Internal Attacks Research Articles

A new quantum group blind signature scheme based on GHZ states

With the development of quantum computers, traditional communication security based on classical computers can no longer guarantee unconditional security. At the same time, a series of encrypted communication solutions that rely on quantum mechanical properties have been spawned. This article focuses on the field of quantum signatures, based on the entangled Greenberger–Horne–Zeilinger state, combining group signatures (any member of the group can sign on behalf of the group) and blind signatures (the signer cannot know the accuracy of the signature content) proposed a new quantum group blind signature scheme that utilizes quantum key distribution to achieve unconditional security. By splitting the original message, the scheme strengthens the relevance of the signature participants and makes the signature to have stronger binding. Then, the security of the scheme against internal and external attacks is analyzed. In this scheme, a pleasing efficiency has been achieved, and the non-repudiation of signatures has been enhanced. At the same time, disputed signatures can be traced back to the signer.

Cross-layer detection and defence mechanism against DDoS and DRDoS attacks in software-defined networks using P4 switches

This paper presents a comprehensive system for detecting and defending against distributed denial-of-service (DDoS) and distributed reflective denial-of-service (DRDoS) flood attacks in software-defined networking (SDN) environments using Programming Protocol-Independent Packet Processors (P4). The proposed system introduces a hybrid intrusion statistical threshold algorithm (HISTA) that analyzes intrusion data statistics to identify potential malicious attacks. Upon detection, the system utilises P4 programmability to implement a custom defence mechanism on the P4 switch. A novel Uruk protocol header collaborates with HISTA for enhanced cross-layer attack detection and categorisation. The HISTA-Uruk mechanism selectively discards malicious packets while ensuring uninterrupted communication for legitimate packets, effectively preventing DDoS/DRDoS attacks. Extensive experiments validate the system’s ability to efficiently detect and thwart various DDoS, DRDoS, and internal attacks, demonstrating its effectiveness in identifying threats and restoring impacted network connections across diverse attack scenarios.

Adversarial Attack in Traffic sign Identification using Deep Learning Frameworks

Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous applications. However, the security issues of deep learning frameworks are among the main risks preventing the wide application of it. Attacks on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and life. We start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in them.

Privacy-Enhanced Cooperative Storage Scheme for Contact-Free Sensory Data in AIoT with Efficient Synchronization

The growing popularity of contact-free smart sensing has contributed to the development of the Artificial Intelligence of Things (AIoT). The contact-free sensory data has great potential to mine and analyze the hidden information for AIoT-enabled applications. However, due to the limited storage resource of contact-free smart sensing devices, data is naturally stored in the cloud, which is at risk of privacy leakage. Cloud storage is generally considered insecure. On one hand, the openness of the cloud environment makes the data easy to be attacked, and the complex AIoT environment also makes the data transmission process vulnerable to the third party. On the other hand, the Cloud Service Provider (CSP) is untrusted. In this article, to ensure the security of data from contact-free smart sensing devices, a Cloud-Edge-End cooperative storage scheme is proposed, which takes full advantage of the differences in the cloud, edge, and end. Firstly, the processed sensory data is stored separately in the three layers by utilizing well-designed data partitioning strategy. This scheme can increase the difficulty of privacy leakage in the transmission process and avoid internal and external attacks. Besides, the contact-free sensory data is highly time-dependent. Therefore, combined with the Cloud-Edge-End cooperation model, this article proposes a delta-based data update method and extends it into a hybrid update mode to improve the synchronization efficiency. Theoretical analysis and experimental results show that the proposed cooperative storage method can resist various security threats in bad situations and outperform other update methods in synchronization efficiency, significantly reducing the synchronization overhead in AIoT.

Novel semi-quantum private comparison protocol with Bell states

Based on Bell states, a new semi-quantum private comparison protocol is proposed that enables two classical users to securely compare the equality of their private information with the aid of a semi-honest third party. Different from the existing semi-quantum private comparison protocols, the two classical participants in the presented protocol do not need to measure and prepare any quantum state, which not only reduces the consumption of quantum devices, but also greatly improves the feasibility of the protocol. Performing different unitary operations on the received particles, classical users can securely compare their secret information. Besides, the devised protocol has higher qubit efficiency than the other similar counterparts, since participants can compare a two-bit privacy each time with one qubit. Meanwhile, after completing the comparison process, all Bell states could be reused since they still retain the corresponding entanglement property, which greatly facilitates the recycle of quantum resources. Security analyses indicate that the designed scheme is secure against external attack and internal attack. Moreover, the operations involved in our scheme are simulated on the IBM Quantum Experience to demonstrate the effectiveness and security of our scheme.

