Biblio

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.

Wireless mesh networks are increasingly deployed as a flexible and low-cost alternative for providing wireless services for a variety of applications including community mesh networking, medical applications, and disaster ad hoc communications, sensor and IoT applications. However, challenges remain such as interference, contention, load imbalance, and congestion. To address these issues, previous work employ load adaptive routing based on load sensitive routing metrics. On the other hand, such approach does not immediately improve network performance because the load estimates used to choose routes are themselves affected by the resulting routing changes in a cyclical manner resulting to oscillation. Although this is not a new phenomenon and has been studied in wired networks, it has not been investigated extensively in wireless mesh and/or sensor networks. We present these instabilities and how they pose performance, security, and energy issues to these networks. Accordingly, we present a feedback-aware mapping system called FARM that handles these instabilities in a manner analogous to a control system with feedback control. Results show that FARM stabilizes routes that improves network performance in throughput, delay, energy efficiency, and security.

Fraud mechanisms have evolved from isolated actions performed by single individuals to complex criminal networks. This paper aims to contribute to the identification of potentially relevant nodes in fraud networks. Whilst traditional methods for fraud detection rely on identifying abnormal patterns, this paper proposes STARBRIDGE: a new linear and scalable, ranked out, parameter free method to identify fraudulent nodes and rings based on Bridging, Influence and Control metrics. This is applied to the telecommunications domain where fraudulent nodes form a star-bridge-star pattern. Over 75% of nodes involved in fraud denote control, bridging centrality and doubled the influence scores, when compared to non-fraudulent nodes in the same role, stars and bridges being chief positions.

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.

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.

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

Child facial expression recognition is a relatively less investigated area within affective computing. Children’s facial expressions differ significantly from adults; thus, it is necessary to develop emotion recognition frameworks that are more objective, descriptive and specific to this target user group. In this paper we propose the first approach that (i) constructs video-level heterogeneous graph representation for facial expression recognition in children, and (ii) predicts children’s facial expressions using the automatically detected Action Units (AUs). To this aim, we construct three separate length-independent representations, namely, statistical, spectral and graph at video-level for detailed multi-level facial behaviour decoding (AU activation status, AU temporal dynamics and spatio-temporal AU activation patterns, respectively). Our experimental results on the LIRIS Children Spontaneous Facial Expression Video Database demonstrate that combining these three feature representations provides the highest accuracy for expression recognition in children.

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.

The Internet of Things (IoT) is rapidly evolving, allowing physical items to share information and coordinate with other nodes, increasing IoT’s value and being widely applied to various applications. Radio Frequency Identification (RFID) is usually used in IoT applications to automate item identification by establishing symmetrical communication between the tag device and the reader. Because RFID reading data is typically in plain text, a security mechanism is required to ensure that the reading results from this RFID data remain confidential. Researchers propose a lightweight encryption algorithm framework for IoT-based RFID applications to address this security issue. Furthermore, this research assesses the implementation of lightweight encryption algorithms, such as Grain v1 and Espresso, as two systems scenarios. The Grain v1 encryption is the final eSTREAM project that accepts an 80-bit key, 64-bit IV, and has a 160-bit internal state with limited application. In contrast, the Espresso algorithm has been implemented in various applications such as 5G wireless communication. Furthermore, this paper tested the performance of each encryption algorithm in the microcontroller and inspected the network performance in an IoT system.

Study on the effect of nanosecond laser anneal (NLA) induced crystallization of ferroelectric (FE) Si-doped hafnium oxide (HSO) material is reported. The laser energy density (0.3 J/cm2 to 1.3 J/cm2) and pulse count (1.0 to 30) variations are explored as pathways for the HSO based metal-ferroelectric-metal (MFM) capacitors. The increase in energy density shows transition toward ferroelectric film crystallization monitored by the remanent polarization (2Pr) and coercive field (2Ec). The NLA conditions show maximum 2Pr (\$\textbackslashsim 24\textbackslash \textbackslashmu\textbackslashmathrmC/\textbackslashtextcmˆ2\$) comparable to the values obtained from reference rapid thermal processing (RTP). Reliability dependence in terms of fatigue (107 cycles) of MFMs on NLA versus RTP crystallization anneal is highlighted. The NLA based MFMs shows improved fatigue cycling at high fields for the low energy densities compared to an RTP anneal. The maximum fatigue cycles to breakdown shows a characteristic dependence on the laser energy density and pulse count. Leakage current and dielectric breakdown of NLA based MFMs at the transition of amorphous to crystalline film state is reported. The role of NLA based anneal on ferroelectric film crystallization and MFM stack reliability is reported in reference with conventional RTP based anneal.

Homomorphic encryption is suitable for a machine learning in the cloud such as a privacy-preserving machine learning. However, ordinary homomorphic public key encryption has a problem that public key holders can generate ciphertexts and anyone can execute homomorphic operations. In this paper, we will propose a solution based on the Keyed Homomorphic-Public Key Encryption proposed by Emura et al.

