Biblio

To restrict unauthorized access to the data of the website. Most of the web-based systems nowadays require users to verify themselves before accessing the website is authentic information. In terms of security, it is very important to take different security measures for the protection of the authentic data of the website. However, most of the authentication systems which are used on the web today have several security flaws. This document is based on the security of the previous schemes. Compared to the previous approaches, this “spoofed proof stateless session model” method offers superior security assurance in a scenario in which an attacker has unauthorized access to the data of the website. The various protocol models are being developed and implemented on the web to analyze the performance. The aim was to secure the authentic database backups of the website and prevent them from SQL injection attacks by using the read-only properties for the database. This limits potential harm and provides users with reasonable security safeguards when an attacker has an unauthorized read-only access to the website's authentic database. This scheme provides robustness to the disclosure of authentic databases. Proven experimental results show the overheads due to the modified authentication method and the insecure model.

Security is undoubtedly the most serious problem for Web applications, and SQL injection (SQLi) attacks are one of the most damaging. The detection of SQL blind injection vulnerability is very important, but unfortunately, it is not fast enough. This is because time-based SQL blind injection lacks web page feedback, so the delay function can only be set artificially to judge whether the injection is successful by observing the response time of the page. However, brute force cracking and binary search methods used in injection require more web requests, resulting in a long time to obtain database information in SQL blind injection. In this paper, a gated recurrent neural network-based SQL blind injection technology is proposed to generate the predictive characters in SQL blind injection. By using the neural language model based on deep learning and character sequence prediction, the method proposed in this paper can learn the regularity of common database information, so that it can predict the next possible character according to the currently obtained database information, and sort it according to probability. In this paper, the training model is evaluated, and experiments are carried out on the shooting range to compare the method used in this paper with sqlmap (the most advanced sqli test automation tool at present). The experimental results show that the method used in this paper is more effective and significant than sqlmap in time-based SQL blind injection. It can obtain the database information of the target site through fewer requests, and run faster.

This paper proposes a power network vulnerability analysis method based on topological approach considering of uncertainties from high-penetrated wind generations. In order to assess the influence of the impact of wind generation owing to its variable wind speed etc., the Quasi Monte Carlo based probabilistic load flow is adopted and performed. On the other hand, an extended stochastic topological vulnerability method involving Complex Network theory with probabilistic load flow is proposed. Corresponding metrics, namely stochastic electrical betweenness and stochastic net-ability are proposed respectively and applied to analyze the vulnerability of power network with wind generations. The case study of CIGRE medium voltage benchmark network is performed for illustration and evaluation. Furthermore, a cascading failures model considering the stochastic metrics is also developed to verify the effectiveness of proposed methodology.

In this paper, we consider a discrete-time stochastic Stackelberg game where there is a defender (also called leader) who has to defend a target and an attacker (also called follower). The attacker has a private type that evolves as a controlled Markov process. The objective is to compute the stochastic Stackelberg equilibrium of the game where defender commits to a strategy. The attacker’s strategy is the best response to the defender strategy and defender’s strategy is optimum given the attacker plays the best response. In general, computing such equilibrium involves solving a fixed-point equation for the whole game. In this paper, we present an algorithm that computes such strategies by solving lower dimensional fixed-point equations for each time t. Based on this algorithm, we compute the Stackelberg equilibrium of a security example.

The use of Virtual Machine (VM) migration as support for software rejuvenation was introduced more than a decade ago. Since then, several works have validated this approach from experimental and theoretical perspectives. Recently, some works shed light on the possibility of using the same technique as Moving Target Defense (MTD). However, to date, no work evaluated the availability and security levels while applying VM migration for both rejuvenation and MTD (multipurpose VM migration). In this paper, we conduct a comprehensive evaluation using Stochastic Petri Net (SPN) models to tackle this challenge. The evaluation covers the steady-state system availability, expected MTD protection, and related metrics of a system under time-based multipurpose VM migration. Results show that the availability and security improvement due to VM migration deployment surpasses 50% in the best scenarios. However, there is a trade-off between availability and security metrics, meaning that improving one implies compromising the other.

