Mode Of Attack Research Articles

Pedestrian emergency evacuation model based on risk field under attack event

In recent years, stabbings have occurred frequently around the world, often causing serious casualties and property losses. Therefore, using evacuation model to simulate and analyze this type of attack mode is of great significance to personnel safety and social stability. However, in the simulation process, the lack of interaction rules between pedestrians and attackers will seriously reduce the accuracy and credibility of the simulation result. Herein, this article focuses on the pedestrian evacuation under stabbings, we proposed a risk field model for pedestrian movement, its formula referred to the continuous point-source diffusion equation in unbounded space. The influence of the surrounding environment on pedestrian movement was considered comprehensively to set the direction of the force exerted by the attacker, pedestrian movement rules and exit selection mechanism were introduced in details. At the same time, we discuss two different attack strategies for attackers, and carried out virtual reality experiments to analyze the experimenter’s attack modes and compare them with the two pure strategies. Multiple simulations are conducted on the basis of the model to explore the physical significance of model parameters and the influence of attack mode and location on evacuation, and to explore the mechanism of pedestrians’ selection of exits under the condition of multiple exits. At the end of this paper, the simulation results are summarized and suggestions for evacuation under emergencies are provided, which may be helpful to the simulation of pedestrian evacuation under emergencies in the future.

An Intelligent Approach of Intrusion Detection in Mobile Crowd Sourcing Systems in the Context of IoT Based SMART City

ABSTRACT The recent era of pervasive computing has evolved with various applications and has ground-breaking realities in mobile crowdsourcing (MCS). Multiple attempts have been devoted to integrating MCS with IoT-based smart cities where crowdsensing has played a crucial role in the recent past. Despite having potential features, MCS devices lack efficiency when security aspects are concerned. The current security approaches exercised in MCS operations imply limited features and are not intelligent enough to deal with different types of attacks in IoT smart cities. On the other hand, as MCS communications involve radio environmental mapping functional blocks from communication, it is an obvious situation that leads to a vulnerable situation of which adversarial modules can take advantage of it. There are different types of active and passive modes of attacks that can degrade the Quality-of-Service (QoS) aspects in IoT-driven smart city operations. This study’s prime aim and the appealing theme is to realize the need for resilient approaches to intelligent intrusion detection in MCS to mitigate different attacks. The study also introduces a theoretical approach of cluster-enabled multi-task (CeMT) based on bio-inspired learning modeling of the genetic approach to identify the maximum possible threats and misbehaving devices in the smart city-based MCS operations. The study also evaluated the model’s performance based on the processing time of identifying malicious events and showed the accuracy of detecting misbehaving working associate (WA) modules.

Event-Triggered Control for Switched Systems With Denial-of-Service Attack

This article studies the stabilization problem for switched systems in the presence of denial-of-service (DoS) attack using the event-triggered scheme. Unlike the traditional switching signal design with DoS attack, some challenges arise in the joint influence on the constraints of DoS duration/frequency, and the asynchronous behavior caused by the controller and the subsystem mode. To this end, a novel multiple Lyapunov function involving the joint effects of DoS attack and controller mode is constructed. This allows that there are no events between two consecutive switching instants. By incorporating the dwell-time switching signal with the event-triggered scheme, the considered system can be globally exponentially stabilizable when the frequency and duration of DoS attack meet certain requirements. It also shows that apart from the codesign associated with the event-triggered parameters with controller gain, the maximum allowable sampling period can be offline calculated. Moreover, the Zeno behavior is eliminated by calculating a lower bound of the event-triggered interval, over which the DoS attack is inactive. Finally, simulations are carried out to verify the effectiveness of the theoretical results.

Computational Exploration of the Synergistic Anticancer Effect of a Multi-Action Ru(II)-Pt(IV) Conjugate.

An in-depth computational study of the ability of a recently proposed multi-action Ru(II)–Pt(IV) conjugate to act as a photosensitizer in photodynamic therapy (PDT) and chemotherapeutic drugs is presented here. The investigated complex is characterized by a polypyridyl Ru(II) chromophore linked to a Pt(IV) complex that, acting as a prodrug, should be activated by reduction releasing the Ru-based chromophore that can absorb light of proper wavelength to be used in PDT. The reaction mechanism for active species formation has been fully elucidated by means of density functional theory and its time-dependent extension. The reduction mechanism, assisted by ascorbate, of the Pt(IV) prodrug to the Pt(II) active species has been explored, taking into consideration all the possible modes of attack of the reductant for releasing the axial ligands and affording active cisplatin. Given the similarity in the photophysical properties of the chromophore linked or not to the Pt(IV) complex, both the Ru(II)–Pt(IV) conjugate precursor and the Ru(II) chromophore should be able to act as PDT photosensitizers according to type I and type II photoprocesses. In particular, they are able to generate singlet oxygen cytotoxic species as well as auto-ionize to form highly reactive O2–• species.