Cyberattacks have been progressed in the fields of Internet of Things, and artificial intelligence technologies using the advanced persistent threat (APT) method recently. The damage caused by ransomware is rapidly spreading among APT attacks, and the range of the damages of individuals, corporations, public institutions, and even governments are increasing. The seriousness of the problem has increased because ransomware has been evolving into an intelligent ransomware attack that spreads over the network to infect multiple users simultaneously. This study used open source endpoint detection and response tools to build and test a framework environment that enables systematic ransomware detection at the network and system level. Experimental results demonstrate that the use of EDR tools can quickly extract ransomware attack features and respond to attacks.

Lock design is an important mechanism for scheduling management and security protection in operating systems. However, there is no effective way to identify the differences and connections among lock models, and users need to spend considerable time to understand different lock architectures. In this paper, we propose a classification scheme that abstracts lock design into three types of models: basic spinlock, semaphore amount extension, lock chain structure, and verify the effectiveness of these three types of lock models in the context of current mainstream applications. We also investigate the specific details of applying this classification method, which can be used as a reference for developers to design lock models, thus shorten the software development cycle.

Supervisory control and data acquisition (SCADA) systems play pivotal role in the operation of modern critical infrastructures (CIs). Technological advancements, innovations, economic trends, etc. have continued to improve SCADA systems effectiveness and overall CIs’ throughput. However, the trends have also continued to expose SCADA systems to security menaces. Intrusions and attacks on SCADA systems can cause service disruptions, equipment damage or/and even fatalities. The use of conventional intrusion detection models have shown trends of ineffectiveness due to the complexity and sophistication of modern day SCADA attacks and intrusions. Also, SCADA characteristics and requirement necessitate exceptional security considerations with regards to intrusive events’ mitigations. This paper explores the viability of supervised learning algorithms in detecting intrusions specific to SCADA systems and their communication protocols. Specifically, we examine four supervised learning algorithms: Random Forest, Naïve Bayes, J48 Decision Tree and Sequential Minimal Optimization-Support Vector Machines (SMO-SVM) for evaluating SCADA datasets. Two SCADA datasets were used for evaluating the performances of our approach. To improve the classification performances, feature selection using principal component analysis was used to preprocess the datasets. Using prominent classification metrics, the SVM-SMO presented the best overall results with regards to the two datasets. In summary, results showed that supervised learning algorithms were able to classify intrusions targeted against SCADA systems with satisfactory performances.

Since the provision of digital services in our days (e.g. container management, transport of COVID vaccinations or LNG) in most economic sectors (e.g. maritime, health, energy) involve national, EU and non-EU stakeholders compose complex Supply Chain Services (SCS). The security of the SCS is most important and it emphasized in the NIS 2 directive [3] and it is a shared responsibility of all stakeholders involved that will need to be compliant with a scheme. In this paper we present an overview of the proposed Cybersecurity Certification Scheme for Supply Chain Services (EUSCS) as proposed by the European Commission (EC) project CYRENE [1]. The EUSCS scheme covers all the three assurance levels defined in the Cybersecurity Act (CSA) [2] taking into consideration the criticality of SCS according to the NIS 2 directive [3], the ENISA Threat Landscape for Supply Chain Attacks [4] and the CYRENE extended online Information Security Management System (ISMS) that allows all SCS stakeholders to provide and access all information needed for certification purposes making the transition from current national schemes in the EU easier.

Target attack identification and detection has always been a concern of network security in the current environment. However, the economic losses caused by DDoS attacks are also enormous. In recent years, DDoS attack detection has made great progress mainly in the user application layer of the network layer. In this paper, a review and discussion are carried out according to the different detection methods and platforms. This paper mainly includes three parts, which respectively review statistics-based machine learning detection, target attack detection on SDN platform and attack detection on cloud service platform. Finally, the research suggestions for DDoS attack detection are given.

Malicious cybersecurity activities have become increasingly worrisome for individuals and companies alike. While machine learning methods like Graph Neural Networks (GNNs) have proven successful on the malware detection task, their output is often difficult to understand. Explainable malware detection methods are needed to automatically identify malicious programs and present results to malware analysts in a way that is human interpretable. In this survey, we outline a number of GNN explainability methods and compare their performance on a real-world malware detection dataset. Specifically, we formulated the detection problem as a graph classification problem on the malware Control Flow Graphs (CFGs). We find that gradient-based methods outperform perturbation-based methods in terms of computational expense and performance on explainer-specific metrics (e.g., Fidelity and Sparsity). Our results provide insights into designing new GNN-based models for cyber malware detection and attribution.

The strategy of permanently allocating a frequency band in a wireless communication network to one application has led to exceptionally low utilization of the vacant spectrum. By utilizing the unused licensed spectrum along with the unlicensed spectrum, Cognitive Radio Sensor Network (CRSNs) ensures the efficiency of spectrum management. To utilize the spectrum dynamically it is important to safeguard the spectrum sensing. Cooperative Spectrum Sensing (CSS) is recommended for this task. CSS aims to provide reliable spectrum sensing. However, there are various vulnerabilities experienced in CSS which can influence the performance of the network. In this work, the focus is on the Byzantine attack in CSS and current security solutions available to avoid the Byzantines in CRSN.

