Attack Methods Research Articles

Research on techniques for detecting brute-force attacks on corporate email

Conventional brute-force attacks can now be detected and identified based on statistical analysis of logs and traffic data. However, they fail to detect low-frequency and distributed brute-force attack behaviors. To address different attack methods, new detection techniques have emerged. This study compares various machine learning algorithms and selects two methods, namely the clustering algorithm k-means and bdscan, as well as the decision tree algorithm for data learning. In one approach, normal user login data is integrated with enterprise email log data. The data is first statistically analyzed and filtered, followed by quantifying data characteristics using information entropy. Subsequently, machine learning algorithms are employed for classification, and the results are visualized for display. In another approach, labeled raw data is used to train a model using the decision tree algorithm. By comparing the two analysis results, a more accurate model can be obtained. These analytical methods can help enterprises strengthen email security and defend against low-frequency and distributed brute-force attacks.

Analysis of neural network detectors for network attacks

While network attacks play a critical role in many advanced persistent threat (APT) campaigns, an arms race exists between the network defenders and the adversary: to make APT campaigns stealthy, the adversary is strongly motivated to evade the detection system. However, new studies have shown that neural network is likely a game-changer in the arms race: neural network could be applied to achieve accurate, signature-free, and low-false-alarm-rate detection. In this work, we investigate whether the adversary could fight back during the next phase of the arms race. In particular, noticing that none of the existing adversarial example generation methods could generate malicious packets (and sessions) that can simultaneously compromise the target machine and evade the neural network detection model, we propose a novel attack method to achieve this goal. We have designed and implemented the new attack. We have also used Address Resolution Protocol (ARP) Poisoning and Domain Name System (DNS) Cache Poisoning as the case study to demonstrate the effectiveness of the proposed attack.

An integrated graph data privacy attack framework based on graph neural networks in IoT

SummaryKnowledge graphs contain a large amount of entity and relational data, and graph neural networks, as a class of efficient graph representation techniques based on deep learning, excel in knowledge graph modeling. However, previous neural network architectures for the most part only learn node representations and do not fully consider the heterogeneity of data. In this article, we innovatively propose a privacy attack framework based on IoT, PAFI, which is able to classify entities and relations, learn embedding representations in multi‐relational graphs, and can be applied to some existing neural network algorithms. Based on this, a fine‐grained privacy attack model, FPM, is proposed, which can perform attack operations on multiple targets, achieve selectivity of target tasks, and greatly improve the generalization ability of the attack model. In this article, the effectiveness of PAFI and FPM is demonstrated by real network datasets, and compared with previous attack methods, both of which achieve good results.

TPoison: Data-Poisoning Attack against GNN-Based Social Trust Model

In online social networks, users can vote on different trust levels for each other to indicate how much they trust their friends. Researchers have improved their ability to predict social trust relationships through a variety of methods, one of which is the graph neural network (GNN) method, but they have also brought the vulnerability of the GNN method into the social trust network model. We propose a data-poisoning attack method for GNN-based social trust models based on the characteristics of social trust networks. We used a two-sample test for power-law distributions of discrete data to avoid changes in the dataset being detected and used an enhanced surrogate model to generate poisoned samples. We further tested the effectiveness of our approach on three real-world datasets and compared it with two other methods. The experimental results using three datasets show that our method can effectively avoid detection. We also used three metrics to illustrate the effectiveness of our attack, and the experimental results show that our attack stayed ahead of the other two methods in all three datasets. In terms of one of our metrics, our attack method decreased the accuracies of the attacked models by 12.6%, 22.8%, and 13.8%.

Transferable adversarial attack based on sensitive perturbation analysis in frequency domain

Recently, transferable adversarial attacks on deep neural networks (DNNs) have attracted significant attention. Although existing adversarial attacks have achieved high attack success rates in white-box scenarios, they perform poorly in black-box attack scenarios. To address the issue of low transferability in black-box adversarial attacks, we propose a frequency-sensitive perturbation-based black-box adversarial attack method to enhance transferability performance from the perspective of Fourier domain sensitivity. Initially, the Fourier sensitivity heatmaps of multiple substitute models are integrated to identify the common sensitive frequency region. Subsequently, the perturbations in the frequency domain are optimized through joint loss, thereby constraining the sensitive region and limiting the perturbations in the non-sensitive region. Finally, in each iteration, a spectral model enhancement is performed to simulate additional substitute models and reduce the spectral discrepancy between the substitute and target models. Extensive experimental results on benchmark datasets demonstrated the effectiveness of the proposed method. Compared to existing black-box adversarial attack methods, the proposed approach generates targeted and non-targeted adversarial samples with higher transfer success rates and good invisibility, thereby confirming its superiority.