When it comes to cryptographic random number generation, poor understanding of the security requirements and “mythical aura” of black-box statistical testing frequently leads it to be used as a substitute for cryptanalysis. To make things worse, a seemingly standard document, NIST SP 800–22, describes 15 statistical tests and suggests that they can be used to evaluate random and pseudorandom number generators in cryptographic applications. The Chi-nese standard GM/T 0005–2012 describes similar tests. These documents have not aged well. The weakest pseudorandom number generators will easily pass these tests, promoting false confidence in insecure systems. We strongly suggest that SP 800–22 be withdrawn by NIST; we consider it to be not just irrelevant but actively harmful. We illustrate this by discussing the “reference generators” contained in the SP 800–22 document itself. None of these generators are suitable for modern cryptography, yet they pass the tests. For future development, we suggest focusing on stochastic modeling of entropy sources instead of model-free statistical tests. Random bit generators should also be reviewed for potential asymmetric backdoors via trapdoor one-way functions, and for security against quantum computing attacks.

The increasing use of Infrastructure as Code (IaC) in DevOps leads to benefits in speed and reliability of deployment operation, but extends to infrastructure challenges typical of software systems. IaC scripts can contain defects that result in security and reliability issues in the deployed infrastructure: techniques for detecting and preventing them are needed. We analyze and survey the current state of research in this respect by conducting a literature review on static analysis techniques for IaC. We describe analysis techniques, defect categories and platforms targeted by tools in the literature.

Synthetic static code analysis test suites are important to test the basic functionality of tools. We present a framework that uses different source code patterns to generate Cross Site Scripting and SQL injection test cases. A decision tree is used to determine if the test cases are vulnerable. The test cases are split into two test suites. The first test suite contains 258,432 test cases that have influence on the decision trees. The second test suite contains 20 vulnerable test cases with different data flow patterns. The test cases are scanned with two commercial static code analysis tools to show that they can be used to benchmark and identify problems of static code analysis tools. Expert interviews confirm that the decision tree is a solid way to determine the vulnerable test cases and that the test suites are relevant.

Code-graph based software defect prediction methods have become a research focus in SDP field. Among them, Code Property Graph is used as a form of data representation for code defects due to its ability to characterize the structural features and dependencies of defect codes. However, since the coarse granularity of Code Property Graph, redundant information which is not related to defects often attached to the characterization of software defects. Thus, it is a problem to be solved in how to locate software defects at a finer granularity in Code Property Graph. Static analysis is a technique for identifying software defects using set defect rules, and there are many proven static analysis tools in the industry. In this paper, we propose a method for locating specific types of defects in the Code Property Graph based on the result of static analysis tool. Experiments show that the location method based on static analysis results can effectively predict the location of specific defect types in real software program.

Static analyzers have become increasingly popular both as developer tools and as subjects of empirical studies. Whereas static analysis tools exist for disparate programming languages, the bulk of the empirical research has focused on the popular Java programming language. In this paper, we investigate to what extent some known results about using static analyzers for Java change when considering C\#-another popular object-oriented language. To this end, we combine two replications of previous Java studies. First, we study which static analysis tools are most widely used among C\# developers, and which warnings are more commonly reported by these tools on open-source C\# projects. Second, we develop and empirically evaluate EagleRepair: a technique to automatically fix code in response to static analysis warnings; this is a replication of our previous work for Java [20]. Our replication indicates, among other things, that 1) static code analysis is fairly popular among C\# developers too; 2) Re-Sharper is the most widely used static analyzer for C\#; 3) several static analysis rules are commonly violated in both Java and C\# projects; 4) automatically generating fixes to static code analysis warnings with good precision is feasible in C\#. The EagleRepair tool developed for this research is available as open source.

