Biblio

This paper aims to address the problem of Deepfake Detection using a Two-Stream Capsule Network. First we review methods used to create Deepfake content, as well as methods proposed in the literature to detect such Deepfake content. We then propose a novel architecture to detect Deepfakes, which consists of a two-stream Capsule network running in parallel that takes in both RGB images/frames as well as Error Level Analysis images. Results show that the proposed approach exhibits the detection accuracy of 73.39 % and 57.45 % for the Deepfake Detection Challenge (DFDC) and the Celeb-DF datasets respectively. These results are, however, from a preliminary implementation of the proposed approach. As part of future work, population-based optimization techniques such as Particle Swarm Optimization (PSO) will be used to tune the hyper parameters for better performance.

Nowadays deepfake videos are concern with social ethics, privacy and security. Deepfake videos are synthetically generated videos that are generated by modifying the facial features and audio features to impose one person’s facial data and audio to other videos. These videos can be used for defaming and fraud. So, counter these types of manipulations and threats, detection of deepfake video is needed. This paper proposes multilayer hybrid recurrent deep learning models for deepfake video detection. Proposed models exploit the noise-based temporal facial convolutional features and temporal learning of hybrid recurrent deep learning models. Experiment results of these models demonstrate its performance over stacked recurrent deep learning models.

This paper describes a Cyber Threat, Vulnerability and Defense Modeling and Simulation tool kit used for evaluation of systems and networks to improve cyber resiliency. This capability is used to help increase the resiliency of networks at various stages of their lifecycle, from initial design and architecture through the operation of deployed systems and networks. Resiliency of computer systems and networks to cyber threats is facilitated by the modeling of agile and resilient defenses versus threats and running multiple simulations evaluated against resiliency metrics. This helps network designers, cyber analysts and Security Operations Center personnel to perform trades using what-if scenarios to select resiliency capabilities and optimally design and configure cyber resiliency capabilities for their systems and networks.

In this paper, a general model for cyber-physical systems (CPSs), that captures the diffusion of attacks from the cyber layer to the physical system, is studied. In particular, a game-theoretic approach is proposed to analyze the interactions between one defender and one attacker over a CPS. In this game, the attacker launches cyber attacks on a number of cyber components of the CPS to maximize the potential harm to the physical system while the system operator chooses to defend a number of cyber nodes to thwart the attacks and minimize potential damage to the physical side. The proposed game explicitly accounts for the fact that both attacker and defender can have different computational capabilities and disparate levels of knowledge of the system. To capture such bounded rationality of attacker and defender, a novel approach inspired from the behavioral framework of cognitive hierarchy theory is developed. In this framework, the defender is assumed to be faced with an attacker that can have different possible thinking levels reflecting its knowledge of the system and computational capabilities. To solve the game, the optimal strategies of each attacker type are characterized and the optimal response of the defender facing these different types is computed. This general approach is applied to smart grid security considering wide area protection with energy markets implications. Numerical results show that a deviation from the Nash equilibrium strategy is beneficial when the bounded rationality of the attacker is considered. Moreover, the results show that the defender's incentive to deviate from the Nash equilibrium decreases when faced with an attacker that has high computational ability.

Cyber-physical systems (CPS) data privacy protection during sharing, aggregating, and publishing is a challenging problem. Several privacy protection mechanisms have been developed in the literature to protect sensitive data from adversarial analysis and eliminate the risk of re-identifying the original properties of shared data. However, most of the existing solutions have drawbacks, such as (i) lack of a proper vulnerability characterization model to accurately identify where privacy is needed, (ii) ignoring data providers privacy preference, (iii) using uniform privacy protection which may create inadequate privacy for some provider while over-protecting others, and (iv) lack of a comprehensive privacy quantification model assuring data privacy-preservation. To address these issues, we propose a personalized privacy preference framework by characterizing and quantifying the CPS vulnerabilities as well as ensuring privacy. First, we introduce a Standard Vulnerability Profiling Library (SVPL) by arranging the nodes of an energy-CPS from maximum to minimum vulnerable based on their privacy loss. Based on this model, we present our personalized privacy framework (PDP) in which Laplace noise is added based on the individual node's selected privacy preferences. Finally, combining these two proposed methods, we demonstrate that our privacy characterization and quantification model can attain better privacy preservation by eliminating the trade-off between privacy, utility, and risk of losing information.

