Common Attacks Research Articles

An analytical approach to network with IoT security using NodeMCU access point-prevention technique

This paper focuses on a very pertinent issue in the field of cybersecurity, particularly concerning the increased use of public Wi-Fi and the proliferation of IoT devices. The scenario, where an attacker captures user details through a malicious access point set up through a NodeMCU, is a practical illustration of a Man-in-the-Middle (MitM) attack. These attacks are especially concerning in the context of smart homes and smart cities, where security breaches can lead to severe privacy invasions and disruptions of essential services. The NodeMCU, being a low-cost, open-source IoT platform, is accessible for creating such malicious access points. It can be programmed to mimic legitimate Wi-Fi hotspots to intercept the data of unsuspecting users. This type of attack can enable unauthorized access to sensitive information such as login credentials, personal data, and even control over connected IoT devices. This approach is user-friendly and can significantly reduce the risk of users unknowingly connecting to malicious websites. The contribution of this paper includes a demonstration and analysis of a common network and IoT attack and a novel method for detecting fraudulent URLs, particularly those used in phishing attacks. By addressing these aspects, the paper makes a significant contribution to the field of cybersecurity and helps foster a more secure and resilient network and IoT ecosystem.

Novel approaches to biometric security using enhanced session keys and elliptic curve cryptography

Biometric systems have taken the front seat as having core foundations in 21stcentury digital information technology, biometric systems hold a position therein or authentication processes. But the growing complexity of cyber threats requires greater security practices. This paper presents an innovative biometric security approach based on the combination of ECC and more advanced session keys. The goal is to strengthen biometric systems against sophisticated cyber threats ensuring fast and effective authentication methods. The proposed approach takes advantage of ECC’s strength in generating secure biometric data along with session keys dynamically to build a more resilient, yet flexible, security model. It is shown on the simulations that security metrics increase significantly in terms of resistance to common cryptographic attacks and data breaches without decreasing the system performance. This study describes not only the current vulnerabilities in biometric security systems towards more secure and reliable authentication systems by the convergence of biometric data to advanced cryptographic techniques.

Identifying risk zones and landscape features that affect common leopard depredation.

Human-wildlife conflict (HWC) is a pressing issue worldwide but varies by species over time and place. One of the most prevalent forms of HWC in the mid-hills of Nepal is human-common-leopard conflict (HLC). Leopard attacks, especially in forested areas, can severely impact villagers and their livestock. Information on HLC in the Gorkha district was scarce, thus making it an ideal location to identify high-risk zones and landscape variables associated with such events. Registered cases were collected and reviewed from the Division Forest Office (DFO) during 2019-2021. Claims from DFO records were confirmed with herders and villagers via eight focus group discussions. To enhance modeling success, researchers identified a total of 163 leopard attack locations on livestock, ensuring a minimum distance of at least 100 meters between locations. Using maximum entropy (MaxEnt) and considering 13 environmental variables, we mapped common leopard attack risk zones. True Skill Statistics (TSS) and area under receiver-operator curve (AUC) were used to evaluate and validate the Output. Furthermore, 10 replications, 1,000 maximum iterations, and 1000 background points were employed during modeling. The average AUC value for the model, which was 0.726 ± 0.021, revealed good accuracy. The model performed well, as indicated by a TSS value of 0.61 ± 0.03. Of the total research area (27.92 km2), about 74% was designated as a low-risk area, 19% as a medium-risk area, and 7% as a high-risk area. Of the 13 environmental variables, distance to water (25.2%) was the most significant predictor of risk, followed by distance to road (16.2%) and elevation (10.7%). According to response curves, the risk of common leopard is highest in the areas between 1.5 to 2 km distances from the water sources, followed by the closest distance from a road and an elevation of 700 to 800 m. Results suggest that managers and local governments should employ intervention strategies immediately to safeguard rural livelihoods in high-risk areas. Improvements include better design of livestock corrals, insurance, and total compensation of livestock losses. Settlements near roads and water sources should improve the design and construction of pens and cages to prevent livestock loss. More studies on the characteristics of victims are suggested to enhance understanding of common leopard attacks, in addition to landscape variables. Such information can be helpful in formulating the best management practices.

SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition

Deep neural networks have empowered accurate device-free human activity recognition, which has wide applications. Deep models can extract robust features from various sensors and generalize well even in challenging situations such as data-insufficient cases. However, these systems could be vulnerable to input perturbations, i.e. adversarial attacks. We empirically demonstrate that both black-box Gaussian attacks and modern adversarial white-box attacks can render their accuracies to plummet. In this paper, we firstly point out that such phenomenon can bring severe safety hazards to device-free sensing systems, and then propose a novel learning framework, SecureSense, to defend common attacks. SecureSense aims to achieve consistent predictions regardless of whether there exists an attack on its input or not, alleviating the negative effect of distribution perturbation caused by adversarial attacks. Extensive experiments demonstrate that our proposed method can significantly enhance the model robustness of existing deep models, overcoming possible attacks. The results validate that our method works well on wireless human activity recognition and person identification systems. To the best of our knowledge, this is the first work to investigate adversarial attacks and further develop a novel defense framework for wireless human activity recognition in mobile computing research.

Optical color fragile watermark based on pixel-free expansion visual cryptography

In recent years, with the continuous development of computer technology, it has brought convenience to people to obtain image information. However, at the same time, the falsification and theft of image information have also emerged, so information security has received increasing attention. When images are used for medicine, military, court, and other purposes, it is necessary to ensure the authenticity and integrity of the image content. Fragile watermarks are used to verify the authenticity and integrity of image content due to their sensitivity to tampering. The watermark information is embedded in the image and integrated with the image. When it is necessary to detect the authenticity and integrity of image information, the extracted watermark can be used to determine whether the image is reliable and complete. Therefore, we propose an optical color fragile watermarking system based on pixel-free expansion visual cryptography. On the one hand, encoding watermark images by using pixel-free expansion visual cryptography avoids pixel expansion issues caused by visual cryptography, allowing for the selection of color host images with the same pixel size as the watermark image in the future, greatly reducing the network bandwidth and storage space occupied during transmission. On the other hand, phase recovery algorithm is used to process the encoded watermark image to obtain phase information for embedding into the host image, further improving the security of the watermark image in an optical way. The feasibility and imperceptibility of the proposed optical color fragile watermark are verified through computer simulation, and its good fragility is verified through a series of simulation attack experiments. It can sensitively detect image tampering in the face of common attacks such as noise pollution, rotation, motion blur processing, filtering, etc.

Smart Collaborative Contract for Endogenous Access Control in Massive Machine Communications

Emerging information technologies have accelerated the construction of the Industrial Internet of Things (IIoT). Unfortunately, the explosive growth of various services has caused serious challenges to access control in terms of scalability and trustworthiness. In this paper, we proposed the smart collaborative contract based endogenous access control mechanism. First, access control procedures running on centralized servers are migrated to distributed equipment located inside the data plane, which can reduce the transmission overhead and improve service capacity. Second, the smart collaborative contract is designed for credibility enhancement since part of attackers may attempt to compromise access control. It covers terminal behavior assessment and service request recording. Experiments conducted in a prototype show that the performance of the proposed scheme is better than the traditional solution and is resistant to common attacks.

Generative Pre-Trained Transformer-Based Reinforcement Learning for Testing Web Application Firewalls

Web Application Firewalls (WAFs) are widely deployed to protect key web applications against multiple security threats, so it is important to test WAFs regularly to prevent attackers from bypassing them easily. Machine-learning-based black-box WAF testing is gaining more attention, though existing learning-based approaches have strict requirements on the source and scale of payload data and suffer from the local optimum problem, limiting their effectiveness and practical application. We propose GPTFuzzer, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">practical</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">effective</i> generation-based approach to test WAFs by generating attack payloads token-by-token. Specifically, we fine-tune a Generative Pre-trained Transformer language model with reinforcement learning to make GPTFuzzer have the least restrictions on payload data and thus more applicable in practice, and we use reward modeling and KL-divergence penalty to improve the effectiveness of our approach and mitigate the local optimum issue. We implement GPTFuzzer and evaluate it on two well-known open-source WAFs against three kinds of common attacks. Experimental results show that GPTFuzzer significantly outperforms state-of-the-art approaches, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> ML-Driven and RAT, finding up to 7.8× (3.2× on average) more bypassing payloads within 1,250,000 requests, or finding out all bypassing payloads using up to 8.1× (3.3× on average) fewer requests.

