Malicious Manipulations Research Articles

Advanced mathematical modeling of mitigating security threats in smart grids through deep ensemble model

A smart grid (SG) is a cutting-edge electrical grid that utilizes digital communication technology and automation to effectively handle electricity consumption, distribution, and generation. It incorporates energy storage systems, smart meters, and renewable energy sources for bidirectional communication and enhanced energy flow between grid modules. Due to their cyberattack vulnerability, SGs need robust safety measures to protect sensitive data, ensure public safety, and maintain a reliable power supply. Robust safety measures, comprising intrusion detection systems (IDSs), are significant to protect against malicious manipulation, unauthorized access, and data breaches in grid operations, confirming the electricity supply chain’s integrity, resilience, and reliability. Deep learning (DL) improves intrusion recognition in SGs by effectually analyzing network data, recognizing complex attack patterns, and adjusting to dynamic threats in real-time, thereby strengthening the reliability and resilience of the grid against cyber-attacks. This study develops a novel Mountain Gazelle Optimization with Deep Ensemble Learning based intrusion detection (MGODEL-ID) technique on SG environment. The MGODEL-ID methodology exploits ensemble learning with metaheuristic approaches to identify intrusions in the SG environment. Primarily, the MGODEL-ID approach utilizes Z-score normalization to convert the input data into a uniform format. Besides, the MGODEL-ID approach employs the MGO model for feature subset selection. Meanwhile, the detection of intrusions is performed by an ensemble of three classifiers such as long short-term memory (LSTM), deep autoencoder (DAE), and extreme learning machine (ELM). Eventually, the dung beetle optimizer (DBO) is utilized to tune the hyperparameter tuning of the classifiers. A widespread simulation outcome is made to demonstrate the improved security outcomes of the MGODEL-ID model. The experimental values implied that the MGODEL-ID model performs better than other models.

MeST-Former: Motion-enhanced Spatiotemporal Transformer for generalizable Deepfake detection

The rise of Deepfake technology has sparked significant concerns due to its potential for misuse and malicious manipulation of multimedia content. Various detection approaches aimed at detecting Deepfake videos have been proposed, mostly relying on the identification of spatial and temporal artifacts. However, due to the different contexts of source images and the variety of generation techniques, current Deepfake detection methods usually perform well on training datasets, and yet generalize poorly to those unseen identities in new datasets. This issue is widely known as the generalization challenge of Deepfake detection. To address this challenge, this paper proposes an advanced spatiotemporal Deepfake video detector, named Motion-enhanced Spatiotemporal Transformer (MeST-Former). MeST-Former is based on the spatiotemporal modeling capacity of the video Swin Transformer. The spatial and temporal features are obtained from the RGB and motion images, respectively. To enhance the generalization ability of MeST-Former to unseen identities in unseen datasets, the ID-related components in the spatial and temporal features are detached. Specifically, MeST-Former adopts the newly proposed Identity-Decoupling Attention (IDC-Att) module to disentangle the ID-related and ID-unrelated components. Only the ID-unrelated components are used to construct more generalizable spatiotemporal representations. This process makes the constructed spatiotemporal features identity-agnostic and more generalizable to unseen identities. We conducted extensive experiments to evaluate the performance of the MeST-Former. Our results indicate that MeST-Former achieves accurate and generalizable Deepfake detection performance. Notably, MeST-Former also demonstrates high efficacy in detecting AI-animated talking-head videos.

GPT-fabricated scientific papers on Google Scholar: Key features, spread, and implications for preempting evidence manipulation

Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society's evidence base, particularly in politically divisive domains, is a growing concern.