Event-triggered adaptive sliding mode control of cyber-physical systems under false data injection attack

This paper studies the event-triggered adaptive sliding mode control(ASMC) problem for the cyber-physical systems (CPSs) under the false data injection attack (FDIA). First, for the unknown upper bound information of the false data injection attack, the adaptive technique is used to estimate the upper bound of the attack mode. Then the event-triggered ASMC method is developed by combining with the event-triggered mechanism. Second, Lyapunov’s stability theory is used to prove the admissibility of the formed event-triggered ASMC design scheme. Finally, simulation results verifies the effectiveness and superiority of the proposed method, which ensures the security and stability of the system and improve the utilization of information resource.

Safety Impact Analysis Considering Physical Failures and Cyber-Attacks for Mechanically Pumped Loop Systems (MPLs).

As complex systems composed of physical and cyber components, mechanically pumped loop systems (MPLs) are vulnerable to both passive threats (e.g., physical failures) and active threats such as cyber-attacks launched on the network control systems. The impact of the aforementioned two threats on MPL operations is yet unknown, and there is no practical way to evaluate their severity. To assess the severity of the impact of physical failures and cyber-attacks on MPLs, a safety impact analysis framework based on Elman Neural Network (ENN) observers and the Gaussian Mixture Model (GMM) algorithm is suggested. The framework discusses three common attack and failure modes: sensor hard failure that occurs suddenly, sensor soft failure that occurs gradually over time, and denial-of-service (DoS) attacks that prevent communication between the controller and valve. Both sensor failures and DoS attacks render the system unsafe, according to simulation data. In comparison to DoS attacks, however, sensor failures, particularly soft failures, inflict the greatest harm to the MPLs. Furthermore, sensors engaged in global control, rather than those involved in local control, need additional protection.

Sparse Actuator and Sensor Attacks Reconstruction for Linear Cyber-Physical Systems With Sliding Mode Observer

Driven by the rapid development of modern industrial processes, cyber-physical systems (CPSs), which tightly conjoin computational and physical resources, have become ever-more prevalent during recent years. However, due to the intrinsical vulnerability of the cyber layer, the system performances of CPSs are easily degraded by malicious false data injection (FDI) attacks, which are launched by adversary. In this article, the issue of secure reconstruction is considered for linear CPSs with simultaneous sparse actuator and sensor attacks. First, an adaptive counteraction searching strategy is proposed to identify the potential combinational attack mode. In this way, malicious FDI attacks are excluded. Second, by constructing a descriptor switched sliding mode observer, the sparse FDI attacks and the system state are reconstructed effectively. Meanwhile, sufficient conditions of the error convergence can be derived. Finally, a numerical simulation is utilized to illustrate the applicability of the proposed theoretical derivation.

An intelligent proactive defense against the client‐side DNS cache poisoning attack via self‐checking deep reinforcement learning

A new class of poisoning attacks has recently emerged targeting the client-side Domain Name System (DNS) cache. It allows users to visit fake websites unconsciously, thereby revealing their information, such as passwords. However, the current DNS defense architecture does not include DNS clients. Although relative encryption solutions can mitigate this attack, they require the cooperation of multiple parties, and the deployment speed is slow. Therefore, we propose an intelligent-driven proactive defense strategy. First, we model the offensive and defensive process as a stochastic game based on moving target defense. Second, we adopt and optimize Proximal Policy Optimization (PPO), a deep reinforcement learning method, to solve problems caused by uncertain attack strategies and unknown state transition probability. Third, we design a self-checking component in PPO to solve the uncertainty of action space caused by game state constraints based on our previous work. Thus the convergence speed and stability of PPO are improved. Finally, to the best of our knowledge, we are the first to game with intelligent attackers besides three conventional ones. Our strategy does not require any modifications to the DNS architecture. Through an extensive experimental campaign, the prototype system is proved to be effective against multiple attack modes. Its success rate is 98.5% approximately, and network round-trip time is about 55 ms. Even for random attackers, our method can achieve the theoretical maximum defensive success rate.

