Biblio

Network attacks are becoming more intense and characterized by complexity and persistence. Mechanisms that ensure network resilience to faults and threats should be well provided. Different approaches have been proposed to network resilience; however, most of them rely on static policies, which is unsuitable for current complex network environments and real-time requirements. To address these issues, we present a Belief-Desire-Intention (BDI) based multi-agent resilience network controller coupled with blockchain. We first clarify the theory and platform of the BDI, then discuss how the BDI evaluates the network resilience. In addition, we present the architecture, workflow, and applications of the resilience network controller. Simulation results show that the resilience network controller can effectively detect and mitigate distributed denial of service attacks.

Recently, it has been proposed to deal with fusion of multi-object densities exploiting the minimum information loss (MIL) rule, which has shown its superiority over generalized covariance intersection (GCI) fusion whenever sensor nodes have low detection probability. On the contrary, GCI shows better performance than MIL when dense clutter is involved in the measurements. In this paper, we are going to study the behavior of multi-object fusion with MIL and, respectively, GCI rules in the situation wherein the sensor network is exposed to cyber-attacks. Both theoretical and numerical analyses demonstrate that MIL is more robust than GCI fusion when the multi-sensor system is subject to a packet substitution attack.

Resiliency of cyber-physical systems (CPSs) against malicious attacks has been a topic of active research in the past decade due to widely recognized importance. Resilient CPS is capable of tolerating some attacks, operating at a reduced capacity with core functions maintained, and failing gracefully to avoid any catastrophic consequences. Existing work includes an architecture for hierarchical control systems, which is a subset of CPS with wide applicability, that is tailored for resiliency. Namely, the architecture consists of local, network and supervision layers and features such as simplex structure, resource isolation by hypervisors, redundant sensors/actuators, and software defined network capabilities. Existing work also includes methods of ensuring a level of resiliency at each one of the layers, respectively. However, for a holistic system level resiliency, individual methods at each layers must be coordinated in their deployment because all three layers interact for the operation of CPS. For this purpose, a resiliency coordinator for CPS is proposed in this work. The resiliency coordinator is the interconnection of central resiliency coordinator in the supervision layer, network resiliency coordinator in the network layer, and finally, local resiliency coordinators in multiple physical systems that compose the physical layer. We show, by examples, the operation of the resiliency coordinator and illustrate that RC accomplishes a level of attack resiliency greater than the sum of resiliency at each one of the layers separately.

In recent years, with the occurrence of climate change and various extreme events, the research on the resistance of physical information systems to large-scale complex faults is of great significance. Propose a power information system to deal with complex faults in extreme weather, establish an anti-interference framework, construct a regional anti-interference strategy based on regional load output matching and topological connectivity, and propose branch active power adjustment methods to reduce disasters. In order to resist the risk of system instability caused by overrun of branch power and phase disconnection, the improved IEEE33 node test system simulation shows that this strategy can effectively reduce the harm of large-scale and complex faults.

Cloud provides access to shared pool of resources like storage, networking, and processing. Distributed denial of service attacks are dangerous for Cloud services because they mainly target the availability of resources. It is important to detect and prevent a DDoS attack for the continuity of Cloud services. In this review, we analyze the different mechanisms of detection and prevention of the DDoS attacks in Clouds. We identify the major DDoS attacks in Clouds and compare the frequently-used strategies to detect, prevent, and mitigate those attacks that will help the future researchers in this area.

There is momentous attention from researchers and practitioners all over the world towards one of the most advanced trends in the world, Smart cities. A smart city is an efficient and sustainable city that offers a superior life quality to all human beings through the optimum management of all its resources. Optimum energy management technique within the smart city is a challenging environment that needs a full focus on basic important needs and supports of the smart city. This includes Smart Grid (SG) infrastructure, Distributed Generation (DG) technology, Smart Home Energy Management System (HEMS), Smart Transportation System (STS), and Energy Storage System (ESS). Out of these five taxonomies, there have been some disputes addressed in profitability and security due to the major involvement of electromobility in the smart transportation system. It creates a big impact on the smart city environment. The disputes in profitability can be effectively handled with the use of dynamic pricing techniques and peer-to-peer (P2P) energy trading mechanisms. On the other hand, security disputes can be overwhelmed by the use of blockchain technology. This paper reviews the energy management-related work on smart cities with the consideration of these basic important needs and supports.

The expansion of the internet has resulted in huge growth in every industry. It does, however, have a substantial impact on the downsides. Because of the internet's rapid growth, personally identifiable information (PII) should be kept secure in the coming years. Obtaining someone's personal information is rather simple nowadays. There are some established methods for keeping our personal information private. Further, it is essential because we must provide our identity cards to someone for every verification step. In this paper, we will look at some of the attempted methods for protecting our identities. We will highlight the research gaps and potential future enhancements in the research for more enhanced security based on our literature review.