In today’s fast pacing world, cybercrimes have time and again proved to be one of the biggest hindrances in national development. According to recent trends, most of the times the victim’s data is breached by trapping it in a phishing attack. Security and privacy of user’s data has become a matter of tremendous concern. In order to address this problem and to protect the naive user’s data, a tool which may help to identify whether a window executable is malicious or not by doing static analysis on it has been proposed. As well as a comparative study has been performed by implementing different classification models like Logistic Regression, Neural Network, SVM. The static analysis approach used takes into parameters of the executables, analysis of properties obtained from PE Section Headers i.e. API calls. Comparing different model will provide the best model to be used for static malware analysis

In this paper, the axial symmetry is used to analyze the deformation and stress change of the wheel, so as to reduce the scale of analysis and reduce the cost in industrial production. Firstly, the material properties are defined, then the rotation section of the wheel is established, the boundary conditions are defined, the model is divided by finite element, the angular velocity and pressure load during rotation are applied, and the radial and axial deformation diagram, radial, axial and equivalent stress distribution diagram of the wheel are obtained through analysis and solution. The use of axisymmetric characteristics can reduce the analysis cost in the analysis, and can be applied to materials or components with such characteristics, so as to facilitate the design and improvement of products and reduce the production cost.

In this paper we consider cyber security requirements of the smart buildings. We identify cyber risks, threats, attack scenarios, security objectives and related security controls. The work was done as a part of a smart building design and construction work. From the controls identified w e concluded security practices for engineering-in smart buildings security. The paper provides an idea toward which system security engineers can strive in the basic design and implementation of the most critical components of the smart buildings. The intent of the concept is to help practitioners to avoid ad hoc approaches in the development of security mechanisms for smart buildings with shared space.

5G has significantly facilitated the development of attractive applications such as autonomous driving and telemedicine due to its lower latency, higher data rates, and enormous connectivity. However, there are still some security and privacy issues in 5G, such as network slicing privacy and flexibility and efficiency of network slicing selection. In the smart grid scenario, this paper proposes a 5G slice selection security scheme based on the Pohlig-Hellman algorithm, which realizes the protection of slice selection privacy data between User i(Ui) and Access and Mobility Management function (AMF), so that the data will not be exposed to third-party attackers. Compared with other schemes, the scheme proposed in this paper is simple in deployment, low in computational overhead, and simple in process, and does not require the help of PKI system. The security analysis also verifies that the scheme can accurately protect the slice selection privacy data between Ui and AMF.

The integration of distributed energy resources (DERs) and expansion of complex network in the distribution grid requires an advanced two-level state estimator to monitor the grid health at micro-level. The distribution state estimator will improve the situational awareness and resiliency of distributed power system. This paper implements a synchrophasors-based master state awareness (MSA) estimator to enhance the cybersecurity in distribution grid by providing a real-time estimation of system operating states to control center operators. In this paper, the implemented MSA estimator utilizes only phasor measurements, bus magnitudes and angles, from phasor measurement units (PMUs), deployed in local substations, to estimate the system states and also detects data integrity attacks, such as load tripping attack that disconnects the load. To validate the proof of concept, we implement this methodology in cyber-physical testbed environment at the Idaho National Laboratory (INL) Electric Grid Security Testbed. Further, to address the "valley of death" and support technology commercialization, field demonstration is also performed at the Critical Infrastructure Test Range Complex (CITRC) at the INL. Our experimental results reveal a promising performance in detecting load tripping attack and providing an accurate situational awareness through an alert visualization dashboard in real-time.

With the electric power distribution grid facing ever increasing complexity and new threats from cyber-attacks, situational awareness for system operators is quickly becoming indispensable. Identifying de-energized lines on the distribution system during a SCADA communication failure is a prime example where operators need to act quickly to deal with an emergent loss of service. Loss of cellular towers, poor signal strength, and even cyber-attacks can impact SCADA visibility of line devices on the distribution system. Neural Networks (NNs) provide a unique approach to learn the characteristics of normal system behavior, identify when abnormal conditions occur, and flag these conditions for system operators. This study applies a 24-hour load forecast for distribution line devices given the weather forecast and day of the week, then determines the current state of distribution devices based on changes in SCADA analogs from communicating line devices. A neural network-based algorithm is applied to historical events on Alabama Power's distribution system to identify de-energized sections of line when a significant amount of SCADA information is hidden.