SUETA: Speaker-specific utterance ensemble based transfer attack on speaker identification system

With the widespread application of speaker identification (SI) systems in security-related tasks, the robustness of SI systems against adversarial examples has garnered increasing attention. Existing works have demonstrated the vulnerability of SI systems to transfer-based black-box attacks, wherein attackers generate adversarial examples with a surrogate model and then transfer them to attack the target system. However, the attack success rate (ASR) of transfer-based black-box attacks is limited by the transferability of adversarial examples. As far as we know, few works have investigated enhancing the transferability of adversarial examples for speech utterances to attack SI systems. Furthermore, existing works only utilize a single utterance in the attack process, but in practical situations, an attacker can usually collect multiple utterances of a speaker. In this paper, we propose a novel transfer-based black-box attack method, called speaker-specific utterance ensemble based transfer attack (SUETA), to attack SI systems. To the best of our knowledge, SUETA is the first transfer-based black-box attack method that utilizes multiple utterances instead of a single one. Furthermore, we also propose an improved variant of SUETA, called SUETA+, by sharing gradients of utterances at the speaker-embedding level. Empirical results show that both SUETA and SUETA+ improve the ASR compared to the baselines under the classical cross-model situation. SUETA+ further shows additional improvement over SUETA, especially in the case of the untargeted attack. SUETA+ also outperforms the baselines in cross-dataset and cross-preprocessor situations, although the ASR for all transfer-based attacks decreases compared to that in the cross-model situation. Moreover, SUETA+ can significantly improve the ASR against commercial APIs (iFlytek and TalentedSoft) and the voice assistant (Tmall Genie) for the untargeted attack compared to the baselines.

A frequency-injection backdoor attack against DNN-Based finger vein verification

Recent research has demonstrated that deep neural networks, typically used in finger vein recognition, are susceptible to different types of attacks, such as adversarial attacks, data poisoning attacks, and backdoor attacks. Among the attacks, backdoor attacks occur in almost every stage of the deep learning pipeline. Finger vein recognition has extensively been used in real-world applications for personal identity authentication. To develop a secure finger vein recognition system, one must study possible backdoor attacks, which can embed hidden malicious behaviors into the system. Existing backdoor attacks are as easily perceptible as conspicuous spatial triggers and difficult-to-resist data augmentation. To address this issue, we propose a novel frequency-injection-based backdoor attack method capable of delivering attacks in finger vein recognition. Specifically, images are transformed from the spatial domain to the frequency domain by discrete wavelet transform (DWT), and the trigger injects several times in the high-frequency part in the vertical direction. Experimental results of public finger vein datasets validate the proposed method's effectiveness, showing good attack performance and bypassing backdoor defense.

Detecting DoS Attacks through Synthetic User Behavior with Long Short-Term Memory Network.

With the escalation in the size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long Short-Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.

A novel chaotic oscillator with a half-line of unstable equilibria: Basins of attraction, chaos control, chaos synchronization, and encryption applications

A novel 3D chaotic oscillator that incorporates quadratic and absolute-function nonlinearities is introduced in this paper. The system dynamics are explored using the Lyapunov direct method, phase plane trajectories, time response, Lyapunov exponents, bifurcation diagrams, and basins of attraction. The uniqueness and existence of the system solution have been proven. The analytical investigations show that the system has two stable equilibrium points along with one unstable equilibrium point and a line of equilibria. The positive half of this line represents unstable equilibria, while the negative half is associated with stable equilibria. Additionally, it is found that the oscillator exhibits a chaotic basin of attraction centered along the line of equilibria and surrounded by a fixed-point attractor. An electronic circuit using Multisim software is designed to demonstrate the possibility of physical implementation for the considered mathematical model. Chaos control is addressed using adaptive and sliding mode control strategies. The performance of both control methods is compared, with the sliding mode control demonstrating superior results in both fast response and small transient overshoot. Furthermore, a novel sliding mode controller for master–slave synchronization is introduced, showing high performance compared to other sliding-mode and adaptive control methods applied in the literature. Finally, the proposed oscillator is employed as a Pseudo Random Number Generator (PRNG) for image encryption applications using a new encryption model. The experimental results confirm the high security and robustness of the proposed encryption algorithm against various attack methods.