DTEXNet: Artificial Intelligence-Based Combination Scheme for DDoS Attacks Detection

Distributed Denial of Service (DDoS) attacks became the most widely spread attack because of their ease of design and execution. Such attacks are challenging to detect and mitigate due to the diversity of DDoS attack modes and the variable size of attack traffic. This makes the research on DDoS attack detection extremely important. Machine learning techniques are used to detect various DDoS attacks with complex and dynamic patterns. However, such techniques require extensive pre-processing and feature engineering to the data to achieve acceptable results. On the other hand, neural networks can achieve acceptable results without the need for such prior preparations. This paper proposes a novel combination scheme between EfficientNet, Xception, and Decision Tree models called DTEXNet. DTEXNet combines two neural networks to benefit from their ability to extract features without the need to prior preparations, and a classical machine learning model that has high performance on similar problems. The solution proposed uses two convolutional neural networks (CNNs) to classify between 10 types of DDoS attacks and uses their prediction results to enhance the performance of Decision Tree model on the same classification task. The results of the experiments carried out show that the proposed solution can significantly improve the results of the Decision tree, EfficientNet, and Xception models if applied individually.

Multi-attack and multi-classification intrusion detection for vehicle-mounted networks based on mosaic-coded convolutional neural network

With the development of Internet of vehicles, the information exchange between vehicles and the outside world results in a higher risk of external network attacks to the vehicles. The attack modes to the most widely used vehicle-mounted CAN bus are complex and diverse, but most of the intrusion detection approaches proposed by now can only detect one type of attack at a time. Aiming at detecting multi-types of attacks using a single model, we proposed a detection method based on the Mosaic-coded convolution neural network for intrusions containing various combinations of attacks with multi-classification capability. A Mosaic-like two-dimensional data grid was created from the one-dimensional CAN ID for the CNN to effectively extract the data features and maintain the time connections between the CAN IDs. Four types of attacks and all possible combinations of them were used to train and test our model. The autoencoder was also used to reduce the dimensionality of the data so as to cut down the model’s complexity. Experimental results showed that the proposed method was effective in detecting all types of attack combinations with high and stable multi-classification ability.

Attack and defence methods in cyber‐physical power system

With the development of digitalisation and intelligence, the power system has been upgraded from the traditional single energy transmission and conversion system to a complex cyber-physical power system (CPPS) with tightly coupled energy and information flows. The cyber-physical power system achieves the controllability and observability of the power system through ubiquitous sensing technology, advanced measurement technology, and powerful information processing technology. However, the large number of intelligent electronic device accesses and frequent information interactions in CPPS make it more vulnerable and susceptible to be attacked than any previous single structured system. Viruses and intrusions can attack the CPPS through the cyber subsystem, which in turn can deal a fatal blow to the energy supply physical system. Because of the above problems, malicious attacks against CPPS have been occurring in recent years and have generated a great deal of scholarly attention. This paper precisely focusses on this problem, by profiling the structure of CPPS and the potential threats, conducting an in-depth analysis of CPPS attack modes from the cyber and physical subsystems, and summarising the three-level security defence methods for CPPS in detail. Finally, the future technological development prospects of CPPS security research are explicitly addressed, which will provide technical support for building reliable, safe, and robust energy systems.

Cascading-Failures Effect on Heterogeneous Internet of Things Systems under Targeted Selective Attack

With the rapid development of the Internet of Things (IoT), the physical system and the network space are further deeply integrated, forming a larger-scale IoT heterogeneous fusion system. The attack mode considered in the security mechanism research of traditional large-scale complex systems is relatively simple; only simple attack types such as random attacks on physical systems or network systems are considered. In addition, existing attack modalities such as selectivity, locality, and distribution cannot fully consider the characteristics of security threats in the IoT system. In this paper, for large-scale heterogeneous IoT system scenarios, attackers can attack network systems or physical systems through cyberspace. We conduct situational awareness analysis on important traffic nodes or backbone nodes and study the cascading failures of two interdependent heterogeneous space systems. In view of the existence of such targeted attack threats in large-scale IoT heterogeneous systems, we focus on security assessment and risk prediction issues. First, this paper analyzes and models different IoT heterogeneous systems. Then using the penetration theory, we analyze the cascading failure process step by step and obtain the critical threshold for system collapse failure. Finally, we further verify the correctness of the theoretical values through simulation to effectively analyze and illustrate the reliability of the parameters affecting the system risk. The experimental results show that the large-scale IoT heterogeneous system presents a first-order discontinuous transition value near the critical threshold and the power-law index of the SF network has little effect on the system security.