At present, cloud service providers control the direct management rights of cloud data, and cloud data cannot be effectively and assured deleted, which may easily lead to security problems such as data residue and user privacy leakage. This paper analyzes the related research work of cloud data assured deletion in recent years from three aspects: encryption key deletion, multi-replica association deletion, and verifiable deletion. The advantages and disadvantages of various deletion schemes are analysed in detail, and finally the prospect of future research on assured deletion of cloud data is given.

Successful information and communication technology (ICT) may propel administrative procedures forward quickly. In order to achieve efficient usage of TCT in their businesses, ICT strategies and plans should be examined to ensure that they align with the organization's visions and missions. Efficient software and hardware work together to provide relevant data that aids in the improvement of how we do business, learn, communicate, entertain, and work. This exposes them to a risky environment that is prone to both internal and outside threats. The term “security” refers to a level of protection or resistance to damage. Security can also be thought of as a barrier between assets and threats. Important terms must be understood in order to have a comprehensive understanding of security. This research paper discusses key terms, concerns, and challenges related to information systems and security auditing. Exploratory research is utilised in this study to find an explanation for the observed occurrences, problems, or behaviour. The study's findings include a list of various security risks that must be seriously addressed in any Information System and Security Audit.

Traditional magnetic mirrors are appealing because of their comparably simple geometry which lends itself to cost-effective construction. However, magnetic mirrors suffer from several inherent problems that make them poor choices for confining and heating plasmas. The chief concerns are the loss-cone instability which continuously saps hot particles from the trap and the interchange instability which effectively transports hot plasma from the core of the trap to the edges where it is lost to the walls. Centrifugal confinement schemes address these concerns with the addition of supersonic poloidal rotation which can effectively shut off the loss-cone. In addition, velocity shear in the flow may mitigate or even turn off the interchange instability if high enough rotation speeds can be achieved. Previous experiments have verified the efficacy of centrifugal confinement but have been unable to achieve sufficient rotation velocities to entirely shut down the interchange modes. [1] The rotation velocity in these experiments was limited by the Critical-Ionization-Velocity (CIV) instability. [3] We plan an experiment to verify that the CIV is the limiting factor in supersonic plasma centrifuges and to explore strategies for avoiding the CIV limit and achieving sufficient rotation speeds to enable stable plasma confinement.

In view of the problems that the existing power grid risk assessment mainly depends on the data fusion of decision-making level, which has strong subjectivity and less effective information, this paper proposes a risk assessment method of microgrid system based on random matrix theory. Firstly, the time series data of multiple sensors are constructed into a high-dimensional matrix according to the different parameter types and nodes; Then, based on random matrix theory and sliding time window processing, the average spectral radius sequence is calculated to characterize the state of microgrid system. Finally, an example is given to verify the effectiveness of the method.

In this paper, a novel method is proposed to assess the power system resilience considering the impacts of hurricanes. Firstly, the transmission line outage model correlated to wind speed is developed. Then, Probability Load Flow (PLF) considering the random outage of lines and the variation of loads is designed, and Latin Hypercube Sampling (LHS) is used to improve the efficiency of Monte Carlo Simulation (MCS) in solving PLF. Moreover, risk indices, including line overloading, node voltage exceeding limit, load shedding and system collapse, are established to assess the resilience of power systems during hurricanes. The method is tested with a modified IEEE 14-bus system, and simulation results indicate the effectiveness of the proposed approach.

This publication deals with the robust attitude stabilization of a quadrotor subject to stealthy actuator attacks. Based first on the nonlinear model of the system, the sector non-linearity approach will be applied in order to deduce a polytopic Takagi-sugeno model. In parallel, a polytopic fuzzy T-S modeling of the data-deception malicious attacks (time-varying parameters) is presented. After some mathematical development, it will be shown that our original nonlinear system subject to stealthy actuator attacks can be represented as an uncertain polytopic T-S system. Based on this latest model, basic concepts for attitude stabilization will be used to implement the control law. The stabilization conditions will be given in terms of Linear Matrix Inequalities (LMIs) deduced from a classical Lyapunov approach. In order to highlight the efficiency of the proposed approach, simulation results will be given.

