Common Attack Methods Research Articles

Intelligent Threat Detection And Response Systems For Safeguarding Cloud-Hosted Electronic Health Records From Cyber Attacks

As more people use cloud-based electronic health record (EHR) systems, they make healthcare better, but they also make it easier for cybercriminals to attack. This article describes a system that uses artificial intelligence (AI) and machine learning (ML) to intelligently watch cloud-hosted EHR environments for bad behaviour, find cyberattacks, and automatically take the right steps to stop them. Using supervised machine learning models that have been trained on known threat indicators, the suggested framework constantly looks at log and system data. As soon as an attack is found, established containment and mitigation steps are carried out naturally to lower the harm. The test results show that the framework can correctly identify common EHR attack methods like ransomware and data theft, as well as quickly and effectively protect private patient data

A New Semi-Quantum Two-Way Authentication Protocol between Control Centers and Neighborhood Gateways in Smart Grids.

To address the potential threat to the power grid industry posed by quantum computers and ensure the security of bidirectional communication in smart grids, it is imperative to develop quantum-safe authentication protocols. This paper proposes a semi-quantum bidirectional authentication protocol between a control center (CC) and a neighboring gateway (NG). This method uses single photons to facilitate communication between the CC and the NG. Security analysis demonstrates that the protocol can effectively resist common attack methods, including double CNOT attacks, impersonation attacks, interception-measurement-retransmission attacks, and entanglement-measurement attacks. Comparisons with other protocols reveal that this protocol has significant advantages, making it more appealing and practical for real-world applications. Finally, by simulating the protocol on the IBM quantum simulator, this protocol not only validates the theoretical framework but also confirms the practical feasibility of the protocol.

DIPA: Adversarial Attack on DNNs by Dropping Information and Pixel-Level Attack on Attention

Deep neural networks (DNNs) have shown remarkable performance across a wide range of fields, including image recognition, natural language processing, and speech processing. However, recent studies indicate that DNNs are highly vulnerable to well-crafted adversarial samples, which can cause incorrect classifications and predictions. These samples are so similar to the original ones that they are nearly undetectable by human vision, posing a significant security risk to DNNs in the real world due to the impact of adversarial attacks. Currently, the most common adversarial attack methods explicitly add adversarial perturbations to image samples, often resulting in adversarial samples that are easier to distinguish by humans. To address this issue, we are motivated to develop more effective methods for generating adversarial samples that remain undetectable to human vision. This paper proposes a pixel-level adversarial attack method based on attention mechanism and high-frequency information separation, named DIPA. Specifically, our approach involves constructing an attention suppression loss function and utilizing gradient information to identify and perturb sensitive pixels. By suppressing the model’s attention to the correct classes, the neural network is misled to focus on irrelevant classes, leading to incorrect judgments. Unlike previous studies, DIPA enhances the attack of adversarial samples by separating the imperceptible details in image samples to more effectively hide the adversarial perturbation while ensuring a higher attack success rate. Our experimental results demonstrate that under the extreme single-pixel attack scenario, DIPA achieves higher attack success rates for neural network models with various architectures. Furthermore, the visualization results and quantitative metrics illustrate that the DIPA can generate more imperceptible adversarial perturbation.

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.

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.

LWARX: Lightweight ARX white-box cipher for satellite communications

Satellite communication links are vulnerable to attacks due to the lack of necessary security protection and can be considered as a white-box environment. In a white-box environment, cryptanalysts can access the intermediate processes of the algorithm and even manipulate or change the operating environment. Cryptanalysts can obtain keys or tamper with important data in many ways, which makes data communication unsecured. To solve the problem, a lightweight white-box cipher over Addition/Rotation/XOR (ARX) structure (LWARX) is proposed, which has 48 rounds of iterations through an unbalanced Feistel structure. On this basis, some of its linear operations are represented as lookup tables, and a secure external coding method is combined to complete the white-box implementation. The design and white-box implementation of the algorithm has resulted in a lighter and more rational structure, and still provides sufficient obfuscation of the data even when the S-box is discarded. The test results show that the average encryption speed is 37.53 Kbps, and average encryption speed after white-box implementation is 30.05 Kbps. And it can resist various attacks. The security of the algorithm against common attack methods such as differential analysis, linear analysis, code lifting attacks and BGE attacks is given in the paper with security analysis and specific values. This scheme balances computing efficiency and security, takes up little space, can be applied to scenarios with limited hardware and software resources, and broadens the application area of white-box cryptography.