FDT: Improving the transferability of adversarial examples with frequency domain transformation

The development of Deep Neural Networks (DNNs) has facilitated profound advancements in computer vision. While DNNs demonstrate substantial capabilities, their susceptibility to adversarial attacks can introduce significant errors and hinder their applicability in real-world scenarios. The characteristic of black-box attacks is that they rely on the input–output mapping of the model without accessing its parameters or gradients. Due to the disparities between the substitute and target models, black-box attacks typically exhibit reduced success rates. To address the challenges outlined previously, we propose the Frequency Domain Transformation (FDT) method that employs the Discrete Cosine Transform (DCT) for transforming the input image into the frequency domain. It innovatively applies a grid mask to generate adversarial samples within the frequency domain. This transformation diminishes the spatial correlation among the image pixels and offers a fresh perspective for enhancing the transferability of adversarial examples. Experiments on adversarial attacks using the ImageNet dataset reveal that adversarial examples, when created through methods that transform data in the frequency domain, show enhanced transferability compared to those produced by transformations in the spatial domain. In attacks on six classification models using the single-model approach, FDT achieved an average success rate of 82.9%, corresponding to a 14.4% improvement over the spectrum simulation attack (SSA) method. For ensemble-model attacks with momentum-based techniques, the average success rate using the proposed strategy was 94.4%, corresponding to a 5.7% improvement over the SSA method.

FedIMP: Parameter Importance-based Model Poisoning attack against Federated learning system

In federated learning systems, the participants collaboratively train a joint model without sharing their raw data. However, these systems are susceptible to poisoning attacks, due to the difficulty in supervising local training processes. Most existing model poisoning attacks target all parameters, resulting in significant model modifications that can be easily detected by checking statistical similarity. We therefore propose FedIMP, an innovative untargeted model poisoning attack method that introduces the concept of parameter importance to enhance stealthiness and effectiveness. We first assess the parameter importance using the Fisher information to selectively poison only those with high importance. Furthermore, we formulate an optimization problem to derive the optimal malicious boosting coefficient such that the attack can evade defense mechanisms while enhancing attack's impact. The experimental results validate the effectiveness of FedIMP, demonstrating its ability to deteriorate model performance and slow down convergence across various aggregation algorithms. Our approach highlights the critical vulnerability in federated learning systems and provides insights for developing more robust defense strategies against poisoning attacks.

The security analysis on the rabbit stream cipher

In recent years, stream cipher systems that have been traditionally designed using linear feedback shift register have been almost entirely compromised by algebraic attack methods. Thus, identifying a method to establish concepts for new-generation ciphers, prevent existing security problems, and design new stream cipher systems that consider both security and performance has become a crucial concern in the field of cryptography. In 2004, the European Union initiated the eSTREAM project to emulate the Advanced Encryption Standard used in the United States. The project consisted of 48 participating stream cipher candidates. Through open selection, review, and runoff voting, the results were announced in May 2008. This research investigated one of the finalists of the eSTREAM competition: The Rabbit stream cipher. Additionally, stream cipher attack methods have been extensively studied in recent years, especially those for distinguishing attacks. Thus, contributions of this article is explored the design concepts of the core algorithms in the new-generation stream cipher systems for determining the corresponding mathematical principles and practical approaches to contribute to the study of stream cipher systems.

