Biblio

The vertiginous technological advance related to globalization and the new digital era has led to the design of new techniques and tools that deal with the risks of technology and information. Terms such as "cybersecurity" stand out, which corresponds to that area of computer science that is responsible for the development and implementation of information protection mechanisms and technological infrastructure, in order to deal with cyberattacks. Phishing is a crime that uses social engineering and technical subterfuge to steal personal identity data and financial account credentials from users, representing a high economic and financial risk worldwide, both for individuals and for large organizations. The objective of this research is to determine the ways to prevent phishing, by analyzing the characteristics of this computer fraud, the various existing modalities and the main prevention strategies, in order to increase the knowledge of users about this. subject, highlighting the importance of adequate training that allows establishing efficient mechanisms to detect and block phishing.

The main objective of this research is to increase security awareness against phishing attacks in the education sector by teaching users about phishing URLs. The educational media was made based on references from several previous studies that were used as basic references. Development of antiphishing game framework educational media using the extended DPE framework. Participants in this study were vocational and college students in the technology field. The respondents included vocational and college students, each with as many as 30 respondents. To assess the level of awareness and understanding of phishing, especially phishing URLs, participants will be given a pre-test before playing the game, and after completing the game, the application will be given a posttest. A paired t-test was used to answer the research hypothesis. The results of data analysis show differences in the results of increasing identification of URL phishing by respondents before and after using educational media of the anti-phishing game framework in increasing security awareness against URL phishing attacks. More serious game development can be carried out in the future to increase user awareness, particularly in phishing or other security issues, and can be implemented for general users who do not have a background in technology.

Forming a secure autonomous vehicle group is extremely challenging since we have to consider threats and vulnerability of autonomous vehicles. Existing studies focus on communications among risk-free autonomous vehicles, which lack metrics to measure passenger security and cargo values. This work proposes a novel autonomous vehicle group formation method. We introduce risk assessment scoring to assess passenger security and cargo values, and propose an autonomous vehicle group formation method based on it. Our vehicle group is composed of a master node, and a number of core and border ones. Finally, the extensive simulation results show that our method is better than a Connectivity Prediction-based Dynamic Clustering model and a Low-InDependently clustering architecture in terms of node survival time, average change count of master nodes, and average risk assessment scoring.

This research evaluates the accuracy of two methods of authorship prediction: syntactical analysis and n-gram, and explores its potential usage. The proposed algorithm measures n-gram, and counts adjectives, adverbs, verbs, nouns, punctuation, and sentence length from the training data, and normalizes each metric. The proposed algorithm compares the metrics of training samples to testing samples and predicts authorship based on the correlation they share for each metric. The severity of correlation between the testing and training data produces significant weight in the decision-making process. For example, if analysis of one metric approximates 100% positive correlation, the weight in the decision is assigned a maximum value for that metric. Conversely, a 100% negative correlation receives the minimum value. This new method of authorship validation holds promise for future innovation in fraud protection, the study of historical documents, and maintaining integrity within academia.

In recent times, the occurrence of malware attacks are increasing at an unprecedented rate. Particularly, the image-based malware attacks are spreading worldwide and many people get harmful malware-based images through the technique called steganography. In the existing system, only open malware and files from the internet can be identified. However, the image-based malware cannot be identified and detected. As a result, so many phishers make use of this technique and exploit the target. Social media platforms would be totally harmful to the users. To avoid these difficulties, Machine learning can be implemented to find the steganographic malware images (contents). The proposed methodology performs an automatic detection of malware and steganographic content by using Machine Learning. Steganography is used to hide messages from apparently innocuous media (e.g., images), and steganalysis is the approach used for detecting this malware. This research work proposes a machine learning (ML) approach to perform steganalysis. In the existing system, only open malware and files from the internet are identified but in the recent times many people get harmful malware-based images through the technique called steganography. Social media platforms would be totally harmful to the users. To avoid these difficulties, the proposed Machine learning has been developed to appropriately detect the steganographic malware images (contents). Father, the steganalysis method using machine learning has been developed for performing logistic classification. By using this, the users can avoid sharing the malware images in social media platforms like WhatsApp, Facebook without downloading it. It can be also used in all the photo-sharing sites such as google photos.

Probabilistic model checking is a useful technique for specifying and verifying properties of stochastic systems including randomized protocols and reinforcement learning models. However, these methods rely on the assumed structure and probabilities of certain system transitions. These assumptions may be incorrect, and may even be violated by an adversary who gains control of some system components. In this paper, we develop a formal framework for adversarial robustness in systems modeled as discrete time Markov chains (DTMCs). We base our framework on existing methods for verifying probabilistic temporal logic properties and extend it to include deterministic, memoryless policies acting in Markov decision processes (MDPs). Our framework includes a flexible approach for specifying structure-preserving and non structure-preserving adversarial models. We outline a class of threat models under which adversaries can perturb system transitions, constrained by an ε ball around the original transition probabilities. We define three main DTMC adversarial robustness problems: adversarial robustness verification, maximal δ synthesis, and worst case attack synthesis. We present two optimization-based solutions to these three problems, leveraging traditional and parametric probabilistic model checking techniques. We then evaluate our solutions on two stochastic protocols and a collection of Grid World case studies, which model an agent acting in an environment described as an MDP. We find that the parametric solution results in fast computation for small parameter spaces. In the case of less restrictive (stronger) adversaries, the number of parameters increases, and directly computing property satisfaction probabilities is more scalable. We demonstrate the usefulness of our definitions and solutions by comparing system outcomes over various properties, threat models, and case studies.

