Static Attack Research Articles

The Mesa Security Model 2.0: A Dynamic Framework for Mitigating Stealth Data Exfiltration

The rising complexity of cyber threats calls for a comprehensive reassessment of current security frameworks in business environments. This research focuses on Stealth Data Exfiltration (SDE), a significant cyber threat characterized by covert infiltration, extended undetectability, and unauthorized dissemination of confidential data. Our findings reveal that conventional defense-in-depth strategies often fall short in combating these sophisticated threats, highlighting the immediate need for a shift in information risk management across businesses. The evolving nature of cyber threats, driven by advancements in techniques, such as social engineering, multi-vector attacks, and the emergence of Generative AI, underscores the need for robust, adaptable, and comprehensive security strategies. As we continue to navigate this complex landscape, it is crucial that we stay ahead of the curve, anticipating potential threats, and continually updating our defenses to protect against them. We propose a shift from traditional perimeter-based, prevention-focused models, which depend on a static attack surface, to a more dynamic framework that prepares for inevitable breaches. This suggested model, known as ‘MESA 2.0 Security Model’, prioritizes swift detection, immediate response, and ongoing resilience, thereby enhancing an organization’s ability to promptly identify and neutralize threats, significantly reducing the consequences of security breaches. This study suggests that businesses adopt a forward-thinking and adaptable approach to security management, which is crucial for staying ahead of the ever-changing cyber threat landscape. By shifting focus from merely preventing incidents to effectively managing them, organizations can better safeguard their vital digital assets against the increasingly complex tactics used by contemporary cyber adversaries. This study provides valuable insights and a solid strategic framework that aims to steer the development of future security practices and policies to effectively address and mitigate advanced persistent threats.

Analysis of land system resilience: Static and dynamic perspectives

Land system face multiple threats due to the long-term effects of climate change, global geopolitical and economic. Our research simulates the static and dynamic resilience of land systems under increasing socioeconomic pressures and frequent disturbances from natural disasters. We use an improved load-capacity model to simulate the cascading failure effects of agricultural land systems. The study finds that parameters change after deliberate attacks much earlier than random attacks that cause network collapse. The core nodes in the trade structure carry most of the functions, and such regions and land types should be focused on. Cascading failures make the network more vulnerable due to the transmissibility of pressure. Static attacks can cause network collapse when more than 30% of nodes are attacked, while dynamic attacks only need to attack 10% of nodes. According to parameter optimization, the node capacity should be maximized while reducing the gap in the initial load of the nodes, thereby weakening the cascading effect. To ensure the reliability of the results, the article conduct robustness tests. Finally, in order to develop sustainable land systems, the article propose that it is necessary to assess resilience, identify key elements, effectively monitor land systems, and develop sound land protection plans.

Identifying influential spreaders in complex networks based on density entropy and community structure

In recent years, exploring the relationship between community structure and node centrality in complex networks has gained significant attention from researchers, given its fundamental theoretical significance and practical implications. To address the impact of network communities on target nodes and effectively identify highly influential nodes with strong propagation capabilities, this paper proposes a novel influential spreaders identification algorithm based on density entropy and community structure (DECS). The proposed method initially integrates a community detection algorithm to obtain the community partition results of the networks. It then comprehensively considers the internal and external density entropies and degree centrality of the target node to evaluate its influence. Experimental validation is conducted on eight networks of varying sizes through susceptible–infected–recovered (SIR) propagation experiments and network static attack experiments. The experimental results demonstrate that the proposed method outperforms five other node centrality methods under the same comparative conditions, particularly in terms of information spreading capability, thereby enhancing the accurate identification of critical nodes in networks.

Identification of Critical Links Based on Electrical Betweenness and Neighborhood Similarity in Cyber-Physical Power Systems.

Identifying critical links is of great importance for ensuring the safety of the cyber-physical power system. Traditional electrical betweenness only considers power flow distribution on the link itself, while ignoring the local influence of neighborhood links and the coupled reaction of information flow on energy flow. An identification method based on electrical betweenness centrality and neighborhood similarity is proposed to consider the internal power flow dynamic influence existing in multi-neighborhood nodes and the topological structure interdependence between power nodes and communication nodes. Firstly, for the power network, the electrical topological overlap is proposed to quantify the vulnerability of the links. This approach comprehensively considers the local contribution of neighborhood nodes, power transmission characteristics, generator capacity, and load. Secondly, in communication networks, effective distance closeness centrality is defined to evaluate the importance of communication links, simultaneously taking into account factors such as the information equipment function and spatial relationships. Next, under the influence of coupled factors, a comprehensive model is constructed based on the dependency relationships between information flow and energy flow to more accurately assess the critical links in the power network. Finally, the simulation results show the effectiveness of the proposed method under dynamic and static attacks.

