Reviewerly/exHarmony huggingface dataset:
Reversible Data Hiding Algorithm in Encrypted Domain Based on Image Interpolation

This study proposes a large capacity, reversible, and separable algorithm based on adaptive embedding to address the issues plaguing reversible data hiding in the encrypted images (RDH-EI) algorithms, such as reversibility, low embedding rate, and incomplete separation. An improved Arnold and chaos-based image encryption algorithm is proposed in the image encryption stage, permitting the original image several times using the Arnold matrix and performing pixel diffusion using the generated chaotic sequence. Then, the encrypted image is interpolated with an improved image interpolation algorithm to create the cover image to be embedded with the secret data. The difference between the secret data to be embedded and the maximum value of n-bit data is first calculated in the data embedding stage. Then, an adaptive data embedding method is proposed to embed the secret data into the interpolated pixels, based on the difference and the size of the secret data. Experimental results show that the proposed algorithm is fully reversible, has no additional data and no data overflow, and is separable with all uncorrelated keys. The proposed algorithm has higher image quality at the same embedding rate than others. It is also resistant to histogram and regular singular (RS) steganalysis.
Adversarial Attacks on Deep Learning-Based DOA Estimation With Covariance Input

Although deep learning methods have made significant advancements across various domains, recent research has shown that carefully crafted adversarial samples can lead to a significant degradation in the performance of deep learning models. Such adversarial examples raise concerns about the reliability and safety of deep learning-based models. Currently, there is a lack of research on the robustness of deep learning based DOA methods against adversarial samples. This letter aims to fill this research gap by leveraging the differentiability of the transformation process from the original signal to the covariance matrix. By utilizing this differentiability, the robustness of the DOA estimation model, which takes the covariance matrix as input, is investigated. Four different white-box attack methods are considered to generate adversarial samples to evaluate the resilience of the model. The experimental results demonstrate that all four methods employed significantly increase the estimation error of the DOA estimation model, posing a serious threat to the model's security.
Text Steganalysis Based on Hierarchical Supervised Learning and Dual Attention Mechanism

Recent methods with deep neural networks for text steganalysis have succeeded in mining various feature representations. However, a limited number of studies have explicitly analyzed potential security issues of generative text steganography. Furthermore, current text steganalysis approaches lack detailed consideration in the intricate design of deep learning architectures tailored to these challenges. In this article, in order to tackle these problems, we first theoretically and empirically analyze the inevitable embedding distortions of generative text steganography at a semantic and statistical levels. In light of this, we then propose an innovative text steganalysis method based on hierarchical supervised learning and a dual attention mechanism. Concretely, to extract highly effective semantic features, the proposed method involves fine-tuning a BERT extractor through the hierarchical supervised learning that combines signals from multiple softmax classifiers, rather than relying solely on the final one. The mean and standard deviation values in the Gaussian distribution of cover and stego texts are then estimated using an encoder of variational autoencoders and used to capture features representing the statistical distortion of generative text steganography. Subsequently, we introduce a dual attention mechanism that dynamically fuses the semantic and statistical features, thereby creating discriminative feature representations essential for text steganalysis. The experimental results demonstrate that our proposed text steganalysis method surpasses the current state-of-the-art techniques across three distinct text steganalysis scenarios: specific text steganalysis, semi-blind text steganalysis, and blind text steganalysis.
Internal Investigations

Prudent governance, as well as common sense, dictates that companies launch an internal investigation once they become aware of the possibility of wrongdoing by their employees, managers and/or agents. The term “internal investigation” can be understood to include all the activities engaged in by companies to collect and evaluate information relating to the purported wrongdoing and the results of any such investigation can be used to fulfill the responsibilities imposed on companies under various federal and state laws to police their own conduct and compliance and report any potential misconduct to the regulatory authorities that oversee the enforcement of such laws.There is no standard form or scope of “investigation,” and the exercise may be limited to just a handful of interviews and review of a small number of documents or may be quite extensive and include interviews with dozens of witnesses and search, collection and review of voluminous amounts of records. The possible subject matter for an internal investigation runs the gamut of compliance areas and can include, for example, a formal employee complaint or report of harassing, threatening, intimidating or violent behavior; a determination of whether employees or others have stolen physical assets or trade secrets or engaged in activities that amount to a misappropriation of company funds; and/or investigations on topics that are unique to the particular industry and regulatory environment of the company, such as investigation of “problem loans” made by a savings and loan and breach of internal controls and procedures by broker-dealers.
Optimum PV distributed generation based on grid and geographical area: A case study of Aden governorate, Yemen