A Lightweight and Secure Communication Protocol for the IoT Environment

Ensuring secure communications for the Internet of Things (IoT) systems remains a challenge. Due to exacting resource limitations of computing, memory, and communication in IoT environments, communication schemes based on asymmetric cryptographic systems can be challenging to deploy. An alternative is to deploy symmetric encryption schemes based on pre-shared keys. However, there are also challenges in designing such schemes and examples include how to achieve an optimal trade-off between security and performance levels while meeting resource consumption requirements. Hence, this paper presents a lightweight key synchronization update algorithm, which is then used as a building block in our proposed lightweight secure communication protocol. The security of the protocol is analyzed to show that it can resist common attacks, such as replay attacks, and man-in-the-middle attacks. We then use Tamarin, a widely accepted security protocol verification tool, for formal verification. In addition, we evaluate the randomness and computational performance of the lightweight key synchronization update algorithm and demonstrate that it outperforms other schemes. We also evaluate the performance of the protocol, in terms of computational and communication costs, to demonstrate utility.

Application of Zero-Watermarking Scheme Based on Swin Transformer for Securing the Metaverse Healthcare Data.

The existing medical image privacy solutions cannot completely solve the security problems created by applying the metaverse healthcare system. A robust zero-watermarking scheme based on the Swin Transformer is proposed in this paper to improve the security of medical images in the metaverse healthcare system. This scheme uses a pretrained Swin Transformer to extract deep features from the original medical images with a good generalization performance and multiscale, and binary feature vectors are generated by using the mean hashing algorithm. Then, the logistic chaotic encryption algorithm boosts the security of the watermarking image by encrypting it. Finally, an encrypted watermarking image is XORed with the binary feature vector to create a zero-watermarking, and the validity of the proposed scheme is verified through experimentation. According to the results of the experiments, the proposed scheme has excellent robustness to common attacks and geometric attacks, and implements privacy protections for medical image security transmissions in the metaverse. The research results provide a reference for the data security and privacy protection of the metaverse healthcare system.

Resilient distributed control of islanded microgrids under hybrid attacks

In this paper, a resilient control strategy is proposed to improve the stability of frequency and voltage recovery for the islanded microgrid (MG) under hybrid cyber attacks. To deal with the common false data injection attacks (FDI) and denial of service attacks (DoS) in MGs, the proposed resilient control strategy utilizes the observers to accurately estimate the potential FDI signals on both the sensors and actuators of each distributed generation unit (DG) and reconstruct the unavailable states in the system to enhance the system’s ability actively. The ultimate uniform boundedness (UUB) of the system under hybrid cyber attacks is proved by the Lyapunov stability theory. Finally, an islanded MG system is established in MATLAB/SIMULINK, and multiple scenarios are simulated to verify the effectiveness of the method.

CYBERSECURITY: RESEARCH ON METHODS FOR DETECTING DDOS ATTACKS

This article describes the problem of DDoS attacks, analyzing their nature and consequences. The paper covers common DDoS attack types, such as SYN flood, ICMP flood, UDP flood. Existing methods for detecting attacks from literature are reviewed, including machine learning approaches, including artificial neural networks, support vector machines and decision trees. The paper introduces a decision tree-based machine learning model for the detection of DDoS attacks. The model is trained and tested on a publicly available dataset. The dataset consists of 1,04,345 rows of data, where every row includes 23 features, such as source IP, destination IP, port number, number of bytes transferred from the switch port, etc. A similar set of characteristics can be obtained on a real network hardware using simple calculations, which makes it possible to approximate the model evaluation to real operating conditions. SYN flood, ICMP flood and UDP flood attack types are present in the data, as well as legitimate traffic. To avoid overfitting, only some columns were used, and columns such as IP addresses were discarded. The field “label” in each row of the dataset contains either 0 or 1 where 0 corresponds to legitimate traffic and 1 to malicious one. The problem of DDoS attack detection is therefore formally reduced to the task of binary classification of each row from the dataset. The constructed model achieves an average classification accuracy of 0.94 with a standard deviation at the level of 0.06 in detecting the above mentioned types of attacks. To objectively assess the effectiveness of the model and avoid distortion of the results, stratified 5-fold cross-validation was used. The developed model can be applied in the real world network hardware to filter malicious packets or as a tool for warning the administrator about an attack. This research advances cybersecurity by enhancing DDoS attack detection.

Dynamic event-triggered resilient controller design for discrete-time singular Markov jump CPSs under hybrid cyber-attacks

In this article, the hybrid cyber-attacks for singular Markov jump systems (MJSs) are mainly solved by designing the asynchronous mixed H∞ and passive controller. A kind of dynamic event-triggered (ETM) based strategy is adopted to maximize the savings of network bandwidth resources. And the hybrid cyber-attacks including two common attacks, the spoofing attack and the denial of service (DoS) attack are considered, which are assumed to be mutually independent and meet Bernoulli distribution. The singular MJSs under cyber-attacks are converted into the singular Markov jump time-delay systems. Then, the criteria of stochastically stable for the closed-loop singular MJSs meeting the mixed H∞ and passive performance are obtained by linear matrix inequalities (LMIs). And by optimizing the obtained LMIs, the gains of asynchronous resilient controller are further derived, and the weighting matrices of dynamic event-triggered based strategy are also gained. Eventually, the feasibility of the offered way is verified by an industrial example of DC motor control system.