Real-time phishing detection using deep learning methods by extensions

Phishing is an attack method that relies on a user’s insufficient vigilance and understanding of the internet. For example, an attacker creates an online transaction website and tricks users into logging into the fake website to steal their personal information, such as credit card numbers, email addresses, phone numbers, and physical addresses. This paper proposes implementing an extension to prevent phishing for internet users. In particular, this study develops a smart warning feature for the proposed extension using deep learning models. The proposed extension installed in the web browser protects users by checking for, warning about, and preventing untrusted connections. This study evaluated and compared the performance of machine learning models using a malicious uniform resource locator (URL) dataset containing 651,191 data samples. The results of the investigation confirm that the proposed extension using a convolutional neural network (CNN) achieved a high accuracy of 98.4%.

A Survey on Graphical Password Strategy Authentication

In an ever-evolving digital realm, ensuring the security and reliability of software applications has become increasingly crucial. The continuous advancement of technology also brings about more complex cyber threats, emphasizing the urgent need to identify and address vulnerabilities. In response to this growing imperative, we introduce "Graphical Password Authenticator: A Novel Approach to Secure User Authentication." The Graphical Password Authenticator reimagines the conventional user authentication process by utilizing graphical elements and innovative techniques. It provides a secure and user-friendly alternative to traditional text-based passwords, offering enhanced protection against unauthorized access. By incorporating graphical images, patterns, or other visual cues, users can create unique and memorable authentication credentials that are difficult for malicious actors to compromise. This approach offers a more intuitive and engaging way for users to access their accounts while significantly reducing the risk of password-related security breaches. The Graphical Password Authenticator system ensures robust security through a combination of graphical recognition and encryption technologies. It is designed to be resistant to common attack methods such as brute force and keyloggers, providing a higher level of protection for user accounts and sensitive information. This novel approach to user authentication fosters a secure and user-centric environment, making it a promising solution for the ever-evolving digital landscape. The Graphical Password Authenticator is a groundbreaking step at the intersection of user authentication and innovation. By redefining the way users prove their identity through memorable graphical elements, this approach empowers users with a more secure and intuitive means of accessing their digital resources. This paradigm shift in authentication methods showcases the potential of technology to enhance user security and create a safer digital future, ultimately bridging the gap between user convenience and robust cybersecurity. Keywords: Graphical Password Authenticator, User Authentication, Cybersecurity, Digital Security, Encryption Technologies, User-Centric Approach, Brute Force Resistance, Keyloggers Protection.

Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion

Visually-aware recommender systems have found widespread applications in domains where visual elements significantly contribute to the inference of users’ potential preferences. While the incorporation of visual information holds the promise of enhancing recommendation accuracy and alleviating the cold-start problem, it is essential to point out that the inclusion of item images may introduce substantial security challenges. Some existing works have shown that the item provider can manipulate item exposure rates to its advantage by constructing adversarial images. However, these works cannot reveal the real vulnerability of visually-aware recommender systems because (1) the generated adversarial images are markedly distorted, rendering them easily detected by human observers; (2) the effectiveness of these attacks is inconsistent and even ineffective in some scenarios or datasets. To shed light on the real vulnerabilities of visually-aware recommender systems when confronted with adversarial images, this paper introduces a novel attack method, IPDGI (Item Promotion by Diffusion Generated Image). Specifically, IPDGI employs a guided diffusion model to generate adversarial samples designed to promote the exposure rates of target items (e.g., long-tail items). Taking advantage of accurately modeling benign images’ distribution by diffusion models, the generated adversarial images have high fidelity with original images, ensuring the stealth of our IPDGI. To demonstrate the effectiveness of our proposed methods, we conduct extensive experiments on two commonly used e-commerce recommendation datasets (Amazon Beauty and Amazon Baby) with several typical visually-aware recommender systems. The experimental results show that our attack method significantly improves both the performance of promoting the long-tailed (i.e., unpopular) items and the quality of generated adversarial images.