High-security holographic display with content and copyright protection based on complex amplitude modulation

The computer-generated hologram provides an approach to modulate the coherent wavefront and has been widely applied in holographic displays. In the actual application, holograms need to be transmitted through the network, which results in the illegal acquisition and malicious manipulation of holographic images. Therefore, it is necessary to develop a practical method to protect the content and copyright of holographic images. In this paper, we develop a high-security holographic display method based on complex amplitude modulation. In our proposed method, the complex hologram of a holographic image is decomposed into two phase-only holograms, one of which is designed as the ciphertext, and the other is regarded as the key. As a result, the holographic image can be reconstructed only when the ciphertext and the key are paired, which boosts the security of the holographic image. Meanwhile, the copyright of the holographic image is protected via a watermark that is embedded in the ciphertext in the form of a hologram. Due to the simultaneous use of encryption and watermark technology, our proposed method could transmit holographic images at a high security level, and has great potential to be applied in holographic displays in the future.

Robust image tamper detection and recovery with self-embedding watermarking using SPIHT and LDPC

In today’s digital landscape, the pervasive use of digital images across diverse domains has led to growing concerns regarding their authenticity and reliability. The potential for malicious manipulation of these images underscores the critical need to develop robust methods for detecting tampering and ensuring their integrity. Fragile watermarking has been found to have extensive applications for tamper detection and recovery. An image watermarking technique for tamper detection, correction, and recovery is presented in this study. The proposed method employs a self-embedding method to generate the reference watermark from the original image, which has the advantage of superior tamper detection, localization, recovery capabilities, and robustness against attacks. Set Partitioning in the Hierarchical Tree (SPIHT) algorithm is applied to generate a reference watermark of the original image. Low-Density Parity Check (LDPC) is employed for error correction, providing higher-quality reconstruction to recover the original image. Schur decomposition processed the watermarked image blocks to generate authentication bits for each block to enhance tampering detection. The proposed watermarking method was evaluated using PSNR, SSIM, BER, and NC metrics for grayscale and colored images. The technique demonstrated high robustness in tamper detection and recovery against various malicious attacks. Comparative analysis with existing methods shows the efficacy of the proposed method.

Rumor detection based on Attention Graph Adversarial Dual Contrast Learning.

It is becoming harder to tell rumors from non-rumors as social media becomes a key news source, which invites malicious manipulation that could do harm to the public's health or cause financial loss. When faced with situations when the session structure of comment sections is deliberately disrupted, traditional models do not handle them adequately. In order to do this, we provide a novel rumor detection architecture that combines dual comparison learning, adversarial training, and attention filters. We suggest the attention filter module to achieve the filtering of some dangerous comments as well as the filtering of some useless comments, allowing the nodes to enter the GAT graph neural network with greater structural information. The adversarial training module (ADV) simulates the occurrence of malicious comments through perturbation, giving the comments some defense against malicious comments. It also serves as a hard negative sample to aid double contrast learning (DCL), which aims to learn the differences between various comments, and incorporates the final loss in the form of a loss function to strengthen the model. According to experimental findings, our AGAD (Attention Graph Adversarial Dual Contrast Learning) model outperforms other cutting-edge algorithms on a number of rumor detection tasks. The code is available at https://github.com/icezhangGG/AGAD.git.

Smart infrastructure design: Machine learning solutions for securing modern cities

In the realm of securing smart cities against emerging cyber threats, this research encompasses three distinct yet interconnected initiatives. First, a pioneering data architecture design leveraging CycleGAN is proposed to counteract False Data Injection Attacks (FDIA). By cyclically transforming data distributions, CycleGAN ensures the integrity and reliability of smart city information, fortifying against malicious manipulations. Second, an innovative Internet of Things (IoT) concept is introduced, aiming to enhance real-time monitoring and context-aware threat detection within Intrusion Detection Systems (IDS). Harnessing the wealth of data generated by IoT devices, this concept provides a comprehensive understanding of network activities, fostering adaptive responses to emerging threats. Lastly, the research delves into the development of a novel IDS hyperparameter adjusting system. This system integrates the strengths of Biogeography-Based Optimization (BBO) and Whale Optimization Algorithm (WOA) to fine-tune IDS configuration parameters. Drawing inspiration from biogeographical principles and collaborative whale behavior, the hybrid optimization system balances exploration and exploitation, adapting the IDS to diverse network environments. Together, these initiatives represent a holistic approach to fortifying smart cities through cutting-edge data architecture, IoT-driven threat detection, and optimized IDS configurations, contributing to the resilience and cybersecurity of modern urban landscapes.