Novel chaotic image cryptosystem using dynamic DNA coding

This study aims to solve the problem of small key space in image cryptosystems based on logistic mapping. First, a new one-dimensional (1D) chaotic system, with a wide continuous chaotic interval, a large Lyapunov exponent and obvious chaotic characteristics are presented. Subsequently, a novel image encryption algorithm based on the new 1D chaotic system and dynamic DNA encoding is designed. Compared with other DNA coding methods, the proposed image encryption algorithm encodes chaotic sequences and ensures that the sequence elements at different positions correspond to different DNA coding schemes. This will help to overcome the fixity of DNA coding and make the proposed dynamic DNA coding easy to operate and implement. Finally, the cipher image is obtained by scrambling and bit XOR operation based on the chaotic sequences. The fixed DNA coding method is compared with other chaotic image encryption schemes, and the experimental results indicate that the image encryption algorithm has higher security and can resist common attacks.

Developing a reliable route protocol for mobile self-organization networks

Mobile ad hoc networks (MANETs), which correspond to a novel wireless technology, are widely used in Internet of Things (IoT) systems such as drones, wireless sensor networks, and military or disaster relief communication. From the perspective of communication and data collection, the success rate of collaborations between nodes in mobile ad hoc networks and reliability of data collection mainly depend on whether the nodes in the network operate normally, namely, according to the established network rules. However, mobile ad hoc networks are vulnerable to attacks targeting transmission channels and nodes owing to their dynamic evolution, openness, and distributed characteristics. Therefore, during the network operation, it is necessary to classify and detect the behavior and characteristics of each node. However, most existing research only analyzes and considers responses against a single or small number of attacks. To address these issues, this article first systematically analyzed and classified common active attacks in MANETs. Then, a node trust model was proposed based on the characteristics of various attacks; subsequently, a new secure routing protocol, namely, TC-AODV, was proposed. This protocol has minimal effect on the original communication dynamics and can effectively deal with Packet drop, wormhole, Session hijacking, and other main attacks in MANETs. The NS3 simulation results show that the proposed routing protocol attains good transmission performance, can effectively identify various attacks and bypass malicious nodes, and securely complete the communication process.

Quantum Secret Reconstruction

AbstractIn addition to secret splitting, secret reconstruction is another important component of secret sharing. In this paper, the first quantum secret reconstruction protocol is proposed to protect the reconstruction process. Before the protocol, a classical secret is divided into multiple shares that are distributed to the shareholds, respectively. In the presented protocol, the dealer utilizes the secret to encrypt a privacy quantum state, and sends the encrypted state to a combiner who is chosen by the dealer within the shareholders. With the help of other shareholders, the combiner utilizes the properties of cluster states to recover the privacy quantum state. It is shown that the proposed protocol is secure against several common attacks, including external and internal attacks. Compared with classical secret reconstruction protocols, this protocol not only achieves information‐theoretic security of all shares, but is also more efficient due to the reduced distribution and computation cost. To demonstrate the feasibility of the protocol, a corresponding simulation quantum experiment is conducted on the IBM Q. Furthermore, in conjunction with quantum fingerprinting, it can be directly applied to achieve the task of quantum multiple secrets sharing, since the classical shares can be reused in the proposed protocol.

Sinkhole Attack Defense Strategy Integrating SPA and Jaya Algorithms in Wireless Sensor Networks.

A sinkhole attack is characterized by low difficulty to launch, high destructive power, and difficulty to detect and defend. It is a common attack mode for wireless sensor networks. This paper proposes a sinkhole attack detection and defense strategy integrating SPA and Jaya algorithms in wireless sensor networks (WSNs). Then, combined with the SPA trust model, the trust values of suspicious nodes were calculated, and the attack nodes were detected. The Jaya algorithm was adopted to avoid the attacked area so that nodes can find the route to communicate with the real Sink, and attack nodes are isolated in the network to improve the capabilities of network directional defense. The simulation results show that the improved detection algorithm can effectively detect malicious nodes in the network, and the defense strategy implemented in the attacked area can improve the packet delivery rate, reduce network delay and energy consumption, and improve the security and reliability of wireless sensor networks.

