natural resources for producing them are limited and some of the elements like nickel which are used in these batteries can be hazardous if left in the environment. In order to recycle electric batteries, furnaces with high temperature corrosion resistance are necessary. Maintaining these furnaces for longer periods is of great interest and necessitates it for the challenges to be dealt with, especially in case of high temperature corrosion.In the present study, high temperature corrosion behavior of Inconel 625 is compared with pure nickel. Both metals have been exposed to fluoride melts (to mimic the recycling process) for different durations of time. The performances of these two bulk metals are investigated mainly through scanning electron microscope and X-ray diffraction. Element leaching, oxidation, depth of internal attack and depletion profile are among the main parameters studied here. Experiments are carried out both in dry and humid condition to see how humidity changes the reactions. Since there are different alloying elements in Inconel 625, and humidity is involved, pure nickel is used as the baseline to see the effect of different parameters involved. Filtered air is used as the main environment in this study and lithium fluoride is used as the main salt.

General quantum secure multiparty computation protocol for simultaneous summation and multiplication

Abstract Quantum secure multiparty computation occupies an important place in quantum cryptography. Based on access structure and linear secret sharing, we propose a new general quantum secure multiparty computation protocol for simultaneous summation and multiplication in a high-dimensional quantum system. In our protocol, each participant within any authorized sets only needs to perform local Pauli operation once on the generalized Bell state, then the summation and multiplication results can be output simultaneously, which improves the practicality of the protocol. Moreover, in the privacy computation phase, the decoy particle detection technique as well as the addition of random numbers are applied to blind the privacy information, making our protocol higher privacy protection. Security analysis shows that our protocol is resistant to a series of typical external attacks and dishonest internal participant attacks such as individual attack and collusion attack. Finally, compared with the existing protocols, our protocol not only has higher efficiency but also lower consumption.

A novel application of the CORAS framework for ensuring cyber hygiene on shipboard RADAR

Radio Detection and Ranging (RADAR) equipment is a significant information and navigational system onboard vessels and a critical part of a ship’s cyber space. It is an electronic system used not only for detecting surrounding objects, to indicate their positions, and tracking targets using radio waves, but also providing safe navigation by receiving and displaying data from other navigational devices. Therefore, it is concerning to see that marine RADAR systems have various cyber vulnerabilities, including data deletion and data relocation. These systems can be manipulated and penetrated via malicious software, unauthorised remote access, human error, or sabotage by internal and external attackers. This is critical to the cyber hygiene of the ship, which affects its reliability and safety. This study performs a cyber risk assessment using the CORAS framework for RADAR cyber security by developing case-based RADAR cyber scenarios in terms of both its specific information technology subsystems and the cyber security control measures. The output of this study includes a holistic and visual assessment of RADAR's cyber security for both its cyber vulnerabilities and cyber hygiene to better protect shipboard RADAR in the future.

Quantum Secret Reconstruction

AbstractIn addition to secret splitting, secret reconstruction is another important component of secret sharing. In this paper, the first quantum secret reconstruction protocol is proposed to protect the reconstruction process. Before the protocol, a classical secret is divided into multiple shares that are distributed to the shareholds, respectively. In the presented protocol, the dealer utilizes the secret to encrypt a privacy quantum state, and sends the encrypted state to a combiner who is chosen by the dealer within the shareholders. With the help of other shareholders, the combiner utilizes the properties of cluster states to recover the privacy quantum state. It is shown that the proposed protocol is secure against several common attacks, including external and internal attacks. Compared with classical secret reconstruction protocols, this protocol not only achieves information‐theoretic security of all shares, but is also more efficient due to the reduced distribution and computation cost. To demonstrate the feasibility of the protocol, a corresponding simulation quantum experiment is conducted on the IBM Q. Furthermore, in conjunction with quantum fingerprinting, it can be directly applied to achieve the task of quantum multiple secrets sharing, since the classical shares can be reused in the proposed protocol.

Two (w, ω, n) weighted threshold quantum secret sharing schemes on d-level single quantum systems

Quantum secret sharing occupies an important position in quantum cryptography. In this paper, we propose two weighted threshold quantum secret sharing schemes on d-level single quantum systems that share classical information and quantum states, respectively. The dealer Alice distributes the secret share to each participant using the Chinese Remainder Theorem(CRT) for a polynomial ring, which can realize secret sharing among participants with different weights. In classical information sharing, the dealer can share multiple secrets in one round of the protocol and the participants can verify the correctness of the recovered secrets. In quantum state sharing, the dealer can share multiple single quantum states in one round of protocol. Finally, we demonstrate that our scheme is resistant to common external and internal attacks and compare it with related schemes.