Optimization of Network Security Intelligent Early Warning System Based on Image Matching Technology of Partial Differential Equation

In order to effectively avoid network information leakage and computer network paralysis, and improve the ability of computer network security early warning, it is necessary to design a computer network security intelligent early warning system based on network behavior. Security warning is considered as the second defense mechanism behind the firewall. It can monitor and warn the network without affecting the network performance, so as to provide real-time protection for external attacks, internal attacks and misoperations, and improve the network security. This paper designs an intelligent early warning system for network security based on partial differential equation image matching technology. The system adopts B/S development mode, and the server and browser are located in the campus network. Data fusion technology is used to assess network security, predict potential threats, and add new intrusion features to the feature database for subsequent use. In this paper, a target matching algorithm based on partial differential equation is proposed by using partial differential equation. The algorithm calculates the phase difference through the algebraic combination of orthogonal filter outputs. For scenes with continuous disparity changes, the error matching rate of this algorithm is lower than that of Michael Bleyer algorithm and vertical constraint algorithm. In general, the disparity map generated by this algorithm has high matching accuracy. The results show that the distribution of alarm information is reasonable and in line with the actual situation.

Novel Intelligent BSM Falsification Attack Detection System Using Trusted Neighbor Vehicle Approach in IoV

The proliferation of cyberattacks has emerged as a significant obstacle for advancing technologies such as the Internet of Things (IoT) and Internet of Vehicles (IoV) in recent times. Notably, cryptographic security measures have been implemented in IoV to counteract these cyberattacks. However, these security measures are inadequate when it comes to thwarting internal attackers within the network, as these attackers possess the necessary security credentials for authenticating basic safety messages (BSMs). The research community has made substantial contributions by proposing misbehavior detection systems (MDS) based on data-centric machine learning to identify and prevent internal attackers within IoV. Nevertheless, the existing MDSs in the literature rely on BSMs received from a single vehicle, thereby enabling internal attackers to manipulate their falsified BSMs and evade detection, resulting in a high incidence of false alarms. In this study, we introduce a new intelligent system for detecting falsified BSMs, employing a trusted neighbor vehicle approach (NIBFADS-UTVA)). Our approach demonstrates exceptional effectiveness, achieving an accuracy, precision, recall, and F1-Score all exceeding 99%.

Secure and efficient communications with fine-grained access control in underwater wireless sensor networks

As UWSN have received attention, a need for an efficient and secure communication protocol has arisen to overcome the low device performance, node failure, and high propagation latency. However, existing works are either specialized for one-to-one communication or cannot satisfy low latency constraints. Therefore, this work proposes a secure communication protocol with fine-grained access control for UWSN that support secure and efficient one-to-many communication and considers potential internal attackers for high security level. Specifically, we adopt lightweight ABE to achieve fine-grained access control at low cost, and introduce outsourced decryption to further alleviate the computational load of underwater sensors.

Effect of Lithium Mica Slag on the Internal Sulfate Attack of Cement Mortar

Lithium mica slag is a byproduct acquired via the sulfate method of lithium extraction, and it contains a certain quantity of soluble sulfates. The improper storage of lithium mica slag not only takes up a large amount of land resources, but also poses a threat in terms of environmental pollution. Therefore, this study aimed to investigate the mechanism by which SO42− dissolves in lithium mica slag, along with the impacts of internal sulfate attacks on mortar specimens with 10%, 20%, and 30% lithium mica slag contents. Testing was carried out in terms of the expansion, mass change, flexural and compressive strengths, porosity, composition, and contents of the products. It was determined that a significant quantity of SO42− was generated in mortar specimens with lithium mica slag. The mortar specimens mixed with lithium mica slag produced more ettringite (AFt: 3CaO·Al2O3·3CaSO4·32H2O), which is the product of internal sulfate attacks. This demonstrates that there was an internal sulfate reaction in the mortar specimens mixed with lithium mica slag. The internal sulfate reaction in mortar samples with lithium mica slag was finished in the later stages. It is noteworthy that the reaction did not lead to any cracking or damage; instead, it later allowed for a retention of strength that was equivalent to the strength of mortar specimens without lithium mica slag. In addition, the partial replacement of cement with lithium mica slag not only reduced the environmental pollution caused by soluble sulfates in lithium mica slag, but also reduced the use of cement and, thus, lowered costs.