Stabilization of Linear Cyber-Physical Systems Against Attacks via Switching Defense

This paper studies cyber-physical systems modeled with linear dynamics subject to attacks on the parameters. The attacker knows at all times the defense employed and injects a destabilizing piecewise Lipschitz time-varying attack signal. The defender does not know the specific attack and aims to preserve system stability. We propose a partitioning strategy for the set of possible attacks that generates a finite collection of candidate defenses such that, for each member of the partition, there is a defense that stabilizes the system with respect to all static attack signals belonging to it. The defender then implements a mechanism that switches among the candidate defenses based on the evaluation of a Lyapunov-based criterion that determines whether the current defense is stabilizing. We characterize the properties of the switched time-varying system with delay, the latter arising from the interval between the switch-triggering events and their actual implementation. Our analysis provides a tolerance on the implementation delay that prevents the defense signal from constantly switching. In addition, we also identify a condition on the switching frequency that ensures global exponential stability. Simulations of the proposed switched defense mechanism illustrate its performance and advantages over static defenses.

An efficient trust-based decision-making approach for WSNs: Machine learning oriented approach

Wireless Sensor Networks (WSNs) are often used for critical applications where trust and security are of paramount importance. Trust evaluation is one of the key mechanisms to ensure the security and reliability of WSNs. Traditional trust evaluation schemes rely on fixed, predetermined thresholds, or rules and static attack models, which may not be suitable for all situations such as dynamic and heterogeneous network environments with new and unknown attack scenarios as well as have several problems such as limited security and scalability, limited accuracy, incomplete coverage, lack of adaptability that can limit their effectiveness. Machine Learning (ML) has been shown to be an effective tool for trust evaluation in WSNs, offering several benefits over existing schemes such as greater adaptability, scalability, and accuracy since ML algorithms can analyze and learn from the data collected in real-time from multiple sources (sensor readings, network traffic, and user behavior) enabling them to dynamically adjust their decision-making criteria based on the current network conditions. Trust-aware ML-based security mechanisms achieve safety and efficient decision-making by reducing uncertainty and risk to accomplish real-world tasks. This paper presents a Machine Learning (ML)-based trust evaluation model in the unattended autonomous WSN environment to achieve reliability, adaptability, scalability, and accuracy by generating quick and reliable trust values dynamically. The proposed machine learning algorithm extracts various trust features such as Co-Location Relationship (CLR), Co-Work Relationship (CWR), Cooperativeness-Frequency-Duration (CFD), and Reward (R) to obtain a robust trust rating of sensor devices and predict future misbehavior. These trust features are combined to generate a final trust rating before making any decision about the reliability of any sensor device. Moreover, the projected trust model (ETDMA) integrate direct communication trust and indirect trust with the help of a logical time window that periodically records the trustworthy and suspicious interactions. Simulation experiments exhibit the effectiveness of the proposed trust evaluation method in terms of change in trust values, malicious nodes detection (94%), FNR (0.9%), F1-Score (0.6), and accuracy (92%) in the presence of 50 malicious nodes.

Event‐triggered attack detection and state estimation based on Gaussian mixture model

AbstractUnder the framework of event‐triggered transmission mechanism, the problem of attack detection and state estimation of multi‐sensor linear time‐invariant systems under static attacks is considered. First, for each transmission channel, the sensor collects measurement information according to an event‐triggered mechanism to reduce unnecessary energy consumption. Then, inspired by the clustering algorithm in machine learning, a detection mechanism based on Gaussian mixture model, which can set a confidence level for the measurement of each sensor is proposed. Finally, centralised data fusion is performed according to the results of attack detection and event‐triggered judgement to realise remote state estimation. A numerical example proves that the proposed algorithm can locate the damaged sensor, save the network transmission bandwidth under the premise of ensuring accuracy and efficiency of sensor estimation.

Research on Embedded Security Control for Smart Home Based on Improved IBE Algorithm

A smart home control system is a comprehensive control system that cross-fuses electronic technology, etc. In the process of data communication in smart home systems, a communication security system based on the upgraded IBE algorithm is created to address network security issues such as hostile assaults, privacy leaks, and identity theft. The system adopts the IBE algorithm for the encryption design, which can effectively resist security threats such as static password loss and pseudo-server attacks, improve system confidentiality, and make data transmission in the smart home system more secure. The key management, it can be argued, is the foundation of cryptographic security and plays an essential role in encryption behavior.