The features of socio-cyber-physical systems are presented, which dictate the need to revise traditional management methods and transform the management system in such a way that it takes into account the presence of a person both in the control object and in the control loop. The use of situational control mechanisms is proposed. The features of this approach and its comparison with existing methods of situational awareness are presented. The comparison has demonstrated wider possibilities and scope for managing socio-cyber-physical systems. It is recommended to consider a wider class of types of relations that exist in socio-cyber-physical systems. It is indicated that such consideration can be based on the use of pseudo-physical logics considered in situational control. It is pointed out that it is necessary to design a classifier of situations (primarily in cyberspace), instead of traditional classifiers of threats and intruders.

According to the characteristics of security threats and massive users in power mobile applications, a mobile application security situational awareness method based on big data architecture is proposed. The method uses open-source big data technology frameworks such as Kafka, Flink, Elasticsearch, etc. to complete the collection, analysis, storage and visual display of massive power mobile application data, and improve the throughput of data processing. The security situation awareness method of power mobile application takes the mobile terminal threat index as the core, divides the risk level for the mobile terminal, and predicts the terminal threat index through support vector machine regression algorithm (SVR), so as to construct the security profile of the mobile application operation terminal. Finally, through visualization services, various data such as power mobile applications and terminal assets, security operation statistics, security strategies, and alarm analysis are displayed to guide security operation and maintenance personnel to carry out power mobile application security monitoring and early warning, banning disposal and traceability analysis and other decision-making work. The experimental analysis results show that the method can meet the requirements of security situation awareness for threat assessment accuracy and response speed, and the related results have been well applied in a power company.

Managing electricity effectively also means knowing as accurately as possible when, where and how electricity is used. Detailed metering and timely allocation of consumption can help identify specific areas where energy consumption is excessive and therefore requires action and optimization. All those interested in the measurement process (distributors, sellers, wholesalers, managers, ultimately customers and new prosumer figures - producers / consumers -) have an interest in monitoring and managing energy flows more efficiently, in real time.Smart meter plays a key role in sending data containing consumer measurements to both the producer and the consumer, thanks to chain 2. It allows you to connect consumption and production, during use and the customer’s identity, allowing billing as Time-of-Use or Real-Time Pricing, and through the new two-way channel, this information is also made available to the consumer / prosumer himself, enabling new services such as awareness of energy consumption at the very moment of energy use.This is made possible by latest generation devices that "talk" with the end user, which use chain 2 and the power line for communication.However, the implementation of smart meters and related digital technologies associated with the smart grid raises various concerns, including, privacy. This paper provides a comparative perspective on privacy policies for residential energy customers, moreover, it will be possible to improve security through the blockchain for the introduction of smart contracts.

Over the past decade, smart grids have been widely implemented. Real-time pricing can better address demand-side management in smart grids. Real-time pricing requires managers to interact more with consumers at the data level, which raises many privacy threats. Thus, we introduce differential privacy into the Real-time pricing for privacy protection. However, differential privacy leaves more space for an adversary to compromise the robustness of the system, which has not been well addressed in the literature. In this paper, we propose a novel active attack detection scheme against stealthy attacks, and then give the proof of correctness and effectiveness of the proposed scheme. Further, we conduct extensive experiments with real datasets from CER to verify the detection performance of the proposed scheme.