The internet has grown increasingly important in everyone's everyday lives due to the availability of numerous web services such as email, cloud storage, video streaming, music streaming, and search engines. On the other hand, attacks by computer programmes such as bots are a common hazard to these internet services. Captcha is a computer program that helps a server-side company determine whether or not a real user is requesting access. Captcha is a security feature that prevents unauthorised access to a user's account by protecting restricted areas from automated programmes, bots, or hackers. Many websites utilise Captcha to prevent spam and other hazardous assaults when visitors log in. However, in recent years, the complexity of Captcha solving has become difficult for humans too, making it less user friendly. To solve this, we propose creating a Captcha that is both simple and engaging for people while also robust enough to protect sensitive data from bots and hackers on the internet. The suggested captcha scheme employs animated artifacts, rotation, and variable fonts as resistance techniques. The proposed captcha technique proves successful against OCR bots with less than 15% accuracy while being easier to solve for human users with more than 98% accuracy.

This paper proposes a Mobile IoT optimization method for Next-Generation networks by evaluating a series of named-based techniques implemented in Information-Centric Networking (ICN). The idea is based on the possibility to have a more suitable naming and forwarding mechanism to be implemented in IoT. The main advantage of the method is in achieving a higher success packet rate and data rate by following the proposed technique even when the device is mobile / roaming around. The proposed technique is utilizing a root prefix naming which allows faster process and dynamic increase for content waiting time in Pending Interest Table (PIT). To test the idea, a simulation is carried out by mimicking how IoT can be implemented, especially in smart cities, where a user can also travel and not be static. Results show that the proposed technique can achieve up to a 13% interest success rate and an 18.7% data rate increase compared to the well-known implementation algorithms. The findings allow for possible further cooperation of data security factors and ensuring energy reduction through leveraging more processes at the edge node.

Watermarking is one of the most common data hiding techniques for multimedia elements. Broadcasting, copy control, copyright protection and authentication are the most frequently used application areas of the watermarking. Secret data can be embedded into the cover image with changing the values of the pixels in spatial domain watermarking. In addition to this method, cover image can be converted into one of the transformation such as Discrete Wavelet Transformation (DWT), Discrete Cousin Transformation (DCT) and Discrete Fourier Transformation (DFT). Later on watermark can be embedded high frequencies of transformation coefficients. In this work, cover image transformed one, two and three level DWT decompositions. Binary watermark is hided into the low and high frequencies in each decomposition. Experimental results show that watermarked image is robust, secure and resist against several geometric attacks especially JPEG compression, Gaussian noise and histogram equalization. Peak Signal-to-Noise Ratio (PSNR) and Similarity Ratio (SR) values show very optimal results when we compare the other frequency and spatial domain algorithms.

In view of the characteristics that rolling bearing is prone to failure under actual working conditions, and it is difficult to classify the fault category and fault degree, the deep belief network is introduced to diagnose the rolling bearing fault. Firstly, principal component analysis is used to reduce the dimension of original input data and delete redundant input information. Then, the dimension reduced data are input into the deep belief network to extract the low dimensional fault feature representation, and the extracted features are input into the classifier for rolling bearing fault pattern recognition. Finally, the diagnosis effect of the proposed network is compared with the existing common shallow neural network. The simulation experiment is carried out through the bearing data in the United States.

This paper provides an in-depth analysis of Android malware that bypassed the strictest defenses of the Google Play application store and penetrated the official Android market between January 2016 and July 2021. We systematically identified 1,238 such malicious applications, grouped them into 134 families, and manually analyzed one application from 105 distinct families. During our manual analysis, we identified malicious payloads the applications execute, conditions guarding execution of the payloads, hiding techniques applications employ to evade detection by the user, and other implementation-level properties relevant for automated malware detection. As most applications in our dataset contain multiple payloads, each triggered via its own complex activation logic, we also contribute a graph-based representation showing activation paths for all application payloads in form of a control- and data-flow graph. Furthermore, we discuss the capabilities of existing malware detection tools, put them in context of the properties observed in the analyzed malware, and identify gaps and future research directions. We believe that our detailed analysis of the recent, evasive malware will be of interest to researchers and practitioners and will help further improve malware detection tools.