FaceSigns: Semi-Fragile Watermarks for Media Authentication

Manipulated media is becoming a prominent threat due to the recent advances in realistic image and video synthesis techniques. There have been several attempts at detecting synthetically tampered media using machine learning classifiers. However, such classifiers do not generalize well to black-box image synthesis techniques and have been shown to be vulnerable to adversarial examples. To address these challenges, we introduce FaceSigns — a deep learning based semi-fragile watermarking technique that allows media authentication by verifying an invisible secret message embedded in the image pixels. Instead of identifying and detecting manipulated media using visual artifacts, we propose to proactively embed a semi-fragile watermark into a real image or video so that we can prove its authenticity when needed. FaceSigns is designed to be fragile to malicious manipulations or tampering while being robust to benign operations such as image/video compression, scaling, saturation, contrast adjustments etc. This allows images and videos shared over the internet to retain the verifiable watermark as long as a malicious modification technique is not applied. We demonstrate that our framework can embed a 128 bit secret as an imperceptible image watermark that can be recovered with a high bit recovery accuracy at several compression levels, while being non-recoverable when unseen malicious manipulations are applied. For a set of unseen benign and malicious manipulations studied in our work, our framework can reliably detect manipulated content with an AUC score of 0.996 which is significantly higher than prior image watermarking and steganography techniques.

Replication Robust Payoff Allocation in Submodular Cooperative Games

Submodular functions have been a powerful mathematical model for a wide range of real-world applications. Recently, submodular functions are becoming increasingly important in machine learning (ML) for modelling notions such as information and redundancy among entities such as data and features. Among these applications, a key question is payoff allocation, i.e., how to evaluate the importance of each entity towards the collective objective? To this end, classic solution concepts from cooperative game theory offer principled approaches to payoff allocation. However, despite the extensive body of game-theoretic literature, payoff allocation in submodular games are relatively under-researched. In particular, an important notion that arises in the emerging submodular applications is redundancy, which may occur from various sources such as abundant data or malicious manipulations where a player replicates its resource and act under multiple identities. Though many game-theoretic solution concepts can be directly used in submodular games, naively applying them for payoff allocation in these settings may incur robustness issues against replication. In this paper, we systematically study the replication manipulation in submodular games and investigate replication robustness, a metric that quantitatively measures the robustness of solution concepts against replication. Using this metric, we present conditions which theoretically characterise the robustness of semivalues, a wide family of solution concepts including the Shapley and Banzhaf value. Moreover, we empirically validate our theoretical results on an emerging submodular ML application, i.e., the ML data market.

Toward a Comprehensive Framework for Ensuring Security and Privacy in Artificial Intelligence

The rapid expansion of artificial intelligence poses significant challenges in terms of data security and privacy. This article proposes a comprehensive approach to develop a framework to address these issues. First, previous research on security and privacy in artificial intelligence is reviewed, highlighting the advances and existing limitations. Likewise, open research areas and gaps that require attention to improve current frameworks are identified. Regarding the development of the framework, data protection in artificial intelligence is addressed, explaining the importance of safeguarding the data used in artificial intelligence models and describing policies and practices to guarantee their security, as well as approaches to preserve the integrity of said data. In addition, the security of artificial intelligence is examined, analyzing the vulnerabilities and risks present in artificial intelligence systems and presenting examples of potential attacks and malicious manipulations, together with security frameworks to mitigate these risks. Similarly, the ethical and regulatory framework relevant to security and privacy in artificial intelligence is considered, offering an overview of existing regulations and guidelines.