Resilient Penalty Function Method for Distributed Constrained Optimization under Byzantine Attack

Distributed optimization algorithms have the advantages of privacy protection and parallel computing. However, the distributed nature of these algorithms makes the system vulnerable to external attacks. This paper presents two penalty function based resilient algorithms for constrained distributed optimization under static and dynamic attacks. The objective function of the optimization problem is extended to nonsmooth ones and the convergence of the proposed algorithms in this case are proved under some mild conditions. Simulation experiments are performed and compared with some existing resilient primal-dual optimization algorithms using median-based mean estimator. For static attack, the proposed algorithm has better performance and faster convergence rate in the simulation experiments. For dynamic attack, the proposed algorithm has better performance and robustness in the simulation experiments, which illustrate that the proposed algorithms are more effective.

PROSPECTIVE DIRECTIONS OF TRAFFIC ANALYSIS AND INTRUSION DETECTION BASED ON NEURAL NETWORKS

The main problems of the network security at the moment are the difficulty of combining existing systems from different vendors and ensuring their stable interaction with each other. Intrusion detection is one of the main tasks of a proper level of network security, because it is they who notify about attacks and can block them when detected. Today, monitoring and analyzing the quality of traffic in the network, detecting and preventing intrusions is helped by IDS systems and IDS systems of the new generation IPS. However, they have been found to have certain drawbacks, such as the limitations of signature-based systems, as static attack signatures limit the flexibility of systems and pose the threat of missing detection of other attacks not entered into the database. This gives rise to the creation of more and more new hybrid systems, but the challenge is to ensure their efficiency and flexibility, which is helped by the use of artificial neural networks (ANNs). This paper considers ways to improve the use of the convolutional neural network model itself by means of modified processing, data analysis, the use of Softmax and FocalLoss functions to avoid the problem of uneven distribution of sample data by the ratio of positive and negative samples, based on training using the KDD99 dataset. The article provides practical examples of possible integration of IDS and ANN systems. Combinations of backpropagation neural networks and radiant-basis neural networks, which showed some of the best results and proved that the combination of networks helps to increase the efficiency of these systems and create a flexible network adjusted to the needs and requirements of the systems. Although the use of artificial neural networks is a popular tool, it has identified a number of disadvantages: critical dependence on the quality of the dataset, which pours both the quality of networking and the amount of data (the more data, the better and more accurate the network training). But if the data is excessive, there is a chance of missing such implicit, but also dangerous attacks as R2L and U2R.

SC-DDPL as a Countermeasure against Static Power Side-Channel Attacks

With the continuous scaling of CMOS technology, which has now reached the 3 nm node at production level, static power begins to dominate the power consumption of nanometer CMOS integrated circuits. A novel class of security attacks to cryptographic circuits which exploit the correlation between the static power and the secret keys was introduced more than ten years ago, and, since then, several successful key recovery experiments have been reported. These results clearly demonstrate that attacks exploiting static power (AESP) represent a serious threat for cryptographic systems implemented in nanometer CMOS technologies. In this work, we analyze the effectiveness of the Standard Cell Delay-based Precharge Logic (SC-DDPL) style in counteracting static power side-channel attacks. Experimental results on an FPGA implementation of a compact PRESENT crypto-core show that the SC-DDPL implementation allows a great improvement of all the security metrics with respect to the standard CMOS implementation and other state-of-the-art countermeasures such as WDDL and MDPL.

A Fully Adaptively Secure Threshold Signature Scheme Based on Dual-Form Signatures Technology

In t , n threshold signature schemes, any subset of t participants out of n can produce a valid signature, but any fewer than t participants cannot. Meanwhile, a threshold signature scheme should remain robust and unforgeable against up to t − 1 corrupted participants. This nonforgeability property is that even an adversary breaking into up to t − 1 participants should be unable to generate signatures on its own. Existential unforgeability against adaptive chosen message attacks is widely considered as a standard security notion for digital signature, and threshold signature should also follow this accordingly. However, there are two special attack models in a threshold signature scheme: one is the static corruption attack and the other is the adaptive corruption attack. Since the adaptive corruption model appears to better capture real threats, designing and proving threshold signature schemes secure in the adaptive corruption model has been focused on in recent years. If a threshold signature is secure under adaptive chosen message attack and adaptive corruption attack, we say it is fully adaptively secure. In this paper, based on the dual pairing vector spaces technology, we construct a threshold signature scheme and use Gerbush et al.’s dual-form signatures technology to prove our scheme, which is fully adaptively secure in the standard model, and then compare it to other schemes in terms of the efficiency and computation.