Dynamic S-Box Construction Using Mordell Elliptic Curves over Galois Field and Its Applications in Image Encryption

Elliptic curve cryptography has gained attention due to its strong resilience against current cryptanalysis methods. Inspired by the increasing demand for reliable and secure cryptographic methods, our research investigates the relationship between complex mathematical structures and image encryption. A substitution box (S-box) is the single non-linear component of several well-known security systems. Mordell elliptic curves are used because of their special characteristics and the immense computational capacity of Galois fields. These S-boxes are dynamic, which adds a layer of complexity that raises the encryption process’s security considerably. We suggest an effective technique for creating S-boxes based on a class of elliptic curves over GF(2n),n≥8. We demonstrate our approach’s robustness against a range of cryptographic threats through thorough examination, highlighting its practical applicability. The assessment of resistance of the newly generated S-box to common attack methods including linear, differential, and algebraic attacks involves a thorough analysis. This analysis is conducted by quantifying various metrics such as non-linearity, linear approximation, strict avalanche, bit independence, and differential approximation to gauge the S-box’s robustness against these attacks. A recommended method for image encryption involves the use of built-in S-boxes to quickly perform pixel replacement and shuffling. To evaluate the efficiency of the proposed strategy, we employed various tests. The research holds relevance as it can provide alternative guidelines for image encryption, which could have wider consequences for the area of cryptography as a whole. We believe that our findings will contribute to the development of secure communication and data protection, as digital security is becoming increasingly important.

A Cross-Plane Color Image Encryption Algorithm Based on 1D-SLM

With the rapid development of 5G technology, it has become fast and easy for people to transmit information on the Internet. Digital images can express information more intuitively, so transmitting information through images has excellent applications. This paper uses a new chaotic system called 1D-Sin-Logistic-Map (1D-SLM). 1D-SLM has two control parameters, which can provide larger parameter space, and the parameter space in the chaotic state is continuous. Through Lyapunov exponent analysis (LE), bifurcation diagrams analysis, spectral entropy analysis (SE), and 0-1 test, it is verified that 1D-SLM has complex dynamic behavior and is very suitable for cryptography. Compared with other 1D chaotic systems, the 1D-SLM has a larger Lyapunov exponent (LE) and spectral entropy (SE). For color image encryption algorithms, only relying on chaotic mapping is not enough to ensure security. So combined with 1D-SLM, we design a color image encryption algorithm, which is implemented by plane expansion, which reduces the correlation between the three channels of color images. The experimental results show that the proposed cross-plane color image encryption algorithm is safe and resistant to common attack methods.

Predictions of Cybersecurity Experts on Future Cyber-Attacks and Related Cybersecurity Measures

The Internet interconnections’ exponential growth has resulted in an increase in cyber-attack occurrences with mostly devastating consequences. Malware is a common tool for performing these attacks in cyberspace. The malefactors would either exploit the present weaknesses or employ the distinctive characteristics of the developing technologies. The cybersecurity community should increase their knowledge on the types and arsenals of cyber-attacks, and security measures against cyber-attacks should be in place as well. Also, advanced and effective malware defense mechanisms should be established. Hence, this study reviews cyber-attack types, measures and security precautions, and professional extrapolations on cyber-attacks future and the associated security measures. Semi-structured interviews were performed, involving five IT managers and nine Cybersecurity Consultants, to obtain the data. The study findings demonstrate prevention as key for data breach risk prevention. Knowledge of common attack methods and the use of cybersecurity software can facilitate individuals and organizations in thwarting hackers and in preserving their data privacy. Two-factor authorization by consumers and new back-end security protocols and security methods, including Artificial intelligence (AI) application, will encumber hacking attempts.

A Novel Hyperchaotic 2D-SFCF with Simple Structure and Its Application in Image Encryption.

In this paper, a novel image encryption algorithm is proposed based on hyperchaotic two-dimensional sin-fractional-cos-fractional (2D-SFCF), called sin-fractional-cos-fractional image-encryption (SFCF-IE). The 2D-SFCF is constructed from two one-dimensional cosine fractional (1-DCFs), and it has a more complex chaotic behavior with a larger parameter space than one-dimensional chaotic systems. Compared with the two-dimensional (2D) chaotic system, the 2D-SFCF has a simple structure, and the parameter space in the chaotic state is continuous, which is beneficial to generating the keystream in the cryptosystem. Therefore, in the novel image encryption algorithm, we use the 2D-SFCF to generate the keystream of the cryptosystem. The encryption algorithm is a process of scrambling and diffusion. Different from common diffusion methods, the diffusion starting position of the SFCF-IE is randomly generated, enhancing the algorithm’s security. Simulation experiments show that the image encrypted by this algorithm has better distribution characteristics and can resist common attack methods.