Application of meta-learning in cyberspace security: a survey

In recent years, machine learning has made great progress in intrusion detection, network protection, anomaly detection, and other issues in cyberspace. However, these traditional machine learning algorithms usually require a lot of data to learn and have a low recognition rate for unknown attacks. Among them, “one-shot learning”, “few-shot learning”, and “zero-shot learning” are challenges that cannot be ignored for traditional machine learning. The more intractable problem in cyberspace security is the changeable attack mode. When a new attack mode appears, there are few or even zero samples that can be learned. Meta-learning comes from imitating human problem-solving methods as humans can quickly learn unknown things based on their existing knowledge when learning. Its purpose is to quickly obtain a model with high accuracy and strong generalization through less data training. This article first divides the meta-learning model into five research directions based on different principles of use. They are model-based, metric-based, optimization-based, online-learning-based, or stacked ensemble-based. Then, the current problems in the field of cyberspace security are categorized into three branches: cyber security, information security, and artificial intelligence security according to different perspectives. Then, the application research results of various meta-learning models on these three branches are reviewed. At the same time, based on the characteristics of strong generalization, evolution, and scalability of meta-learning, we contrast and summarize its advantages in solving problems. Finally, the prospect of future deep application of meta-learning in the field of cyberspace security is summarized.

Identification of reference scenarios for security attacks to the process industry

The possibility of inducing severe security-related events with damage to people, property, and the environment by deliberate malicious attacks to chemical and process plants handling large quantities of hazardous materials received an increasing attention in recent years. The identification of the credible security scenarios is required by Security Vulnerability/Risk Assessment (SVA/SRA) methodologies. However, the current availability of supporting tools is limited. This may hinder a proper management of the risks, especially in the European context where security threats are only marginally recognized under the Seveso legislation. The present study aims at supporting a harmonized identification of the scenarios triggered by deliberate malicious physical attacks to chemical and process plants. An approach based on Bow-Tie formalism is proposed to identify reference security scenarios. The Bow-Tie diagram is used to link the attack modes (Attack Tree) to the relevant release scenarios (Security Events) and to the physical damage scenarios (Event Tree). Reference Bow-Tie diagrams were defined considering substances commonly present in process plants (e.g. flammable substances and oxidizing solids). The validation of the reference scenarios (both attack scenarios and physical damage scenarios) was provided by the analysis of more than 20 security-related incidents that occurred in chemical and process facilities worldwide in the last 50 years. Application to a case-study proved the effectiveness of the results achieved in supporting SVA/SRA studies and in promoting integration among safety and security management.

Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images.

With the rapid development of communication technology, digital technology has been widely used in all walks of life. Nevertheless, with the wide dissemination of digital information, there are many security problems. Aiming at preventing privacy disclosure and ensuring the safe storage and sharing of image and video data in the cloud platform, the present work proposes an encryption algorithm against neural cryptography based on deep learning. Primarily, the image saliency detection algorithm is used to identify the significant target of the video image. According to the significant target, the important region and nonimportant region are divided adaptively, and the encrypted two regions are reorganized to obtain the final encrypted image. Then, after demonstrating how attackers conduct attacks to the network under the ciphertext attack mode, an improved encryption algorithm based on selective ciphertext attack is proposed to improve the existing encryption algorithm of the neural network. Besides, a secure encryption algorithm is obtained through detailed analysis and comparison of the security ability of the algorithm. The experimental results show that Bob's decryption error rate will decrease over time. The average classification error rate of Eve increases over time, but when Bob and Alice learn a secure encryption network structure, Eve's classification accuracy is not superior to random prediction. Chosen ciphertext attack-advantageous neural cryptography (CCA-ANC) has an encryption time of 14s and an average speed of 69mb/s, which has obvious advantages over other encryption algorithms. The self-learning secure encryption algorithm proposed here significantly improves the security of the password and ensures data security in the video image.