With the advent of motorization, result in traffic system more congested, how to make the traffic system more effective and also take safety into account, namely build a intelligent transportation system, has become a hot spot of society. The vehicle distance control system studied in this paper is an important function in intelligent transportation system, through introducing cyber-physical systems (CPS) technology into it, set up system model, make the vehicles maintain a preset safety distance, thereby not only help improve the effective utilization of traffic system, but also help avoid the collision due to the speed change. Finally, use Simulink software to simulate and analyze the performance of the system, the result shows that the model can effectively cope with the distance change which is due to speed change, and ensure the vehicles maintain a preset safety distance within a short period of time.

Cyber-physical systems (CPS) is an integration of computation with physical systems and physical processes. It is widely used in energy, health and other industrial areas. Modeling and simulation is of the greatest challenges in CPS research. Modelica has a great potentiality in the modeling and simulation of CPS. We analyze the characteristics and requirements of CPS modeling, and also the features of Modelica in the paper. In respect of information model, physical model and model interface, this paper introduces a unified modeling method for CPS, based on Modelica. The method provides a reliable foundation for the design, analysis and verification of CPS.

CPS traffic characteristics is one of key techniques of Cyber-Physical Systems (CPS). A deep research of CPS network traffic characteristics can help to better plan and design CPS networks. A brief overview of the key concepts of CPS is firstly presented. Then CPS application scenarios are analyzed in details and classified. The characteristics of CPS traffic is analyzed theoretically for different CPS application scenarios. At last, the characteristics of CPS traffic is verified using NS-2 simulation.

The ever-growing advances in science and technology have led to a rapid increase in the complexity of most engineered systems. Cyber-physical Systems (CPSs) are the result of this technology advancement that involves new paradigms, architectures and functionalities derived from different engineering domains. Due to the nature of CPSs, which are composed of many heterogeneous components that constantly interact one another and with the environment, it is difficult to study, explain hypothesis and evaluate design alternatives without using Modeling and Simulation (M&S) approaches. M&S is increasingly used in the CPS domain with different objectives; however, its adoption is not easy and straightforward but can lead to pitfalls that need to be recognized and addressed. This paper identifies some important pitfalls deriving from the application of M&S approaches to the CPS study and presents remedies, which are already available in the literature, to prevent and face them.

Cyber-physical systems are particularly difficult to model and simulate because their components mix many different system modalities. In this paper we address the main technical challenges on system simulation taking into account by new characters of CPS, and provide a comprehensive view of the simulation modeling methods for integration of continuous-discrete model. Regards to UML and Simulink, two most widely accepted modeling methods in industrial designs, we study on three methods to perform the cooperation of these two kinds of heterogeneous models for co-simulation. The solution of an implementation of co-simulation method for CPS was designed under three levels architecture.

Cyber-Physical System(CPS) is now a new evolutional morphology of embedded systems. With features of merging computation and physical processes together, the traditional verification and simulation methods have being challenged recently. After analyzed the state-of-art of related research, a new simulation environment is studied according to the characters of a special autonomous cyber-physical system-Unmanned Aerial Vehicle, and designed to be scene-driven, modeling and reconfigurable. In this environment, a novel CPS-in-loop architecture, which can support simulations under different customized scenes, is studied firstly to ensure its opening and flexibility. And as another foundation, some dynamics models of CPS and atmospheric ones of relative sensors are introduced to simulate the motion of CPS and the change of its posture. On the basis above, the reconfigurable scene-driven mechanisms that are Based on hybrid events are mainly excogitated. Then, different scenes can be configured in terms of special verification requirements, and then each scene will be decomposed into a spatio-temporal event sequence and scheduled by a scene executor. With this environment, not only the posture of CPS, but also the autonomy of its behavior can be verified and observed. It will be meaningful for the design of such autonomous CPS.