Swarm Intelligence-Optimized Energy Management for Prolonging the Lifetime of Wireless Sensor Networks

Recent technological and industrial progress has enabled the development of small, high-performing, energy-saving, affordable sensor nodes that possess the potential to adapt, be self-aware, and self-organize. These nodes are designed for versatile communications applications. Sensor networks for sustainable development focus on the ways in which sensor network technology can enhance social development and improve living standards without causing harm to the environment or depleting natural resources. Wireless sensor networks (WSNs) offer undeniable benefits in various fields, including the military, healthcare, traffic monitoring, and remote image sensing. Given the constraints of sensor networks, varying degrees of security are necessary for these critical applications, posing difficulties in the implementation of conventional algorithms. The issue of security has emerged as a primary concern in the context of IoT and smart city applications. Sensor networks are often regarded as the fundamental building blocks of IoTs and smart cities. The WSN encompasses a routing algorithm, network strength, packet loss, energy loss, and various other intricate considerations. The WSN also addresses intricate matters such as energy usage, a proficient approach for picking cluster heads, and various other concerns. The recent growth of Wireless Sensor Networks (WSNs) has made it increasingly difficult to ensure the trustworthiness and reliability of data due to the distinct features and limitations of nodes. Hostile nodes can easily damage the integrity of the network by inserting fake and malicious data, as well as launching internal attacks. Trust-based security is employed to detect and identify rogue nodes, providing a robust and adaptable protection mechanism. Trust evaluation models are crucial security-enhancement mechanisms that enhance the reliability and collaboration of sensor nodes in wireless sensor networks. This study recommends the use of DFA UTrust, a unique trust technique, to effectively satisfy the security requirements of WSNs.

Retracted: Defending against Internal Attacks in Healthcare-Based WSNs

Retracted: Defending against Internal Attacks in Healthcare-Based WSNs

Single‐Photon Based Three‐Party Quantum Secure Direct Communication with Identity Authentication

AbstractMultipartite quantum secure direct communication (MQSDC) enables multiple message senders to simultaneously and independently transmit secret messages to a message receiver through quantum channels without sharing keys. Existing MQSDC protocols all assume that all the communication parties are legal, which is difficult to guarantee in practical applications. In this study, a single‐photon based three‐party QSDC protocol with identity authentication is proposed. In the protocol, the message receiver first authenticates the identity of two practical message senders. Only when the identity authentication is passed, the legal message senders can encode their messages by the hyper‐encoding technology. In theory, two bits of messages can be transmitted to the message receiver in a communication round. The protocol can resist the external attack and internal attack, and guarantee the security of the transmitted messages and the identity codes of each legal message sender. The secret message capacity of the protocol is simulated with two‐decoy‐state method. The maximal communication distance between any two communication parties can reach 31.75 km with weak signal and decoy state pulses. The three‐party QSDC protocol can be extended to a general MQSDC protocol and has important application in the further practical MQSDC field.

Ensemble Learning Based Malicious Node Detection in SDN-Based VANETs

Background: The architecture of Software Defined Networking (SDN) integrated with Vehicular Ad-hoc Networks (VANETs) is considered a practical method for handling large-scale, dynamic, heterogeneous vehicular networks, since it offers flexibility, programmability, scalability, and a global understanding. However, the integration with VANETs introduces additional security vulnerabilities due to the deployment of a logically centralized control mechanism. These security attacks are classified as internal and external based on the nature of the attacker. The method adopted in this work facilitated the detection of internal position falsification attacks. Objective: This study aimed to investigate the performance of k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest machine learning (ML) algorithms in detecting position falsification attacks using the Vehicular Reference Misbehavior (VeReMi) dataset. It also aimed to conduct a comparative analysis of two ensemble classification models, namely voting and stacking for final decision-making. These ensemble classification methods used the ML algorithms cooperatively to achieve improved classification. Methods: The simulations and evaluations were conducted using the Python programming language. VeReMi dataset was selected since it was an application-specific dataset for VANETs environment. Performance evaluation metrics, such as accuracy, precision, recall, F-measure, and prediction time were also used in the comparative studies. Results: This experimental study showed that Random Forest ML algorithm provided the best performance in detecting attacks among the ML algorithms. Voting and stacking were both used to enhance classification accuracy and reduce time required to identify an attack through predictions generated by k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest classifiers. Conclusion: In terms of attack detection accuracy, both methods (voting and stacking) achieved the same level of accuracy as Random Forest. However, the detection of attack using stacking could be achieved in roughly less than half the time required by voting ensemble. Keywords: Machine learning methods, Majority voting ensemble, SDN-based VANETs, Security attacks, Stacking ensemble classifiers, VANETs,