A Research on Graphical Password Strategy Authentication

In an ever-evolving digital realm, ensuring the security and reliability of software applications has become increasingly crucial. The continuous advancement of technology also brings about more complex cyber threats, emphasizing the urgent need to identify and address vulnerabilities. In response to this growing imperative, we introduce "Graphical Password Authenticator: A Novel Approach to Secure User Authentication." The Graphical Password Authenticator reimagines the conventional user authentication process by utilizing graphical elements and innovative techniques. It provides a secure and user-friendly alternative to traditional text-based passwords, offering enhanced protection against unauthorized access. By incorporating graphical images, patterns, or other visual cues, users can create unique and memorable authentication credentials that are difficult for malicious actors to compromise. This approach offers a more intuitive and engaging way for users to access their accounts while significantly reducing the risk of password-related security breaches. The Graphical Password Authenticator system ensures robust security through a combination of graphical recognition and encryption technologies. It is designed to be resistant to common attack methods such as brute force and keyloggers, providing a higher level of protection for user accounts and sensitive information. This novel approach to user authentication fosters a secure and user-centric environment, making it a promising solution for the ever-evolving digital landscape. The Graphical Password Authenticator is a groundbreaking step at the intersection of user authentication and innovation. By redefining the way users prove their identity through memorable graphical elements, this approach empowers users with a more secure and intuitive means of accessing their digital resources. This paradigm shift in authentication methods showcases the potential of technology to enhance user security and create a safer digital future, ultimately bridging the gap between user convenience and robust cybersecurity.

An adversarial example attack method based on predicted bounding box adaptive deformation in optical remote sensing images

Existing global adversarial attacks are not applicable to real-time optical remote sensing object detectors based on the YOLO series of deep neural networks, which makes it difficult to improve the adversarial robustness of single-stage detectors. The existing methods do not work well enough in optical remote sensing images, which may be due to the mechanism of adversarial perturbations is not suitable. Therefore, an adaptive deformation method (ADM) was proposed to fool the detector into generating wrong predicted bounding boxes. Building upon this, we introduce the Adaptive Deformation Method Iterative Fast Gradient Sign Method (ADM-I-FGSM) and Adaptive Deformation Mechanism Projected Gradient Descent (ADM-PGD) against YOLOv4 and YOLOv5. ADM method can obtain the deformation trend values based on the length-to-width ratio of the prediction box, and the adversarial perturbation trend generated based on these trend values has better adversarial effect. Through experiments, we validate that our approach exhibits a higher adversarial success rate compared to the state-of-the-art methods. We anticipate that our unveiled attack scheme will aid in the evaluation of adversarial resilience of these models.

A Multi-task Adversarial Attack Against Face Authentication

Deep-learning-based identity management systems, such as face authentication systems, are vulnerable to adversarial attacks. However, existing attacks are typically designed for single-task purposes, which means they are tailored to exploit vulnerabilities unique to the individual target rather than being adaptable for multiple users or systems. This limitation makes them unsuitable for certain attack scenarios, such as morphing, universal, transferable, and counter attacks. In this paper, we propose a multi-task adversarial attack algorithm called MTADV that are adaptable for multiple users or systems. By interpreting these scenarios as multi-task attacks, MTADV is applicable to both single- and multi-task attacks, and feasible in the white- and gray-box settings. Furthermore, MTADV is effective against various face datasets, including LFW, CelebA, and CelebA-HQ, and can work with different deep learning models, such as FaceNet, InsightFace, and CurricularFace. Importantly, MTADV retains its feasibility as a single-task attack targeting a single user/system. To the best of our knowledge, MTADV is the first adversarial attack method that can target all of the aforementioned scenarios in one algorithm.

A Novel Security Risk Analysis Using the AHP Method in Smart Railway Systems

Transportation has an essential place in societies and importance to people in terms of its social and economic aspects. Innovative rail systems need to be integrated with developing technologies for transportation. Systemic failures, personnel errors, sabotage, and cyber-attacks in the techniques used will cause a damaged corporate reputation and revenue losses. In this study, cybersecurity attack methods in smart rail systems were determined, and cyber events occurring worldwide through these technologies were analyzed. Risk analysis in terms of transportation safety in smart rail systems was determined by considering the opinions of 10 different experts along with the Analytic Hierarchical Process (AHP) performance criteria. Informatics experts were selected from a group of people with at least 5–15 years of experience. According to these risk analysis calculations, cybersecurity stood out as the most critical security risk at 27.74%. Other risky areas included physical security, calculated at 14.59%, operator errors at 16.20%, and environmental security at 10.93%.

The Ransomware Rollercoaster: A Comprehensive Survey on Attack Methods, Consequences, and the Evolving Landscape

The Ransomware Rollercoaster: A Comprehensive Survey on Attack Methods, Consequences, and the Evolving Landscape