Trustworthy AI Principles to Face Adversarial Machine Learning: A Novel Study

Artificial Intelligence (AI) has witnessed significant advancements in recent years, enabling its widespread adoption across various domains. However, this progress has also given rise to new challenges, particularly in the context of adversarial machine learning. Adversarial attacks exploit vulnerabilities in AI models, resulting in their misclassification or misbehaviour. To address this critical issue, it is crucial to develop trustworthy AI systems that can withstand such adversarial threats. This paper presents a comprehensive study that covers the types of adversarial machine learning cyber-attacks, methods employed by adversaries to launch such attacks, effective defence mechanisms, and potential future directions in the field. It starts by exploring various types of adversarial ML attacks, characteristics and potential consequences of each attack type, emphasizing the risks they pose to privacy, security, and fairness in AI systems and delving into the methods employed by adversaries to launch adversarial ML attacks. By understanding the tactics used by adversaries, researchers and practitioners can develop robust defence mechanisms that can withstand these attacks. Building upon this understanding, a range of defence strategies can be invented for defending against adversarial ML attacks and emerging research areas, such as the integration of secure multi-party computation, differential privacy, and federated learning are used to enhance the resilience of AI models. By understanding the nature of adversarial attacks and implementing effective defence strategies, AI systems can be fortified against malicious manipulations. The findings of this study contribute to the development of trustworthy AI systems, ensuring their resilience, transparency, and fairness.

Process hazard and operability analysis of BPCS and SIS malicious manipulations by POROS 2.0

The increasing interconnectivity with external networks and the higher reliance on digital systems make the facilities of the chemical, process, and Oil&Gas industry more vulnerable to cyber-attacks. These attacks have the potential of causing events with severe consequences on property, people, and the surrounding environment such as major event scenarios. The application of the currently available methodologies for cyber risk identification to complex plants with a large number of units may be demanding and cumbersome. The present study proposes an updated methodology, named POROS 2.0, that allows reducing time and effort in application by limiting the scope of the analysis to relevant cybersecurity scenarios. The latter are identified by investigating the potential escalation of consequences propagating among process and/or utility nodes of the manipulations of BPCS and SIS, similar to what is done in the HazOp technique in the safety domain. POROS 2.0 was demonstrated by the application to a case study addressing a fixed offshore platform for gas exploitation.

An author credit allocation method with improved distinguishability and robustness

Abstract Purpose The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations. Design/methodology/approach We utilize a modified Sigmoid function to handle the fat-tail distributed citation counts. We also remove the target paper in calculating the contribution of co-citations. Following previous studies, we use 30 Nobel Prize-winning papers and their citation networks based on the American Physical Society (APS) and the Microsoft Academic Graph (MAG) dataset to test the accuracy of our proposed method (NCCAS). In addition, we use 654,148 articles published in the field of computer science from 2000 to 2009 in the MAG dataset to validate the distinguishability and robustness of NCCAS. Finding Compared with the state-of-the-art methods, NCCAS gives the most accurate prediction of Nobel laureates. Furthermore, the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors. The results by NCCAS are also more robust to malicious manipulation. Finally, we perform ablation studies to show the contribution of different components in our methods. Research limitations Due to limited ground truth on the true leading author of a work, the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers. Practical implications NCCAS is successfully applied to a large number of publications, demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders. Originality/value Compared with existing methods, NCCAS not only identifies the leading author of a paper more accurately, but also makes the deification more distinguishable and more robust, providing a new tool for related studies.

Trust Enhanced Collaborative Filtering Recommendation Algorithm

Recommender systems have been designed to suggest to users the suitable items based on the user profiles and therefore reduce the danger of information overload, however, the recommender systems are still prone to profile injection attacks which then exposes a user to a potential fraud, which leads to a sense of untrustworthiness and reduced accuracy due to malicious manipulations. In this research, we developed a model which should be embedded into the recommendation pipeline in order to improve trustworthiness of recommender system output. We extended the classical collaborative recommendation algorithm by adding a new trust parameter and then compare the prediction accuracy of the trust enhanced collaborative filtering algorithm against that of the classical collaborative filtering algorithm using Mean Absolute Error and Root Mean Square Error and then test the hypothesis using t-test. We found that the new trust parameter improves the accuracy of collaborating filtering algorithm significantly.