A Novel Method for Moving Laterally and Discovering Malicious Lateral Movements in Windows Operating Systems: A Case Study

Lateral movement is a pervasive threat because modern networked systems that provide access to multiple users are far more efficient than their non-networked counterparts. It is a well-known attack methodology with extensive research conducted investigating the prevention of lateral movement in enterprise systems. However, attackers use increasingly sophisticated methods to move laterally that bypass typical detection systems. This research comprehensively reviews the problems in lateral movement detection and outlines common defenses to protect modern systems from lateral movement attacks. A literature review outlines techniques for automatic detection of malicious lateral movement, explaining common attack methods utilized by advanced persistent threats and components built into the Windows operating system that can assist with discovering malicious lateral movement. Finally, a novel approach for moving laterally designed by other security researchers is reviewed and studied, an original process for detecting this method of lateral movement is proposed, and the application of the detection methodology is also expanded.

A New Method for WebShell Detection Based on Bidirectional GRU and Attention Mechanism

With the rapid development of hacker technology, network security issues have become increasingly serious. Uploading WebShell is one of the most common attack methods used by network intruders. WebShell escape technology is changing with each passing day, and the traditional method based on feature matching is difficult to accurately detect. In order to detect WebShell more accurately and mitigate the threat caused by WebShell attacks, a WebShell detection method combining bidirectional GRU (gated recurrent unit) and attention mechanism is proposed for the first time. First, the sample is preprocessed to remove useless information such as annotations. Then, the sample is divided into a series of words, the word2vec model is used to obtain the word vector, and finally, the word vector is input into the network for prediction. According to the experimental results, compared with peer methods, the method in this study performs better in performance indicators such as accuracy rate, recall rate, and F1 value. The model not only detects the PHP-type WebShell but also has a good performance on the WebShell written in JSP, ASPX, or ASP languages. The detection accuracy of PHP-type, JSP-type, and ASP-type WebShell reached 99.36%, 99.23%, and 99.87%, respectively, and the recall rate was 98.6%, 99.13%, and 99.56%, respectively.

A Novel Multi-Sample Generation Method for Adversarial Attacks

Deep learning models are widely used in daily life, which bring great convenience to our lives, but they are vulnerable to attacks. How to build an attack system with strong generalization ability to test the robustness of deep learning systems is a hot issue in current research, among which the research on black-box attacks is extremely challenging. Most current research on black-box attacks assumes that the input dataset is known. However, in fact, it is difficult for us to obtain detailed information for those datasets. In order to solve the above challenges, we propose a multi-sample generation model for black-box model attacks, called MsGM. MsGM is mainly composed of three parts: multi-sample generation, substitute model training, and adversarial sample generation and attack. Firstly, we design a multi-task generation model to learn the distribution of the original dataset. The model first converts an arbitrary signal of a certain distribution into the shared features of the original dataset through deconvolution operations, and then according to different input conditions, multiple identical sub-networks generate the corresponding targeted samples. Secondly, the generated sample features achieve different outputs through querying the black-box model and training the substitute model, which are used to construct different loss functions to optimize and update the generator and substitute model. Finally, some common white-box attack methods are used to attack the substitute model to generate corresponding adversarial samples, which are utilized to attack the black-box model. We conducted a large number of experiments on the MNIST and CIFAR-10 datasets. The experimental results show that under the same settings and attack algorithms, MsGM achieves better performance than the based models.

A Highly Stealthy Adaptive Decay Attack Against Speaker Recognition

Speaker recognition based on deep learning is currently the most advanced and mainstream technology in the industry. Adversarial attacks, an emerging and powerful attack against neural network models, were first applied in the image domain and gradually expanded to other domains, also posing serious security problems for speaker recognition. Common gradient-based attack methods such as FGSM, PGD, and MI-FGSM can deceive speaker recognition models with high confidence, yet their carefully crafted adversarial examples suffer from poor stealthiness and are easily perceived by the human ear. To improve the stealthiness of the adversarial examples, this paper proposes a new attack method called the Adaptive Decay Attack (ADA), which is applied to three different scenarios in speaker recognition. The method takes the set number of iterations as the termination condition, automatically adjusts the size of the maximum perturbation according to whether the attack is successful or not, and then uses the decay methods in learning rates such as exponential decay and cosine annealing to continuously reduce the step size. The experimental results show that under the two speaker recognition models x-vector, and i-vector, the proposed attack method improves the stealthiness metrics such as SNR and PESQ by at least 30% and 39%, respectively, compared with the best PGD attack under speaker identification of untargeted attacks. For the speaker identification task with targeted attacks, the average improvement is at least 20% and 25% compared to PGD. For the speaker verification task, the improvement is at least 29.5% and 33.4% compared to PGD. In addition, we also use this attack method for adversarial training to enhance the robustness of the model. Experimental results show that ADA-based adversarial training takes 28.31% less time than PGD-based adversarial training, and its improved robustness is generally superior to PGD-based adversarial training. Specifically, the attack success rate of PGD and ADA methods decreased from 50.88% to 36.47% and 64.74% to 45.82%, respectively.