Decentralized Exchange Transaction Analysis and Maximal Extractable Value Attack Identification: Focusing on Uniswap USDC3

With the advancement of blockchain technology and growing concerns about the vulnerabilities and mistrust in centralized financial services, decentralized finance (DeFi) and decentralized exchanges (DEXs) have emerged as promising alternatives. This paper delves into the challenges and issues within DeFi, with a particular focus on Uniswap. We highlight the susceptibility to Maximal Extractable Value (MEV) attacks, providing a background on the current state of DeFi and DEXs. Our approach includes a detailed transaction analysis on Uniswap to identify and analyze MEV attack patterns, alongside a method for detecting bots. The results offer critical insights into the nature of various attacks in DEXs and the correlation between internal and external blockchain events and MEV attack patterns. This research provides valuable guidelines for enhancing DEX security and mitigating MEV risks, serving as an essential resource for stakeholders in the DeFi ecosystem.

Ensuring the Security of Information Flows at University Departments

The relevance of the study is caused by possible threats to the information security of information flows at the university department revealed during the latest information security audit. The purpose of our research is to detect information flows of the department and a detailed analysis of methods for their protection. The object of the study is the process of ensuring security of information flows at the university department. The subject of the research is the creation of information flow schemes of the department and developing network interaction schemes of the university department based on the user access policy. Particular attention is paid to the description of the identified potential risks arising from unauthorized access, external or internal attacks. It is important to note that the suggested ideas to improve the security of information flows of the university department are an application of existing methods of protecting information necessary for practical use in the educational process. The possibility of implementing the security of applied software and network support of the department is shown.

Organizations' readiness for insider attacks: A process‐oriented approach

AbstractContextOrganizations constantly strive to protect their assets from outsider attacks by implementing various security controls, such as data encryption algorithms, intrusion detection software, firewalls, and antivirus programs. Unfortunately, attackers strike not only from outside the organization but also from within. Such internal attacks are called insider attacks or threats, and the people responsible for them are insider attackers or insider threat agents. Insider attacks pose more significant risks and can result in greater organizational losses than outsider attacks. Thus, every organization should be vigilant regarding such attackers to protect its valuable resources from harm. Finding solutions to protect organizations from such attacks is critical. Despite the importance of this topic, little research has been conducted on providing solutions to mitigate insider attacks.ObjectiveThis study aims to develop an organizational readiness model to assess an organization's readiness for insider attacks.MethodWe conducted a multivocal literature review to identify practices that can be used to assess organizations' readiness against insider attacks. These practices were grouped into different knowledge areas of insider attacks for organizations. The insider attack readiness model was developed using identified best practices and knowledge areas: compliance, top management, human resources, and technical.ResultsThis model was evaluated at two levels—academic and real‐world environments. The evaluation results show that the proposed model can identify organizations' readiness against insider attacks.ConclusionThe proposed model can guide organizations through a secure environment against insider attacks.

Hybrid protocols for multi-party semiquantum private comparison, multiplication and summation without a pre-shared key based on d-dimensional single-particle states

In this paper, by utilizing d-dimensional single-particle states, three semiquantum cryptography protocols, i.e., the multi-party semiquantum private comparison (MSQPC) protocol, the multi-party semiquantum multiplication (MSQM) protocol and the multi-party semiquantum summation (MSQS) protocol, can be achieved simultaneously under the assistance of two semi-honest quantum third parties (TPs). Here, the proposed MSQPC scheme is the only protocol which is devoted to judging the size relationship of secret integers from more than two semiquantum participants without a pre-shared key. And the proposed MSQM protocol absorbs the innovative concept of semiquantumness into quantum multiplication for the first time, which can calculate the modulo d multiplication of private inputs from more than two semiquantum users. As for the proposed MSQS protocol, it is the only semiquantum summation protocol which aims to accomplish the modulo d addition of more than three semiquantum users’ private integers. Neither quantum entanglement swapping nor unitary operations are necessary in the three proposed protocols. The security analysis verifies in detail that both the external attacks and the internal attacks can be resisted in the three proposed protocols.