Secure Internet of medical Things (IoMT) based on ECMQV-MAC authentication protocol and EKMC-SCP blockchain networking

The Internet of Medical Things (IoMT) is a collection of healthcare technologies (medical equipment, software applications, and services) that allows doctors, care providers, and medical test centers to store and exchange health data electronically. However, protecting data privacy via IoMT transmission is a difficult problem. To preserve privacy, blockchain integrated with IoMT enables resilience against a variety of attacks. Blockchain accomplishes peer-to-peer data sharing and stops the majority of the attackers. However, adopting blockchain for Healthcare applications is a prominent challenge due to data privacy issues such as illegal authentication, Byzantine Problems, majority attacks, high computing power, and the enormous storage required to compute healthcare data. Blockchain restrictions in the IoMT sector are solved using the proposed encrypted K-means clustering-based stellar consensus protocol (EKMC-SCP) blockchain networking method. The proposed framework performs mutual user authentication based on the elliptic curve Menezes–Qu–Vanstone-based message authentication code (ECMQV-MAC) protocol under a secure data storage using Deltoid curve-based Pallier cryptosystem (DC-PC) and a key generation mechanism using the Dixon’s method-based Blum–Goldwasser cryptosystem, (DM-BGC). The protocol provides a legal authentication for users to access the blockchain networking (BCN) and avoids large data storage in BC. Furthermore, key generation method makes the privacy of the data more robust against certain internal and external attacks. Finally, a legal user can upload healthcare data in BC, and secure data transfer is performed using EKMC-SCP in the BCN. The experimental results show that the developed framework obtains better throughput and PDR value and reduces the privacy leakages and the time overhead to generating new blocks compared to those of existing state-of-the-art methods.

An ensemble learning framework for the detection of RPL attacks in IoT networks based on the genetic feature selection approach

The rapid proliferation of Internet of Things (IoT) devices has raised critical concerns regarding the security of corresponding IoT networks. The Routing Protocol for Low-Power and Lossy Networks (RPL), a foundational element in IoT communication, is susceptible to diverse routing attacks due to IoT nodes’ constrained resources and open nature. This underscores the necessity for an Intrusion Detection System (IDS) to safeguard RPL-based IoT networks. Existing anomaly-based IDS suffer from high false alarm rates (FAR). In response to these challenges, this paper presents the Ensemble Learning-based Intrusion Detection System (ELG-IDS), which employs stacking and extreme parameter optimization to detect three RPL internal attacks: version number, decreased rank, and DIS flooding attacks. ELG-IDS employs enhanced feature extraction and genetic algorithm (GA)-based feature selection. Experimental results on a dedicated dataset demonstrate ELG-IDS's remarkable accuracy: 99.18%, 99.38%, 99.66%, and 97.90% for version number, rank attack, DIS flooding, and an average accuracy of 97.90% in multi-classification mode, respectively. This study advances IoT network security through ELG-IDS, enhancing protection against evolving security challenges.

Unveiling the failure mechanism on creep response of a casting Ni-based superalloy in thin-wall thickness

With the improvement of thermal efficiency and the lightweight tendency of engine blades, Ni-based superalloy is widely used owing to its excellent performance in high-temperature atmospheres. This work studied the effects of surface oxidation, internal environmental attack, and matrix damage on the failure mechanism in a thin-walled casting Ni-based superalloy at 980 °C/160 Mpa. At the edge of the fracture, the sample suffered a severe environmental attack, resulting in the oxidation-affected zone forms. However, the loss of effective bear area induced by surface damage could not be the main reason for the sample's failure. At the interior of the matrix, voids were preferably initiated at the interface of MC carbides. As the increase of creep deformation, dynamic recrystallization (DRX) occurred at the tip of the voids, which increased the transverse grain boundaries and promoted crack propagation. Moreover, the DRX provided a short penetration path for the nitrogen, causing internal nitridation with AlN and Ti(Ta)N to precipitate. EBSD analysis confirmed that nitrides induced significant dislocations to accumulate at the boundaries of nitrides/γ, accelerating the failure of the sample.