Recently, the global trend has increased to use renewable energies more widely. Several factors have helped the development of this sector, including generous investment, the country's strategy and strong will, and increasing public awareness. Photovoltaic is one of the most common and widely used types of energy. In the current status, this source is the available option that the population of Yemen depends on as a result of the deterioration of the frequent power service from the national grid and the periodic economic crises. However, most Yemen-related studies are confined to off-grid systems. Such systems would increase the financial burden on citizens with limited income. This study works to improve the electric grid performance by injecting three photovoltaic-based distributed generations (PV-DG) in Aden, Yemen. The authors have proposed three distinct cases, each of which involves the implementation of four methodologies, categorized into primary and secondary approaches. Primary methods encompass the optimal geographical PV site selection and PV-DG allocation (size and bus bar in the grid). Secondary methods include the initial PV design and the Least Costly Path Model (LCPM). The workflow begins with identifying suitable locations using the AHP-WLC method with GIS. Subsequently, optimal PV-DG sizes and bus bars are determined using the ABC algorithm to minimize power loss. The third stage involves PV design to refine the search area, and the final stage utilizes LCPM to determine the best option among optimal geolocations. The most suitable PV geolocations are found on 204. 38 km2 for the first and second cases, while 76. 28 km2 for the third case. Even though case (3) generates less area, this is more acceptable due to the neglect of evaluation climate criteria. In contrast, the total injected active power is determined about 15.7 MW and 26 MW for cases (1) and (2 and 3), respectively. Even though the small size is found in case (1), the considering partial grid allows more flexibility. Based on the results obtained, the authors are confident that the current study will mitigate the burden on the national grid and thus improve the individual's life. Furthermore, the proposed methods can be considered one of the bases for further studies of other technologies of renewable energy.
Reviews

An Introduction to Python Programming for Scientists and Engineers, by Johnny Wei-Bing Lin, Hannah Aizenman, Erin Manette Cartas Espinel, Kim Gunnerson, and Joanne Liu, 2022.
A comparative evaluation of three consecutive artificial intelligence algorithms released by Techcyte for identification of blasts and white blood cells in abnormal peripheral blood films

Abstract Introduction Digital pathology artificial intelligence (AI) platforms have the capacity to improve over time through “deep machine learning.” We have previously reported on the accuracy of peripheral white blood cell (WBC) differential and blast identification by Techcyte (Techcyte, Inc., Orem, UT, USA), a digital scanner‐agnostic web‐based system for blood film reporting. The aim of the current study was to compare AI protocols released over time to assess improvement in cell identification. Methods WBC differentials were performed using Techcyte's online AI software on the same 124 digitized abnormal peripheral blood films (including 64 acute and 22 chronic leukaemias) in 2019 (AI1), 2020 (AI2), and 2022 (AI3), with no reassignment by a morphologist at any time point. AI results were correlated to the “gold standard” of manual microscopy, and comparison of Lin's concordance coefficients (LCC) and sensitivity and specificity of blast identification were used to determine the superior AI version. Results AI correlations ( r ) with manual microscopy for individual cell types ranged from 0.50–0.90 (AI1), 0.66–0.86 (AI2) and 0.71–0.91 (AI3). AI3 concordance with manual microscopy was significantly improved compared to AI1 for identification of neutrophils (LCC AI3 = 0.86 vs. AI1 = 0.77, p = 0.03), total granulocytes (LCC AI3 = 0.92 vs. AI1 = 0.82, p = 0.0008), immature granulocytes (LCC AI3 = 0.67 vs. AI1 = 0.38, p = 0.0014), and promyelocytes (LCC AI3 = 0.53 vs. AI1 = 0.16, p = 0.0008). Sensitivity for blast identification ( n = 65 slides) improved from 97% (AI1), to 98% (AI2), to 100% (AI3), while blast specificity decreased from 24% (AI1), to 14% (AI2) to 12% (AI3). Conclusion Techcyte AI has shown significant improvement in cell identification over time and maintains high sensitivity for blast identification in malignant films.
Efficient Membership Inference Attacks against Federated Learning via Bias Differences