Characterization of bacterial diversity and screening of cellulose-degrading bacteria in the gut system of Glenea cantor (Fabricius) larvae.

The intestinal bacteria of longhorn beetles would be ideal targets for pest control and lignocellulosic resources by destroying or exploiting their cellulose-degrading function. This article aims to investigate the diversity and community structure of intestinal bacteria the oligophagous longhorn beetle Glenea cantor. Additionally, it seeks to identify the presence of lignocellulose-degrading bacteria in the gut, and explore their role in consuming host kapok trees Bombax malabaricum. In this study, the bacterial community from G. cantor was examined by Illumina sequencing of 16S ribosomal RNA (rRNA) targeting the V3 and V4 regions. A total of 563,201 valid sequences and 814 OTUs were obtained. The dominant phyla were Proteobacteria, and the dominant genera were Acinetobacter and Lactococcus. The analysis of microbial diversity revealed a high bacterial diversity in the samples, with the gut bacteria playing a crucial role in the physiological activities of the host, particularly, 9 genera of intestinal bacteria with cellulose degradation function were found, highlighting their vital role in cellulose degradation. Five strains of cellulose-degrading bacteria, belonging to the genus Pseudomonas, were obtained from the intestinal tract of G. cantor larvae using traditional isolation and culture techniques as well as 16S rDNA sequencing. Among these strains, A4 exhibited a cellulase activity of 94.42 ± 0.42U/mL, while A5 displayed the highest filter paper enzyme activity of 127.46 ± 3.54U/mL. These results offered valuable insights into potential targets for pest control through internal attack digestion and cellulose-degrading bacteria in longhorn beetles.

Macrophage modulation in activation process induced immune thrombocytopenia

The immune system operates like an orchestra that harmoniously maintains the homeostasis balance while protecting from external or internal pathogens attack. Inflammation is one of the key critical immune defenses to eradicate pathogens and encourage tissue repair and recovery by activating the host’s immune and non-immune cells. As a part of the immune response during inflammation, blood platelets serve various functions; however, their activation and involvement in inflammation can also contribute to pathological conditions, such as thrombosis, which results in myocardial infarction, stroke, and venous thromboembolism. Activated platelets can mobilize and release intracellular granules (alpha and dense granules), which include secondary mediators like chemokine PF4/CXCL4. In contrast to most other chemokines, PF4 participates in several long-term regulatory processes, such as cell differentiation, survival, and proliferation. However, recent findings suggest that PF4 is also responsible for modulating macrophage polarization, which can substantially impact the development of induced immune thrombotic thrombocytopenia. This review aims to explain how PF4 induced vascular problems by modulation of macrophage development during immunological thrombocytopenia. A literature search using the keywords PF4, CXCL4, macrophage M4, platelet macrophage M4, and induced immune thrombocytopenia was done using the following databases: Google Scholar, ProQuest, ScienceDirect, and Scopus for articles published from 2000 to 2023. The literature study was done to find the connection between platelet activation, macrophage modulation, and vascular problems such as atherosclerosis and thromboembolism in induced immune thrombotic thrombocytopenia. Several recent studies on PF4, macrophage modulation, and vaccine-induced thrombotic thrombocytopenia were carefully reviewed. This review concludes that macrophage polarization modulation is promising in managing vascular problems in patients with induced immune thrombotic thrombocytopenia.

Implementing quantum anonymous multi-party ranking: the influence and application of the law of large numbers

Secure multi-party ranking is not only a pivotal component within the domain of secure multi-party computation but also holds extensive real-world applicability, given its efficacy in safeguarding the integrity and privacy of data. In this paper, we propose a verifiable quantum anonymous multi-party ranking protocol, which leverages the power of multi-particle entangled states in conjunction with the law of large numbers. The protocol achieves the secure ranking of participants’ private data while verifying their identities through the assistance of a semi-honest third party. We prove that the protocol is resistant to different types of attacks from internal or external attackers. In addition, the proposed protocol employs qubits as the information carriers, which improves the feasibility of the protocol. We demonstrate the feasibility of the protocol by using the online analog quantum computer of IBM Corporation placed on the cloud. Our research represents an innovative initiative that merges the realms of quantum cryptography and statistical analysis to address pertinent practical challenges.

Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0

Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts.

PREEN: An Aggregation Mechanism for Privacy-Preserving Smart-Metering Communications

The widespread use of home consumer eletronics (CEs) provides residents facility, assistance and comfort. Meanwhile, smart meters are deployed at households to optimize electricity supply and demand and improve electricity efficiency. These meters monitor, collect and aggregate the electricity consumption of the home CEs in a near-real-time way. However, such processes seriously invade consumer privacy, e.g., household occupancy and economic status. Many existing privacy-preserving schemes rely on the assumption that certain components can be trusted, which is hard to be ensured, and cannot resist against collusion attacks. In this study, we leverage differential privacy and a re-encryption model to produce a privacy-preserving aggregation mechanism, called PREEN, for smart-metering communications. PREEN simultaneously facilitates data aggregation, differential privacy, and protection against various attacks. Specifically, we adopt ElGamal algorithm to construct the re-encryption scheme and differential privacy to aggregate the consumption measurements of residential users at the gateways and control center, thereby enabling resistance against slashing privacy attacks. Strict and detailed security proofs as well as extensive performance evaluations are adopted to demonstrate that the proposed PREEN provides rigorous privacy preservation and confidentiality against diverse external and internal attacks, while remaining a comparable or even lower computational overhead and a negligible communication overhead and a reasonable accuracy in terms of the utility of differential privacy, compared to the state-of-the-art mechanisms.

Effect of Sulfate Attack on the Expansion Behavior of Cement-Treated Aggregates.

The expansion induced by sulfate attack on cement-treated aggregates (SACA) is a well-known problem that can be solved. It causes obvious heaves in road bases and railway subgrades. In this paper, the effects of the sodium sulfate content, cement content, degree of compaction, sulfate types, attack types, aluminum ion supply, concentration of curing sulfate solution, and temperature on the expansion behavior induced by SACA were investigated over 60 days in the laboratory. Based on the Sobol sensitivity analysis method, the influence of the sensitivity of each factor on the expansion was quantitatively analyzed, and the dominant factor of expansion was proposed. Results show that sulfate content is the domain factor of expansion that is induced by SACA, and it presents a logarithmic function relationship with strain. The 0.5% sodium sulfate content is the minimum sulfate content which causes the expansion that is induced by SACA. When the sulfate content is less than 1%, the expansion induced by SACA is minor. When the sulfate content is between 1% and 3%, the expansion behavior is expressed in four stages as follows: rapid strain increase, followed by a short stagnation period, then a significant strain increase and, finally, constant strain. When the sulfate content is greater than 5%, there are two stages comprising the expansion behavior as follows: the rapid strain increases and constant strain occurs. Greater sulfate content, greater degree of compaction, and lower temperature have positive effects on the expansion induced by SACA. The cement content does not have a consistent effect on expansion behavior. Compared with a sodium sulfate attack, both the reaction rate and expansion of cement-treated aggregates that are attacked by gypsum are smaller, and the attack period is also longer. When the sulfate content is greater than 1%, the addition of kaolin promotes the progression of the expansion induced by SACA. A small amount of water is sufficient for the demand for the sulfate attack. When the sulfate content is at a certain level, the expansion induced by SACA that is under external attack is much smaller than the expansion that is under internal attack. This study is expected to serve as a reference for future research on the mechanics of SACA, and it attempts to provide theoretical support for amending expansions that are induced by SACA.

Verifiable multi-party quantum secret sharing based on GHZ states

In this paper, we propose a verifiable multi-party secret sharing protocol based on an entanglement swapping theorem of GHZ states. In our protocol, all receivers only need to perform simple GHZ measurements to recover the secret. The quantum-safe system proposed in this paper is feasible with current quantum technologies, and we show that it is secure against both external and internal attacks. In addition, the secret sender can verify whether the secret is correctly recovered.