Embedded devices are becoming increasingly pervasive in safety-critical systems of the emerging cyber-physical world. While trusted execution environments (TEEs), such as ARM TrustZone, have been widely deployed in mobile platforms, little attention has been given to deployment on real-time cyber-physical systems, which present a different set of challenges compared to mobile applications. For safety-critical cyber-physical systems, such as autonomous drones or automobiles, the current TEE deployment paradigm, which focuses only on confidentiality and integrity, is insufficient. Computation in these systems also needs to be completed in a timely manner (e.g., before the car hits a pedestrian), putting a much stronger emphasis on availability.To bridge this gap, we present RT-TEE, a real-time trusted execution environment. There are three key research challenges. First, RT-TEE bootstraps the ability to ensure availability using a minimal set of hardware primitives on commodity embedded platforms. Second, to balance real-time performance and scheduler complexity, we designed a policy-based event-driven hierarchical scheduler. Third, to mitigate the risks of having device drivers in the secure environment, we designed an I/O reference monitor that leverages software sandboxing and driver debloating to provide fine-grained access control on peripherals while minimizing the trusted computing base (TCB).We implemented prototypes on both ARMv8-A and ARMv8-M platforms. The system is tested on both synthetic tasks and real-life CPS applications. We evaluated rover and plane in simulation and quadcopter both in simulation and with a real drone.

SaaS is a cloud-based application service that allows users to use applications that work in a cloud environment. SaaS is a subscription type, and the service expenditure varies depending on the license, the number of users, and duration of use. For efficient network management, security and cost management, accurate detection of user behavior for SaaS applications is required. In this paper, we propose a rule-based traffic analysis method for the user behavior detection. We conduct comparative experiments with signature-based method by using the real SaaS application and demonstrate the validity of the proposed method.

Soft real-time applications, including multimedia, gaming, and smart appliances, rely on specific architectural characteristics to deliver output in a time-constrained fashion. Any violation of application deadlines can lower the Quality-of-Service (QoS). The data sets associated with these applications are distributed over cores that communicate via Network-on-Chip (NoC) in multi-core systems. Accordingly, the response time of such applications depends on the worst-case latency of request/reply packets. A malicious implant such as Hardware Trojan (HT) that initiates a delay-of-service attack can tamper with the system performance. We model an HT that mounts a time-delay attack in the system by violating the path selection strategy used by the adaptive NoC router. Our analysis shows that once activated, the proposed HT increases the packet latency by 17% and degrades the system performance (IPC) by 18% over the Baseline. Furthermore, we propose an HT detection framework that uses packet traffic analysis and path monitoring to localise the HT. Experiment results show that the proposed detection framework exhibits 4.8% less power consumption and 6.4% less area than the existing technique.

In this paper, an overall introduction of fingerprint encryption algorithm is made, and then a fingerprint encryption algorithm with error correction is designed by adding error correction mechanism. This new fingerprint encryption algorithm can produce stochastic key in the form of multinomial coefficient by using the binary system sequencer, encrypt fingerprint, and use the Lagrange difference value to restore the multinomial during authenticating. Due to using the cyclic redundancy check code to find out the most accurate key, the accuracy of this algorithm can be ensured. Experimental result indicates that the fuzzy vault algorithm with error correction can well realize the template protection, and meet the requirements of biological information security protection. In addition, it also indicates that the system's safety performance can be enhanced by chanaing the key's length.

In the context of big data era, in order to prevent malicious access and information leakage during data services, researchers put forward a location big data encryption method based on privacy protection in practical exploration. According to the problems arising from the development of information network in recent years, users often encounter the situation of randomly obtaining location information in the network environment, which not only threatens their privacy security, but also affects the effective transmission of information. Therefore, this study proposed the privacy protection as the core position of big data encryption method, must first clear position with large data representation and positioning information, distinguish between processing position information and the unknown information, the fuzzy encryption theory, dynamic location data regrouping, eventually build privacy protection as the core of the encryption algorithm. The empirical results show that this method can not only effectively block the intrusion of attack data, but also effectively control the error of position data encryption.

Cyberspace is the fifth largest activity space after land, sea, air and space. Safeguarding Cyberspace Security is a major issue related to national security, national sovereignty and the legitimate rights and interests of the people. With the rapid development of artificial intelligence technology and its application in various fields, cyberspace security is facing new challenges. How to help the network security personnel grasp the security trend at any time, help the network security monitoring personnel respond to the alarm information quickly, and facilitate the tracking and processing of the monitoring personnel. This paper introduces a method of using situational awareness micro application actual combat attack and defense robot to quickly feed back the network attack information to the monitoring personnel, timely report the attack information to the information reporting platform and automatically block the malicious IP.

The power industrial control system is an important part of the national critical Information infrastructure. Its security is related to the national strategic security and has become an important target of cyber attacks. In order to solve the problem that the vulnerability detection technology of power industrial control system cannot meet the requirement of non-destructive, this paper proposes an industrial control vulnerability analysis technology combined with dynamic and static analysis technology. On this basis, an industrial control non-destructive vulnerability detection system is designed, and a simulation verification platform is built to verify the effectiveness of the industrial control non-destructive vulnerability detection system. These provide technical support for the safety protection research of the power industrial control system.