CompressKey—Near Lossless Layout Compression and Encryption Using Convolutional Auto-Encoder Model and Expansion-Reduction Pattern Techniques

Malicious manipulation of very large-scale integration physical-layout design is a serious problem in modern integrated circuit design. The physical-layout design database requires a highly compressed secured storage medium. In this article, we propose a secured compressive asymmetrical convolutional auto-encoder (ACAE) machine learning framework, CompressKey, which performs layout compression and encryption simultaneously. It utilizes geometric features to eliminate redundancies in layout patterns. We propose a “Divide and Merge” technique to partition each layer into smaller sizes of unique patterns to address the inconsistency of layout pattern complexity. We also propose “Matrix Expansion” and “Matrix Reduction” techniques on the matrix-based pattern to achieve secured “near lossless” compression on the layouts. We have evaluated CompressKey on 14/28/32 nm open-source ICCAD contest databases and achieved a secured compression ratio of 4.54 with encryption features outperforming <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.22\times $ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.59\times $ </tex-math></inline-formula> compared to the state-of-the-art techniques.

Identification of cyber-risks for the control and safety instrumented systems: a synergic framework for the process industry

Malicious interferences to Industrial Automation and Control Systems (IACS) such as the Basic Process Control System (BPCS) and the Safety Instrumented System (SIS) of chemical and process facilities may initiate events with severe consequences such as major accident scenarios (e.g., loss of containment of hazardous substances) and production outages. Existing security vulnerability and risk assessment (SVA/SRA) methodologies, as well as the cybersecurity risk assessment approach proposed by ISA/IEC 62443 series of standards, do not provide any practical method or guideline supporting cyber-risk identification. Moreover, an evident lack of procedures addressing the concrete connection between malicious manipulations of the BPCS and SIS and the impacts on the physical process system that can be initiated, is present in the scientific literature. Given the outlined gap, in the present study, a synergic framework of tools is described and applied to a case study (offshore Oil&Gas platform for gas compression), supporting the systematic identification of the risks that can originate as a result of a malicious interference to the BPCS and SIS. The framework consists of a past incident analysis (PIA) and of two rigorous methodologies, PHAROS, focused on major accident hazards, and POROS, addressing also operability issues. The concept of cyber-attack credibility is here introduced to identify the most credible sets of manipulations based on the score of the plant knowledge level required by the attacker and that of the cyber complexity of the attack pattern, allowing to provide valuable information on how to effectively allocate resources for a more secure network architecture.

An Automated Framework for Board-Level Trojan Benchmarking

Economic and operational advantages have led the supply chain of printed circuit boards (PCBs) to incorporate various untrusted entities. Any of the untrusted entities are capable of introducing malicious alterations to facilitate a functional failure or leakage of secret information during field operation. While researchers have been investigating the threat of malicious modification within the scale of individual microelectronic components, the possibility of a board-level malicious manipulation has essentially been unexplored. In the absence of standard benchmarking solutions, prospective countermeasures for PCB trust assurance are likely to utilize homegrown representation of the attacks that undermine their evaluation and do not provide scope for comparison with other techniques. In this article, we have developed a benchmarking solution to facilitate an unbiased and comparable evaluation of countermeasures applicable to PCB trust assurance. Based on a taxonomy tailored for PCB-level alterations, we have developed a toolflow for the automatic generation of Trojan benchmarks to facilitate a comprehensive evaluation against a large number of diverse Trojan implementations and application of data mining for trust verification. Using the toolflow, we have developed a suite of custom “Trojan benchmarks” (i.e., PCB designs with Trojans) containing representative examples of Trojans in the taxonomy inserted in different PCB designs of varying complexity and functionality. Finally, with experimental measurements from a fabricated PCB and structural analysis of netlist, we analyze the stealthiness of the Trojan designs and present the runtime of the tool for a large number of PCB designs.