This paper presents ongoing work on the formalism of Cyber-Physical Systems (CPS) simulations. These systems are distributed real-time systems, and their simulations might be distributed or not. In this paper, we propose a model to describe the modular components forming a simulation of a CPS. The main goal is to introduce a model of generic simulation distributed architecture, on which we are able to execute a logical architecture of simulation. This architecture of simulation allows the expression of structural and behavioural constraints on the simulation, abstracting its execution. We will propose two implementations of the execution architecture based on generic architectures of distributed simulation: $\cdot$ The High Level Architecture (HLA), an IEEE standard for distributed simulation, and one of its open-source implementation of RunTime Infrastructure (RTI): CERTI. $\cdot$ The Distributed Simulation Scheduler (DSS), an Airbus framework scheduling predefined models. Finally, we present the initial results obtained applying our formalism to the open-source case study from the ROSACE case study.

In IoT scenarios, computational and communication costs on the user side are important problems. In most expressive ABE schemes, there is a linear relationship between the access structure size and the number of heavy pairing operations that are used in the decryption process. This property limits the application of ABE. We propose an expressive CP-ABE with the constant number of pairings in the decryption process. The simulation shows that the proposed scheme is highly efficient in encryption and decryption processes. In addition, we use the outsourcing method in decryption to get better performance on the user side. The main burden of decryption computations is done by the cloud without revealing any information about the plaintext. We introduce a new revocation method. In this method, the users' communication channels aren't used during the revocation process. These features significantly reduce the computational and communication costs on the user side that makes the proposed scheme suitable for applications such as IoT. The proposed scheme is selectively CPA-secure in the standard model.

CP-ABE (ciphertext-policy attribute-based encryption) is a promising encryption scheme. In this paper, a highly expressive revocable scheme based on the key encryption keys (KEK) tree is proposed. In this method, the cloud server realizes the cancellation of attribute-level users and effectively reduces the computational burden of the data owner and attribute authority. This scheme embeds a unique random value associated with the user in the attribute group keys. The attribute group keys of each user are different, and it is impossible to initiate a collusion attack. Computing outsourcing makes most of the decryption work done by the cloud server, and the data user only need to perform an exponential operation; in terms of security, the security proof is completed under the standard model based on simple assumptions. Under the premise of ensuring security, the scheme in this paper has the functions of revocation and traceability, and the speed of decryption calculation is also improved.

IP Identification (IP ID) is an IP header field that identifies a data packet in the network to distinguish its fragments from others during the reassembly process. Random generated IP ID field could be used as a covert channel by embedding hidden bits within it. This paper uses the support vector machine (SVM) while enabling a features reduction procedure for investigating to what extend could the entropy feature of the IP ID covert channel affect the detection. Then, an entropy-based SVM is employed to evaluate the roles of the IP ID covert channel hidden bits on detection. Results show that, entropy is a distinct discrimination feature in classifying and detecting the IP ID covert channel with high accuracy. Additionally, it is found that each of the type, the number and the position of the hidden bits within the IP ID field has a specified influence on the IP ID covert channel detection accuracy.

Web application vulnerability is one of the major causes of cyber attacks. Cyber criminals exploit these vulnerabilities to inject malicious commands to the unsanitized user input in order to bypass authentication of the database through some cyber-attack techniques like cross site scripting (XSS), phishing, Structured Query Language Injection Attack (SQLIA), malware etc., Although many research works have been conducted to resolve the above mentioned attacks, only few challenges with respect to SQLIA could be resolved. Ensuring security against complete set of malicious payloads are extremely complicated and demanding. It requires appropriate classification of legitimate and injected SQL commands. The existing approaches dealt with limited set of signatures, keywords and symbols of SQL queries to identify the injected queries. This work focuses on extracting SQL injection patterns with the help of existing parsing and tagging techniques. Pattern-based tags are trained and modeled using Multi-layer Perceptron which significantly performs well in classification of queries with accuracy of 94.4% which is better than the existing approaches.