Federated learning aims to complete model training without private data sharing, but many privacy risks remain. Recent studies have shown that federated learning is vulnerable to membership inference attacks. The weight as an important parameter in neural networks has been proven effective for membership inference attacks, but it leads to significant overhead. Facing this issue, in this paper, we propose a bias-based method for efficient membership inference attacks against federated learning. Different from the weight that determines the direction of the decision surface, the bias also plays an important role in determining the distance to move along the direction. Moreover, the number of bias is way less than the weight. We consider two types of attacks: local attack and global attack, corresponding to two possible types of insiders: participant and central aggregator. For the local attack, we design a neural network-based inference, which fully learns the vertical bias changes of the member data and non-member data. For the global attack, we design a difference comparison-based inference to determine the data source. Extensive experimental results on four public datasets show that the proposed method achieves state-of-the-art inference accuracy. Moreover, experiments prove the effectiveness of the proposed method to resist some commonly used defenses.
Phantom-CSI Attacks against Wireless Liveness Detection

All systems monitoring human behavior in real time are, by their nature, attractive targets for spoofing. For example, misdirecting live-feed security cameras or voice-controllable Internet-of-Things (IoT) systems (e.g., Amazon Alexa and Google Assistant) has immediately intuitive benefits, so there is a consequent need for detecting liveness of the human(s) whose behavior is being monitored. Emerging research lines have focused on analyzing changes in prevalent wireless signals to detect video or voice spoofing attacks, as wireless-based techniques do not require the user to carry any additional device or sensor for liveness detection. Video/voice streaming and coexisting wireless signals convey different aspects of the same overall contextual information related to human activities, and the presence of spoofing attacks on the former breaks this relationship, so the latter performs well as liveness detection to augment the former. However, we recognize and herein evaluate how to spoof the latter as well to defeat this liveness detection. In our attack, an adversary can easily create phantom wireless signals and synchronize them with spoofed video/voice signals, such that the legitimate user can no longer distinguish real from fake human activity. Real-world experimental results on top of software-defined radio platforms validate the possibility of generating fake CSI flows and demonstrate that with the phantom-CSI attack, the true positive rates (TPRs) of wireless liveness detection systems for video and voice decrease from 100% spoofing detection to just 4.4% and 0, respectively.
Flow-MAE: Leveraging Masked AutoEncoder for Accurate, Efficient and Robust Malicious Traffic Classification

Malicious traffic classification is crucial for Intrusion Detection Systems (IDS). However, traditional Machine Learning approaches necessitate expert knowledge and a significant amount of well-labeled data. Although recent studies have employed pre-training models from the Natural Language Processing domain, such as ET-BERT, for traffic classification, their effectiveness is impeded by limited input length and fixed Byte Pair Encoding.
The Design and Application of Financial System in University Based on Blockchain Technology

The financial data of universities have high requirements for security and confidentiality, and the approval process of various businesses is numerous and tedious, which leads to low overall efficiency of the university financial business process. Blockchain technology is not just a technology spreading digital currencies; it can optimize the financial sharing center. Therefore, this paper presents an optimization framework for financial system which based on blockchain technology can greatly shorten business handling time by changing the linear data transmission mode into a divergent communication mode. And the same time, the Hash algorithm will also improve the quality of financial information and facilitate the online transmission of contracts. The results show that compared with the original financial business process, the business processing time is shortened by 3–5 days, and the work efficiency can be improved by 20%.
CoZure: Context Free Grammar Co-Pilot Tool for Finding New Lateral Movements in Azure Active Directory

Securing cloud environments such as Microsoft Azure cloud is challenging and vulnerabilities due to misconfigurations, especially with user roles assignment, are common. There have been significant efforts to find vulnerabilities that enable lateral movements in Azure AD systems. All of the existing works, however, either follow a manual process to find new vulnerabilities or are only able to discover whether known vulnerabilities exist in a deployed Azure environment. We develop an Azure Active Directory (AAD) lateral movement-discovery tool, CoZure, that can help researchers find new lateral movements in an Azure AD environment. CoZure deploys algorithms from Context-Free Grammar (CFG) to first learn the ways (grammar rules) that security researchers find vulnerabilities and then extend these rules to discover new lateral movement paths. CoZure first collects a large set of existing AAD environment commands using a specialized scraping tool, it then uses CFG to build a knowledge base dataset from these commands and previous attacks. Cozure then applies the knowledge learned to find new combinations of commands that could open up new candidate lateral movements, which are then tested in a real AD environment for validation and manually checked by the user. CoZure helped discover lateral movements that current fuzzing tools (e.g., OneFuzz, RESTler) cannot identify and also shows better performance in finding existing misconfiguration issues in Azure AD. Using CoZure, we have discovered two new (not previously known) lateral movement methods that could lead to numerous new attacking paths in Azure AD.
Temporary Block Withholding Attacks on Filecoin’s Expected Consensus