An Efficient Dual-image Reversible Data Hiding Scheme Based on Exploiting Modification Direction

Reversible data hiding (RDH) subtly alter the cover image for embedding the secret data, after extracting the secret data, the cover image can also be completely restored. In this work, we propose a novel RDH scheme based on Exploiting Modification Direction (EMD) with dual images. The characteristics of EMD matrix is exploited, one secret bit and a base-5 digit can be concealed in a cover pixel to generate a pixel pair which will be assigned to two stego images. Without any overhead message, the reversibility can be achieved. In the experiment, the proposed scheme outperforms some state-of-the-art methods in terms of measuring in embedding ratio (ER) or peak-signal-to-noise ratio (PSNR). In addition, the proposed scheme has good performance on resisting static attacks on pixel-value differencing histogram (PDH) and regular singular (RS) analysis.

A Descriptor System design framework for false data injection attack toward power systems

In this paper, a false data injection attack scheme is developed for power systems in the framework of descriptor system. On account of generators dynamics and static power flows, a descriptor system model for power systems is firstly established. Then, a necessary and sufficient condition for perfect attack is given in terms of unstable eigenvector by the eigen-decomposition of system matrices. Based on this, a false data injection attack scheme, which can pass not only the observer detection but also the measurement limit detection, is presented to destabilize the power systems. The proposed scheme has the advantages of concealment and implementation simplicity when compared to the existing static attack counterpart. Additionally, the obtained results can help to understand the weakness of power systems and establish some more secure protective measures. Simulations on the IEEE 9 - bus and 14 - bus power systems demonstrate the superiority and effectiveness of the proposed method.

Refining characteristics of isometric straight bar plates with different bar angles

Bar angle is one of the most important parameters of an isometric straight bar refiner plate and its effects on the change in pulp and paper properties during refining is still unclear. This study investigated the effect of plate bar angle on refining quality and refining efficiency by clarifying the definition of bar angle. Five isometric straight bar plates with different bar angles were used in low consistency refining of bleached sulphate eucalyptus pulp (BSEP). Samples at different refining time intervals were collected, and the fibers, pulp, and paper properties were detected and analyzed. There was a great influence of bar angle on the fibers, pulp, and paper properties. Additionally, a critical static attack angle, or critical bar angle for plates with specific field angle, existed. The modification of fibers and pulp properties was strong when the plate reached a critical bar angle. Meanwhile, there was an optimal static attack angle for a plate with specific field angle, when considering paper properties such as tensile index and tear index. The refining time was shortest when the pulp was refined by the plate with critical bar angle, allowing the paper properties to reach a maximum value. It was shown that the static attack angle of refining should be selected in the range of 45º to 70º, which includes the optimal value and the bar angle was proposed in the range of 11.25º to 23.75º for the plate with common field angle of 22.5º.

Optimal Denial-of-Service attack energy management against state estimation over an SINR-based network

We consider a scenario in which a DoS attacker with the limited power resource and the purpose of degrading the system performance, jams a wireless network through which the packet from a sensor is sent to a remote estimator. To degrade the estimation quality most effectively with a given energy budget, the attacker aims to solve the problem of how much power to obstruct the channel each time, which is the recently proposed optimal attack energy management problem. The existing works are built on an ideal network model in which the packet dropout never occurs when the attack is absent. To encompass wireless transmission losses, we introduce the signal-to-interference-plus-noise ratio-based network. First we focus on the case when the attacker employs the constant power level. To maximize the expected terminal estimation error at the remote estimator, we provide some more relaxed sufficient conditions compared with the existing work for the existence of an explicit solution to the optimal static attack energy management problem and the solution is constructed. For the other important index of system performance, the average expected estimation error, the associated sufficient conditions are also derived based on a different analysis approach with the existing work. And a feasible method is presented for both indexes to seek the optimal constant attack power level when the system fails to meet the proposed sufficient conditions. Then when the real-time ACK information can be acquired, a Markov decision process (MDP) based algorithm is designed to solve the optimal dynamic attack energy management problem. We further study the optimal tradeoff between attack energy and system degradation. Specifically, by moving the energy constraint into the objective function to maximize the system index and minimize the energy consumption simultaneously, the other MDP based algorithm is proposed to find the optimal dynamic attack power policy which is further shown to have a monotone structure. The theoretical results are illustrated by simulations.