SEV-Step A Single-Stepping Framework for AMD-SEV

The ever increasing popularity and availability of Trusted Execution Environments (TEEs) had a stark influence on microarchitectural attack research in academia, as their strong attacker model both boosts existing attack vectors and introduces several new ones. While many works have focused on Intel SGX, other TEEs like AMD SEV have recently also started to receive more attention. A common technique when attacking SGX enclaves is single-stepping, where the system’s APIC timer is used to interrupt the enclave after every instruction. Single-stepping increases the temporal resolution of subsequent microarchitectural attacks to a maximum. A key driver in the proliferation of this complex attack technique was the SGX-Step framework, which offered a stable reference implementation for single-stepping and a relatively easy setup. In this paper, we demonstrate that SEV VMs can also be reliably single-stepped. To lay the foundation for further microarchitectural attack research against SEV, we introduce the reusable SEV-Step framework. Besides reliable single-stepping, SEV-Step provides easy access to common attack primitives like page fault tracking and cache attacks against SEV. All features can be used interactively from user space. We demonstrate SEV-Step’s capabilities by carrying out an end-toend cache attack against SEV that leaks the volume key of a LUKS2-encrypted disk. Finally, we show for the first time that SEV is vulnerable to Nemesis-style attacks, which allow to extract information about the type and operands of single-stepped instructions from SEV-protected VMs.

Simulation of Vehicular Bots-Based DDoS Attacks in Connected Vehicles Networks

Connected vehicles are more vulnerable to attacks than wired networks since they involve rapid mobility, continuous data flow across connected nodes, and dynamic network design in a distributed network environment. Distributed Denial of Service (DDOS) is one of the most common and dangerous security attacks on connected vehicle networks. Attackers can remotely control malicious nodes that are programmed to attack other nodes known. The compromised nodes are known as botnets, which will constantly flood the target nodes with User Datagram Protocol (UDP) packets, disrupting the target nodes data flow and operation. Hence, the goal of this research is to create and simulate a vehicular bot-based Distributed Denial of Service (DDoS) assault in connected vehicle networks. A simulation-based methodology is implemented to observe the impact of the number of bots, DDoS rate, and maximum bulk packet size on network performance. Using the NS-3 network simulator, 73 random mobile vehicle nodes with up to 100 vehicle bots were simulated, and the results are discussed. Regardless of the computational constraints, the findings from this study adds to understanding the risks and problems associated with data transmission by analyzing the impact of vehicular bot-based DDoS attacks on connected vehicle performance. Doi: 10.28991/HIJ-2023-04-04-014 Full Text: PDF

Evaluating RSA encryption: Primality testing, pollards algorithms, and security challenges

In the dynamic realm of cryptography, Rivest, Shamir, and Adleman (RSA) encryption stands as a pivotal element in ensuring secure communications. This analysis offers a detailed exploration of RSA encryption, emphasizing critical aspects such as primality testing, the intricacies of Pollards factorization algorithms, and the overarching security challenges intrinsic to this prevalent encryption paradigm. Delving deep into the heart of RSA, the research underscores the paramount role of primality testing in the RSA key generation process and critically evaluates the efficiency and reliability of diverse primality test methodologies. Moreover, it navigates the potential pitfalls introduced by Pollards algorithms and ponders their consequential implications for RSAs security matrix. Beyond these technicalities, the analysis brings to the forefront a spectrum of security challenges besieging RSA. This encompasses nuances like vulnerabilities arising from diminutive private keys, pitfalls linked to common modulus attacks, and susceptibilities stemming from cache timing discrepancies. By illuminating both the robust facets and inherent vulnerabilities of RSA encryption, this scholarly work elevates the current narrative on cryptographic security. It accentuates the perpetual necessity for meticulous scrutiny and agile adaptability in the quest to shield sensitive digital information in our progressively interconnected world.

Two (w, ω, n) weighted threshold quantum secret sharing schemes on d-level single quantum systems

Quantum secret sharing occupies an important position in quantum cryptography. In this paper, we propose two weighted threshold quantum secret sharing schemes on d-level single quantum systems that share classical information and quantum states, respectively. The dealer Alice distributes the secret share to each participant using the Chinese Remainder Theorem(CRT) for a polynomial ring, which can realize secret sharing among participants with different weights. In classical information sharing, the dealer can share multiple secrets in one round of the protocol and the participants can verify the correctness of the recovered secrets. In quantum state sharing, the dealer can share multiple single quantum states in one round of protocol. Finally, we demonstrate that our scheme is resistant to common external and internal attacks and compare it with related schemes.