Filecoin is the most impactful storage-oriented cryptocurrency. In this system, miners dedicate their storage space to the network and verify transactions to earn rewards. Nowadays, Filecoin’s network capacity has surpassed 15 exbibytes.
EnclaveVPN: Toward Optimized Utilization of Enclave Page Cache and Practical Performance of Data Plane for Security-Enhanced Cloud VPN

A cloud Virtual Private Network (VPN) is an essential infrastructure for tenants to connect their on-premise networks with a cloud network. However, tenants are often reluctant to adopt the cloud VPN because of security concerns, such as key disclosure, impersonation, and packet sniffing. Software Guard Extensions (SGX) is a good candidate to address the security concerns because it can create enclaves in the isolated memory (i.e., Enclave Page Cache (EPC)) to protect security-sensitive code and data from malicious access. In this paper, we propose EnclaveVPN, which supports a security-enhanced IPsec gateway using SGX with optimized EPC utilization and practical performance of the data plane. EnclaveVPN leverages enclaves to manage cryptographic keys and execute cryptographic operations for the IPsec gateway. EnclaveVPN allows only encrypted packets to be transmitted within and to/from the cloud network and presents features for optimizing EPC utilization and minimizing overhead in the data plane. We implemented a prototype on a real SGX v1.0 machine (Xeon E-2286M 2.40GHz 8-core CPU). The experiment and benchmark results showed that EnclaveVPN saved the EPC up to 62.5 and achieved approximately 87 of the data plane performance of the non-SGX IPsec gateway.
Quarantine: Mitigating Transient Execution Attacks with Physical Domain Isolation

Since the Spectre and Meltdown disclosure in 2018, the list of new transient execution vulnerabilities that abuse the shared nature of microarchitectural resources on CPU cores has been growing rapidly. In response, vendors keep deploying "spot" (per-variant) mitigations, which have become increasingly costly when combined against all the attacks—especially on older-generation processors. Indeed, some are so expensive that system administrators may not deploy them at all. Worse still, spot mitigations can only address known (N-day) attacks as they do not tackle the underlying problem: different security domains that run simultaneously on the same physical CPU cores and share their microarchitectural resources.
SEnFuzzer: Detecting SGX Memory Corruption via Information Feedback and Tailored Interface Analysis

Intel SGX provides protected memory called enclave to secure the private user data against corrupted or malicious OS environment. However, several researches have shown that the SGX applications suffer from memory corruption vulnerabilities, thus leading to critical information leakage. Detecting memory corruption vulnerability in SGX applications can be cumbersome. Existing works either use symbolic execution or formal methods to analyze the enclave library, which is known to be inefficient and errors prone. Fuzzing, an effective and efficient vulnerability detection method is rarely used in SGX and has limitations.
PrivMon: A Stream-Based System for Real-Time Privacy Attack Detection for Machine Learning Models

Machine learning (ML) models can expose the private information of training data when confronted with privacy attacks. Specifically, a malicious user with black-box access to a ML-as-a-service platform can reconstruct the training data (i.e., model inversion attacks) or infer the membership information (i.e., membership inference attacks) simply by querying the ML model. Despite the pressing need for effective defenses against privacy attacks with black-box access, existing approaches have mostly focused on enhancing the robustness of the ML model via modifying the model training process or the model prediction process. These defenses can compromise model utility and require the cooperation of the underlying AI platform (i.e., platform-dependent). These constraints largely limit the real-world applicability of existing defenses.
Towards Understanding Alerts raised by Unsupervised Network Intrusion Detection Systems

The use of Machine Learning for anomaly detection in cyber security-critical applications, such as intrusion detection systems, has been hindered by the lack of explainability. Without understanding the reason behind anomaly alerts, it is too expensive or impossible for human analysts to verify and identify cyber-attacks. Our research addresses this challenge and focuses on unsupervised network intrusion detection, where only benign network traffic is available for training the detection model. We propose a novel post-hoc explanation method, called AE-pvalues, which is based on the p-values of the reconstruction errors produced by an Auto-Encoder-based anomaly detection method. Our work identifies the most informative network traffic features associated with an anomaly alert, providing interpretations for the generated alerts. We conduct an empirical study using a large-scale network intrusion dataset, CICIDS2017, to compare the proposed AE-pvalues method with two state-of-the-art baselines applied in the unsupervised anomaly detection task. Our experimental results show that the AE-pvalues method accurately identifies abnormal influential network traffic features. Furthermore, our study demonstrates that the explanation outputs can help identify different types of network attacks in the detected anomalies, enabling human security analysts to understand the root cause of the anomalies and take prompt action to strengthen security measures.