Cryptanalysis and improvement of authentication scheme for roaming service in ubiquitous network

The paper analyzes a recently proposed secure authentication and key agreement scheme for roaming service in a ubiquitous network. In 2018, Lee et al. proposed a biometric-based anonymous authentication scheme for roaming in ubiquitous networks. But, we found that Lee et al. scheme is prone to the off-line dictionary attack when a user’s smart device is stolen, replay attack due to static variables and de-synchronization attack when an adversary blocks a message causing failure of authentication mechanism. Further, the scheme lacks no key control property and has incorrect XOR calculation. In the sequel, we presented an improved biometric based scheme to remove the weaknesses in Lee et al.’s scheme, which also does not require an update of identity in every session, hence preventing de-synchronization attack. Also, the security of the proposed schemes were analyzed in a widely accepted random oracle model. Further, computational and communication cost comparisons indicate that our improved scheme is more suitable for ubiquitous networks.

CHAMELEON: Hides privacy in cloud IoT system by LSB and CSE

SummaryRecently, in a variety of Internet of Things (IoT) application scenarios, fingerprint image as a common biological information has been widely collected. For example, in safe city projects, civil fingerprints representing unique identification are vastly assembled, stored, and transmitted wirelessly. Under such situations, data privacy protection becomes increasingly valued. In our study, this fundamental problem was centered. In our work, the method to remain secure in such an universal cloud based IoT system and the approach to hide important information in biologically collected data such are fully discussed. A new lightweight algorithm in protecting the privacy and integrity of data in a cloud based IoT system is badly needed. We believe that it is a way to hide the core information in the surrounding environment just like “Chameleon” to protect the data security. Thus, in this paper, we propose “CHAMELEON” by using least significant bits (LSB) to hide important information in fingerprint images and chaotic sequence encryption (CSE) to securely transmit useful data. The experimental results of this algorithm show that Chameleon can resist static attacks and protect the privacy of users regarding to the information security and integrity.

AdaWFPA: Adaptive Online Website Fingerprinting Attack for Tor Anonymous Network: A Stream-wise Paradigm

Nowadays, network traffic analysis is quite pervasive in human practice. Website fingerprinting attack, which is a new variant of traffic analysis attacks, identifies the websites visited by clients in encrypted and anonymized Tor connections by observing patterns in packet flows. Previous website fingerprinting attacks focus on static models in which the classifier is trained within a time period and then it is utilized to identify targeted websites. Static attacks cannot handle the time effect on the accuracy since their classifiers are not trained on the newest versions of the websites. Consequently, their accuracy drops drastically when tested on captured traffic traces of websites, days after training. This time effect is known as concept drift.In order to maintain the performance of the classifier, the classifier must be updated over time. In static attacks, updating the classifier includes updating the whole dataset again along with retraining the classifier. Recollecting, maintaining, and updating datasets as well as updating and retraining the classifier are very time and memory consuming. To deal with the emerging issues arising from concept drift and expensive retraining phases, this paper proposes AdaWFPA; an adaptive online website fingerprinting attack which is based on adaptive stream mining algorithms. AdaWFPA avoids concept drift by updating its model over time. Our empirical analyses and experiments over real world datasets indicate the superiority of our approach; as it offers better accuracy, precision, and recall in comparison with other state-of-the-art methods in the literature.

Mixed Methods in Decision-Making Through Polar Coordinate Technique: Differences by Gender on Beach Handball Specialist.

The aim of this research was to analyze decision making of the specialist in beach handball in the framework of mixed methods, and through the observation of their actions. To do this, and distinguishing it by genre, an analysis of polar coordinates was realized using the assist and completion of this specific position as behavior criteria. A total of 24 sessions were observed with the HOISAN computer software, using an ad hoc design tool. Observation design used was nomothetic, punctual, and multidimensional. The obtained results showed significant relations between the behavior criteria and conditional behaviors (pairing), showing significant relations different for male and female categories. Significant differences were estimated statistically about the importance of the specialist in the development of the static attack in beach handball and their influence in the final result of the match, based on their greater or lesser success in decision making. In the male category it is highlighted that the specialist’s assists are not positively related to a favorable score and that the attack usually ends up in the central areas against closed defensive systems; in addition, their successful completion decisions are produced by means of launches in the central zone, also against closed defensive systems; however, in the female category, specialists’ assists lead to end the attacks in flight on the far right against open defensive systems.