Any type of engineered design requires metrics for trading off both desirable and undesirable properties. For integrated circuits, typical properties include circuit size, performance, power, etc., where for example, performance is a desirable property and power consumption is not. Security metrics, on the other hand, are extremely difficult to develop because there are active adversaries that intend to compromise the protected circuitry. This implies metric values may not be static quantities, but instead are measures that degrade depending on attack effectiveness. In order to deal with this dynamic aspect of a security metric, a general attack model is proposed that enables the effectiveness of various security approaches to be directly compared in the context of an attack. Here, we describe, define and demonstrate that the metrics presented are both meaningful and measurable.

The vehicular networks extend the internet services to road edge. They allow users to stay connected offering them a set of safety and infotainment services like weather forecasts and road conditions. The security and privacy are essential issues in computing systems and networks. They are particularly important in vehicular networks due to their direct impact on the users’ safety on road. Various researchers have concentrated their efforts on resolving these two issues in vehicular networks. A great number of researches are found in literature and with still existing open issues and security risks to be solved, the research is continuous in this area. However, the researchers may face some difficulties in choosing the correct method to prove their works or to illustrate their excellency in comparison with existing solutions. In this paper, we review a set of evaluation methodologies and metrics to measure, proof or analyze privacy and security solutions. The aim of this review is to illuminate the readers about the possible existing methods to help them choose the correct techniques to use and reduce their difficulties.

This paper belongs to a sequence of manuscripts that discuss generic and easy-to-apply security metrics for Strong PUFs. These metrics cannot and shall not fully replace in-depth machine learning (ML) studies in the security assessment of Strong PUF candidates. But they can complement the latter, serve in initial PUF complexity analyses, and are much easier and more efficient to apply: They do not require detailed knowledge of various ML methods, substantial computation times, or the availability of an internal parametric model of the studied PUF. Our metrics also can be standardized particularly easily. This avoids the sometimes inconclusive or contradictory findings of existing ML-based security test, which may result from the usage of different or non-optimized ML algorithms and hyperparameters, differing hardware resources, or varying numbers of challenge-response pairs in the training phase.This first manuscript within the abovementioned sequence treats one of the conceptually most straightforward security metrics on that path: It investigates the effects that small perturbations in the PUF-challenges have on the resulting PUF-responses. We first develop and implement several sub-metrics that realize this approach in practice. We then empirically show that these metrics have surprising predictive power, and compare our obtained test scores with the known real-world security of several popular Strong PUF designs. The latter include (XOR) Arbiter PUFs, Feed-Forward Arbiter PUFs, and (XOR) Bistable Ring PUFs. Along the way, our manuscript also suggests techniques for representing the results of our metrics graphically, and for interpreting them in a meaningful manner.

This paper presents a scalable single-input-multiple-output DC/DC converter targeting load transient response and security improvement for low-power System-on-Chips (SoCs). A two-stage modular architecture is introduced to enable scalability. The shared switched-capacitor pre-charging circuits are implemented to improve load transient response and decouple correlations between inputs and outputs. The demo version of the converter has three identical outputs, each supporting 0.3V to 0.9V with a maximum load current of 150mA. Based on post-layout simulation results in 32nm CMOS process, the converter output provides 19.3V/μs reference tracking speed and 27mA/ns workload transitions with negligible voltage droops or spikes. No cross regulation is observed at any outputs with a worst-case voltage ripple of 68mV. Peak efficiency reaches 85.5% for each output. With variable delays added externally, the input-output correlations can change 10 times and for steady-state operation, such correlation factors are always kept below 0.05. The converter is also scaled to support 6 outputs with only 0.56mm2 more area and maintains same load transient response performance.

he growing trend towards network “softwarization” allows the creation and deployment of even complex network environments in a few minutes or seconds, rather than days or weeks as required by traditional methods. This revolutionary approach made it necessary to seek automatic processes to solve network security problems. One of the main issues in the automation of network security concerns the proper and efficient modeling of network traffic. In this paper, we describe two optimized Traffic Flows representation models, called Atomic Flows and Maximal Flows. In addition to the description, we have validated and evaluated the proposed models to solve two key network security problems - security verification and automatic configuration - showing the advantages and limitations of each solution.