With the widespread deployment of data-driven services, the demand for data volumes continues to grow. At present, many applications lack reliable human supervision in the process of data collection, which makes the collected data contain low-quality data or even malicious data. This low-quality or malicious data make AI systems potentially face much security challenges. One of the main security threats in the training phase of machine learning is data poisoning attacks, which compromise model integrity by contaminating training data to make the resulting model skewed or unusable. This paper reviews the relevant researches on data poisoning attacks in various task environments: first, the classification of attacks is summarized, then the defense methods of data poisoning attacks are sorted out, and finally, the possible research directions in the prospect.

The prevalence of mobile devices (smartphones) along with the availability of high-speed internet access world-wide resulted in a wide variety of mobile applications that carry a large amount of confidential information. Although popular mobile operating systems such as iOS and Android constantly increase their defenses methods, data shows that the number of intrusions and attacks using mobile applications is rising continuously. Experts use techniques to detect malware before the malicious application gets installed, during the runtime or by the network traffic analysis. In this paper, we first present the information about different categories of mobile malware and threats; then, we classify the recent research methods on mobile malware traffic detection.

Smart cities are a wide range of projects made to facilitate the problems of everyday life and ensure security. Our interest focuses only on the Intelligent Transport System (ITS) that takes care of the transportation issues using the Vehicular Ad-Hoc Network (VANET) paradigm as its base. VANETs are a promising technology for autonomous driving that provides many benefits to the user conveniences to improve road safety and driving comfort. VANET is a promising technology for autonomous driving that provides many benefits to the user's conveniences by improving road safety and driving comfort. The problem with such rapid development is the continuously increasing digital threats. Among all these threats, we will target the Sybil attack since it has been proved to be one of the most dangerous attacks in VANETs. It allows the attacker to generate multiple forged identities to disseminate numerous false messages, disrupt safety-related services, or misuse the systems. In addition, Machine Learning (ML) is showing a significant influence on classification problems, thus we propose a behavior-based classification algorithm that is tested on the provided VeReMi dataset coupled with various machine learning techniques for comparison. The simulation results prove the ability of our proposed mechanism to detect the Sybil attack in VANETs.

Acoustic wave (AW) synthesis methodologies have become popular among AW filter designers because they provide a fast and precise seed to start with the design of AW devices. Nowadays, with the increasing complexity of carrier aggregation, there is a strong necessity to develop synthesis methods more focused on multiport filtering schemes. However, when dealing with multiport filtering functions, numerical accuracy plays an important role to succeed with the synthesis process since polynomial degrees are much higher as compared to the standalone filter case. In addition to polynomial degree, the number set of polynomial coefficients is also an important source of error during the extraction of the circuital elements of the filter. Nonetheless, in this paper is demonstrated that coupling matrix approaches are the best choice when the objective is to synthesize filtering functions with complex roots in their characteristic polynomials, which is the case of the channel polynomials of the multiport device.

We present a system for interactive examination of learned security policies. It allows a user to traverse episodes of Markov decision processes in a controlled manner and to track the actions triggered by security policies. Similar to a software debugger, a user can continue or or halt an episode at any time step and inspect parameters and probability distributions of interest. The system enables insight into the structure of a given policy and in the behavior of a policy in edge cases. We demonstrate the system with a network intrusion use case. We examine the evolution of an IT infrastructure’s state and the actions prescribed by security policies while an attack occurs. The policies for the demonstration have been obtained through a reinforcement learning approach that includes a simulation system where policies are incrementally learned and an emulation system that produces statistics that drive the simulation runs.

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.

Internet of Things (IoT) and those protocol CoAP and MQTT has security issues that have entirely changed the security strategy should be utilized and behaved for devices restriction. Several challenges have been observed in multiple domains of security, but Distributed Denial of Service (DDoS) have actually dangerous in IoT that have RT. Thus, the IoT paradigm and those protocols CoAP and MQTT have been investigated to seek whether network services could be efficiently delivered for resources usage, managed, and disseminated to the devices. Internet of Things is justifiably joined with the best practices augmentation to make this task enriched. However, factors behaviors related to traditional networks have not been effectively mitigated until now. In this paper, we present and deep, qualitative, and comprehensive systematic mapping to find the answers to the following research questions, such as, (i) What is the state-of-the-art in IoT security, (ii) How to solve the restriction devices challenges via infrastructure involvement, (iii) What type of technical/protocol/ paradigm needs to be studied, and (iv) Security profile should be taken care of, (v) As the proposals are being evaluated: A. If in simulated/virtualized/emulated environment or; B. On real devices, in which case which devices. After doing a comparative study with other papers dictate that our work presents a timely contribution in terms of novel knowledge toward an understanding of formulating IoT security challenges under the IoT restriction devices take care.