A Summary of Security Techniques-Based Blockchain in IoV

With the rapid development of the informatization and industrialization of the Internet of Vehicles (IoV), the number and application of connected vehicles are growing rapidly. The safety problem is related to the property and life of human beings, which has attracted extensive attention from academic and industrial circles. Based on the study of high-quality literature published in the past decade and other high-level research works, this paper first analyzes the forms of attack against the Internet of Vehicles from the two aspects of attack mode and target. Then, it summarizes the existing blockchain-based system framework of the Internet of Vehicles (BIoV) and then discusses the security solutions of blockchain-based vehicles from the aspects of authentication, privacy, trust management, access control, and so on, to support the distributed system architecture and solve the security challenges of the Internet of Vehicles. Finally, the technical difficulties and the direction of further research of BIoV are summarized.

Research on UCAV Maneuvering Decision Method Based on Heuristic Reinforcement Learning.

With the rapid development of unmanned combat aerial vehicle (UCAV)-related technologies, UCAVs are playing an increasingly important role in military operations. It has become an inevitable trend in the development of future air combat battlefields that UCAVs complete air combat tasks independently to acquire air superiority. In this paper, the UCAV maneuver decision problem in continuous action space is studied based on the deep reinforcement learning strategy optimization method. The UCAV platform model of continuous action space was established. Focusing on the problem of insufficient exploration ability of Ornstein–Uhlenbeck (OU) exploration strategy in the deep deterministic policy gradient (DDPG) algorithm, a heuristic DDPG algorithm was proposed by introducing heuristic exploration strategy, and then a UCAV air combat maneuver decision method based on a heuristic DDPG algorithm is proposed. The superior performance of the algorithm is verified by comparison with different algorithms in the test environment, and the effectiveness of the decision method is verified by simulation of air combat tasks with different difficulty and attack modes.

Synchronously scrambled diffuse image encryption method based on a new cosine chaotic map

We present a new cosine chaotic mapping proved by chaos theory test and analysis such that the system has good cryptography properties, wide chaos range, simple structure, and good sensitivity to initial value, and the mapping can meet the needs of chaotic image encryption. Based on the cosine chaotic system, we propose a new encryption method. First, according to the cyclic characteristics of the mapping, the cyclic information wave is simulated. Second, the quasi-Doppler effect is used to synchronously scramble and diffuse the image to obfuscate the original pixel. Finally, the XOR diffusion of image pixels is carried out by information wave to further enhance the encryption effect. Simulation experiment and security analysis show that the algorithm has good security, can resist the common attack mode, and has good efficiency.

Modelling Underload Cascading Failure and Mitigation Strategy of Supply Chain Complex Network in COVID-19

The outbreak of COVID-19 has caused problems such as shortage of workforce, cost increase, cash flow tension, and uncertainty of supply chain. It has a specific negative impact on the raw material supply, procurement management, production resumption, logistics, and market of the supply chain, which can trigger cascading failures in supply chain networks. Aiming at the failure of upstream/downstream firms in supply chain networks due to the decreased product demand/material supply under the COVID-19, the present study adopted an underload cascading failure model for the supply chain networks. In this model, the hierarchical supply chain networks were constructed based on the Erdos Renyi (ER) model and Barabasi Albert (BA) model. The validity of the model was verified under random attack and target attack. In the random attack mode, the influences of model parameters were studied, and in the target attack mode, the influence of target protection and random protection measures on enhancing network invulnerability was also studied. Simulation results showed that the initial load and capacity lower bound of nodes impact cascading failure size. The former has a positive correlation with cascading failure size, while the latter negatively correlates with cascading failure size. Furthermore, random protection measures are more practical to prevent cascading failures.

Ready Access to Benzannulated [5,5]-Oxaspirolactones Using Au(III)-Catalyzed Cascade Cyclizations.

This work showcases an unprecedented Au(III)-catalyzed cascade cyclization of 2-(4-hydroxyalkynyl)benzoates to access benzannulated [5,5]-oxaspirolactones related to biologically active natural products. This reaction proceeds through an initial 5-endo-dig mode of hydroalkoxylation of the alkynol segment to give the oxocarbenium species (via cyclic enol-ether) followed by the addition of carboxylate onto the oxocarbenium that delivers the oxaspirolactone scaffold. While testing this method's scope, we found that the steric and electronic environment of the hydroxyl group could alter the reaction pathway that delivers isochromenone through a competitive 6-endo-dig mode of attack of the carboxylate onto the tethered alkyne.