ИРРАЦИОНАЛЬНЫЕ НАРОДНЫЕ ЗНАНИЯ В СИБИРСКОМ СЕЛЕ XX - ПЕРВЫХ ДЕСЯТИЛЕТИЙ XXI ВЕКА (ПО ПОЛЕВЫМ МАТЕРИАЛАМ)

The study is focused on the problems of studying irrational traditional knowledge in the rural space of Siberia in the specifed period. rural space of Siberia in the described period. The author identifes problematic aspects in the collection of feld materials and difculties in working with archival materials to search for information on irrational traditional knowledge. In addition, he identifes such types of irrational knowledge as medical, aimed at the preservation and health maintenance; magical, mostly related to malicious manipulations and protection against them, as well as prohibitions, omens and ideas about forces and objects of nature. In addition to traditional representations in the sphere of traditional knowledge, there are also their modern interpretations. In addition to traditional ideas in the sphere of traditional knowledge, their modern interpretations are also presented. The main source was feld materials of ethnographic expeditions and practices of Omsk State University in 1998-2022, published and archival materials.

Information-Containing Adversarial Perturbation for Combating Facial Manipulation Systems

With the development of deep learning technology, the facial manipulation system has become powerful and easy to use. Such systems can modify the attributes of the given facial images, such as hair color, gender, and age. Malicious applications of such systems pose a serious threat to individuals’ privacy and reputation. Existing studies have proposed various approaches to protect images against facial manipulations. Passive defense methods aim to detect whether the face is real or fake, which works for posterior forensics but can not prevent malicious manipulation. Initiative defense methods protect images upfront by injecting adversarial perturbations into images to disrupt facial manipulation systems but can not identify whether the image is fake. To address the limitation of existing methods, we propose a novel two-tier protection method named Information-containing Adversarial Perturbation (IAP), which provides more comprehensive protection for facial images. We use an encoder to map a facial image and its identity message to a cross-model adversarial example which can disrupt multiple facial manipulation systems to achieve initiative protection. Recovering the message in adversarial examples with a decoder serves passive protection, contributing to provenance tracking and fake image detection. We introduce a feature-level correlation measurement that is more suitable to measure the difference between the facial images than the commonly used mean squared error. Moreover, we propose a spectral diffusion method to spread messages to different frequency channels, thereby improving the robustness of the message against facial manipulation. Extensive experimental results demonstrate that our proposed IAP can recover the messages from the adversarial examples with high average accuracy and effectively disrupt the facial manipulation systems.

An Online Approach to Covert Attack Detection and Identification in Power Systems

The development of information technology and digital control systems has posed an inevitable trend of interconnectivity and wirelessness of data communications in power systems. As a result, the cybersecurity of power systems has been raising concerns over the past decades. Yet the complexity of power systems and their conventional designs with local and wide area network communications has posed increasing vulnerability to cyberattacks. Among the types of cyberattacks, data integrity attacks can be more harmful to power systems, especially power transmission systems, through malicious manipulation of measurement and control data to cause damage to the physical system. As the sophistication of cyberattacks increases, certain cyberattacks can successfully bypass traditional intrusion detection systems, which calls for better design of detection schemes. On the other hand, in a large-scale network, attack identification for a power system is as important as attack detection. In this paper, we develop an online approach based on the Sparse Group Lasso to detect and identify the location of a typical type of data integrity attack named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">covert attacks</i> on power transmission systems. We provide the theoretical foundation of the proposed method with a simplified linear system model and extend the method to nonlinear systems. We validate the performance of our method on both linear and nonlinear system settings with a comprehensive numerical study.