A Multistage Blockchain-Based Secure and Trustworthy Smart Healthcare System Using ECG Characteristic

With the rapid development of advanced biomedical sensors, the Internet of Things, and modern wireless communication technologies, smart healthcare systems provide feasible solutions to the problems of population aging and telemedicine services. However, physiological information involves personal privacy, and the data security concern during the transmission of information on public channels has become a critical issue that has restricted the wider acceptance of smart healthcare systems. In this article, a secure and trustworthy smart healthcare system interoperating with wireless body area networks based on multistage blockchain is proposed. The security scheme provides a completely secure and trustworthy environment covering the entire data flow from the front-end to the back-end. The evaluation results show that the encryption scheme based on piecewise linear chaotic map can resist common attack methods and reduce encryption time. The system has the advantages of low complexity for encryption scheme, and larger capacity and efficiency for the blockchain-based data transmission and storage system.

Investigation of Cyber-Attack Methods and Measures in Smart Grids

Smart grids have been developing rapidly with the development of technologies in recent years. In the field of critical infrastructures such as natural gas, electricity, water, and energy systems, which are among the smart grids, its use has been increasing in Turkey and all over the world in recent years. With the increase in the use of smart grids, security problems have also gained importance. Cybersecurity attacks against these networks are increasing every year. In this research study, smart cities, smart networks, and the most common cybersecurity attack methods against these systems were investigated. The studies on security in smart grids in the last 10 years have been examined and presented in a table. As a result of these researches, cyber-attacks that are experienced and likely to occur in smart networks were determined. The 20 most used cyber-attack methods were analyzed. In addition, the measures that can be taken against cyber-attacks are analyzed in detail. In addition, in this article, studies on security problems in smart grids are examined and evaluated.

A cooperative DDoS attack detection scheme based on entropy and ensemble learning in SDN

In order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.

Bit-level image encryption algorithm based on BP neural network and gray code

In recent years, people have put forward various image encryption algorithms based on pixel level. In fact, bit level encryption has better effect than pixel level encryption. Therefore, this paper proposes a new bit-level image encryption algorithm based on Back Propagation (BP) neural network and Gray code. Firstly, the plaintext image is conversioned into binary image, then, the hyperchaotic Lorentz system is used to generate two sets of chaotic sequences for the Gray code bit-level permutation operation to generate the permutation matrix. Secondly, the permutation matrix is converted into a bit matrix reverse order output to generate a diffusion matrix. Finally, the algorithm uses a BP neural network composed of Logistic map and Piece-Wise Linear Chaotic (PWLCM) map to generate a key stream. The key stream is xored with the diffusion matrix to generate a ciphertext matrix. The experimental results show that the algorithm improves the encryption efficiency, has good security and can resist common attack methods.

A novel colour image encryption based on fractional order Lorenz system

A colour image encryption algorithm based on fractional Lorenz system has been developed in this paper. Firstly, the colour image has been divided into three layers: R, G, and B, and Arnold map has been used for scrambling the three layers separately, and the plaintext pixel has been selected as the scrambling parameter. The Tent map has been used to associate the plain text to generate the initial values of the fractional Lorenz system, which has been iterated by the system to generate a pseudo-random sequence that can be used for two pixel diffusions. The first pixel diffusion is to diffuse the pixels of each layer to the other two layers, and the second pixel diffusion is to add modulus diffusion inside the layer. Simulation results illustrate that the plaintext image is encrypted into a meaningless noise image. The superior performance of proposed algorithm has been proved by the key space analysis, histogram analysis, correlation test, sensitivity analysis and information entropy analysis, and it can resist various common attack methods.

Network forensics analysis on email scam botnet

Using botnet to send spam is a common attack method of cyber blackmailers and extortionist. In recent years, with the widespread application of blockchain, a new type of extortion scam spam using bitcoin to achieve anonymous transfer have gradually emerged, which poses a great threat to cyber security. This paper aims to a university email system for spam botnet detection. We design a network forensics framework, which can identify extortion scam email and spam-sending botnet. Furthermore, this framework can also analyze the bitcoin money laundering network used by attacker. Experiment on real-world datasets shows that compared to some classic spam filtering models, our method has a higher recall rate on extortion scam email, while provides further analysis on botnet cluster and money laundering network.