Due to the continuous improvement of deep learning, saliency object detection based on deep learning has been a hot topic in computational vision. The Fully Convolutional Neural Network (FCNS) has become the mainstream method in salient target measurement. In this article, we propose a new end-to-end multi-level feature fusion module(MCFB), success-fully achieving the goal of extracting rich multi-scale global information by integrating semantic and detailed information. In our module, we obtain different levels of feature maps through convolution, and then cascade the different levels of feature maps, fully considering our global information, and get a rough saliency image. We also propose an optimization module upon our base module to further optimize the feature map. To obtain a clearer boundary, we use a self-defined loss function to optimize the learning process, which includes the Intersection-over-Union (IoU) losses, Binary Cross-Entropy (BCE), and Structural Similarity (SSIM). The module can extract global information to a greater extent while obtaining clearer boundaries. Compared with some existing representative methods, this method has achieved good results.

Distributed acoustic sensing (DAS) systems based on fiber brag grating (FBG) have been widely used for distributed temperature and strain sensing over the past years, and function well in perimeter security monitoring and structural health monitoring. However, with relevant algorithms functioning with low accuracy, the DAS system presently has trouble in signal recognition, which puts forward a higher requirement on a high-precision identification method. In this paper, we propose an improved recognition method based on relative fundamental signal processing methods and convolutional neural network (CNN) to construct a mathematical model of disturbance FBG signal recognition. Firstly, we apply short-time energy (STE) to extract original disturbance signals. Secondly, we adopt short-time Fourier transform (STFT) to divide a longer time signal into short segments. Finally, we employ a CNN model, which has already been trained to recognize disturbance signals. Experimental results conducted in the real environments show that our proposed algorithm can obtain accuracy over 96.5%.

Federated learning (FL) has nourished a promising scheme to solve the data silo, which enables multiple clients to construct a joint model without centralizing data. The critical concerns for flourishing FL applications are that build a security and privacy-preserving learning environment. It is thus highly necessary to comprehensively identify and classify potential threats to utilize FL under security guarantees. This paper starts from the perspective of launched attacks with different computing participants to construct the unique threats classification, highlighting the significant attacks, e.g., poisoning attacks, inference attacks, and generative adversarial networks (GAN) attacks. Our study shows that existing FL protocols do not always provide sufficient security, containing various attacks from both clients and servers. GAN attacks lead to larger significant threats among the kinds of threats given the invisible of the attack process. Moreover, we summarize a detailed review of several defense mechanisms and approaches to resist privacy risks and security breaches. Then advantages and weaknesses are generalized, respectively. Finally, we conclude the paper to prospect the challenges and some potential research directions.

Traditional grid-centric data storage methods are vulnerable to network attacks or failures due to downtime, causing problems such as data loss or tampering. The security of data storage can be effectively improved by establishing an alliance chain. However, the existing consortium chain consensus algorithm has low scalability, and the consensus time will explode as the number of nodes increases. This paper proposes an improved consensus algorithm (MSBFT) based on multi-signature to address this problem, which spreads data by establishing a system communication tree, reducing communication and network transmission costs, and improving system scalability. By generating schnorr multi-signature as the shared signature of system nodes, the computational cost of verification between nodes is reduced. At the end of the article, simulations prove the superiority of the proposed method.

Nowadays security of shared resources and big data is an important and critical issue. With the growth of information technology and social networks, data and resources are shared in the distributed environment such as cloud and fog computing. Various access control models protect the shared resources from unauthorized users or malicious intruders. Despite the attribute-based access control model that meets the complex security requirement of todays' new computing technologies, considerable anomalies and conflicts in ABAC policies affect the efficiency of the security system. One important and toughest task is policy validation thus to detect and eliminate anomalies and conflicts in policies. Though the previous researches identified anomalies, failed to detect and analyze all considerable anomalies that results vulnerable to hacks and attacks. The primary objective of this paper is to study and analyze the possible anomalies and conflicts in ABAC security policies. We have discussed and analyzed considerable conflicts in policies based on previous researches. This paper can provide a detailed review of anomalies and conflicts in security policies.

Distributed Denial-of-Service (DDoS) attacks pose a huge risk to the network and threaten its stability. A game theoretic approach for intrusion detection and prevention is proposed to avoid DDoS attacks in the internet. Game theory provides a control mechanism that automates the intrusion detection and prevention process within a network. In the proposed system, system-subject interaction is modeled as a 2-player Bayesian signaling zero sum game. The game's Nash Equilibrium gives a strategy for the attacker and the system such that neither can increase their payoff by changing their strategy unilaterally. Moreover, the Intent Objective and Strategy (IOS) of the attacker and the system are modeled and quantified using the concept of incentives. In the proposed system, the prevention subsystem consists of three important components namely a game engine, database and a search engine for computing the Nash equilibrium, to store and search the database for providing the optimum defense strategy. The framework proposed is validated via simulations using ns3 network simulator and has acquired over 80% detection rate, 90% prevention rate and 6% false positive alarms.

DDoS attacks have grown in popularity as a tactic for potential hackers, cyber blackmailers, and cyberpunks. These attacks have the potential to put a person unconscious in a matter of seconds, resulting in severe economic losses. Despite the vast range of conventional mitigation techniques available today, DDoS assaults are still happening to grow in frequency, volume, and intensity. A new network paradigm is necessary to meet the requirements of today's tough security issues. We examine the available detection and mitigation of DDoS attacks techniques in depth. We classify solutions based on detection of DDoS attacks methodologies and define the prerequisites for a feasible solution. We present a novel methodology named D3 for detecting and mitigating DDoS attacks using cuckoo filter.

Technological progress has changed our world beyond recognition. However, along with the incredible benefits and conveniences we have received new dangers and risks. Mankind is increasingly becoming hostage to information technology and cyber world. Recently, cybercrime is one of the top 10 risks to sustainable development in the world. It poses serious new challenges to global security and economy. The aim of the article is to obtain an assessment of some of the financial effects of modern IT crimes based on an analysis of the main aspects of monetary costs and the hidden economic impact of cybercrime. A multifactor regression model has been proposed to determine the contribution of the cost of the main consequences of IT incidents: business disruption, information loss, revenue loss and equipment damage caused by different types of cyberattacks worldwide in 2019 to total cost of cyberattacks. Information loss has been found to have a major impact on the total cost of cyberattacks, reducing profits and incurring additional costs for businesses. It was built a canonical model for identifying the dependence of total submission to ID ransomware, total cost of cybercrime and the main indicators of economic development for the TOP-10 countries. There is a significant correlation between two sets of indicators, in particular, it is confirmed that most cyberattacks target countries - countries with a high level of development, and the consequences of IT crimes are more significant for low-income countries.

Cyber-Physical systems (CPSs) are exposed to cyber- physical attacks, i.e., security breaches in cyberspace that adversely affect the physical processes of the systems.We define two probabilistic metrics to estimate the physical impact of attacks targeting cyber-physical systems formalised in terms of a probabilistic hybrid extension of Hennessy and Regan's Timed Process Language. Our impact metrics estimate the impact of cyber-physical attacks taking into account: (i) the severity of the inflicted damage in a given amount of time, and (ii) the probability that these attacks are actually accomplished, according to the dynamics of the system under attack. In doing so, we pay special attention to stealthy attacks, i. e., attacks that cannot be detected by intrusion detection systems. As further contribution, we show that, under precise conditions, our metrics allow us to estimate the impact of attacks targeting a complex CPS in a compositional way, i.e., in terms of the impact on its sub-systems.