An accurate state estimation (SE) considering increased uncertainty by the high penetration of renewable energy systems (RESs) is more and more important to enhance situational awareness, and the optimal and resilient operation of the renewable-rich power grids. However, it is anticipated that adversaries who plan to manipulate the target power grid will generate attacks that inject inaccurate data to the SE using the vulnerabilities of the devices and networks. Among potential attack types, false data injection attack (FDIA) is gaining popularity since this can bypass bad data detection (BDD) methods implemented in the SE systems. Although numerous FDIA detection methods have been recently proposed, the uncertainty of system configuration that arises by the continuously increasing penetration of RESs has been been given less consideration in the FDIA algorithms. To address this issue, this paper proposes a new FDIA detection scheme that is applicable to renewable energy-rich power grids. A deep learning framework is developed in particular by synergistically constructing a Bidirectional Long Short-Term Memory (Bi-LSTM) with modern smart grid characteristics. The developed framework is evaluated on the IEEE 14-bus system integrating several RESs by using several attack scenarios. A comparison of the numerical results shows that the proposed FDIA detection mechanism outperforms the existing deep learning-based approaches in a renewable energy-rich grid environment.

Accurate and synchronized timing information is required by power system operators for controlling the grid infrastructure (relays, Phasor Measurement Units (PMUs), etc.) and determining asset positions. Satellite-based global positioning system (GPS) is the primary source of timing information. However, GPS disruptions today (both intentional and unintentional) can significantly compromise the reliability and security of our electric grids. A robust alternate source for accurate timing is critical to serve both as a deterrent against malicious attacks and as a redundant system in enhancing the resilience against extreme events that could disrupt the GPS network. To achieve this, we rely on the highly accurate, terrestrial atomic clock-based network for alternative timing and synchronization. In this paper, we discuss an experimental setup for an alternative timing approach. The data obtained from this experimental setup is continuously monitored and analyzed using various time deviation metrics. We also use these metrics to compute deviations of our clock with respect to the National Institute of Standards and Technologys (NIST) GPS data. The results obtained from these metric computations are elaborately discussed. Finally, we discuss the integration of the procedures involved, like real-time data ingestion, metric computation, and result visualization, in a novel microservices-based architecture for situational awareness.

Increasing connectivity and automation in vehicles leads to a greater potential attack surface. Such vulnerabilities within vehicles can also be used for auto-theft, increasing the potential for attackers to disable anti-theft mechanisms implemented by vehicle manufacturers. We utilize patterns derived from Controller Area Network (CAN) bus traffic to verify driver “behavior”, as a basis to prevent vehicle theft. Our proposed model uses semi-supervised learning that continuously profiles a driver, using features extracted from CAN bus traffic. We have selected 15 key features and obtained an accuracy of 99% using a dataset comprising a total of 51 features across 10 different drivers. We use a number of data analysis algorithms, such as J48, Random Forest, JRip and clustering, using 94K records. Our results show that J48 is the best performing algorithm in terms of training and testing (1.95 seconds and 0.44 seconds recorded, respectively). We also analyze the effect of using a sliding window on algorithm performance, altering the size of the window to identify the impact on prediction accuracy.

Real-time situational awareness (SA) plays an essential role in accurate and timely incident response. Maintaining SA is, however, extremely costly due to excessive false alerts generated by intrusion detection systems, which require prioritization and manual investigation by security analysts. In this paper, we propose a novel approach to prioritizing alerts so as to maximize SA, by formulating the problem as that of active learning in a hidden Markov model (HMM). We propose to use the entropy of the belief of the security state as a proxy for the mean squared error (MSE) of the belief, and we develop two computationally tractable policies for choosing alerts to investigate that minimize the entropy, taking into account the potential uncertainty of the investigations' results. We use simulations to compare our policies to a variety of baseline policies. We find that our policies reduce the MSE of the belief of the security state by up to 50% compared to static baseline policies, and they are robust to high false alert rates and to the investigation errors.

Confidentiality and integrity security are the key challenges in future 5G networks. To encounter these challenges, various signature and key agreement protocols are being implemented in 5G systems to secure high-speed mobile-to-mobile communication. Many security ciphers such as SNOW 3G, Advanced Encryption Standard (AES), and ZUC are used for 5G security. Among these protocols, the AES algorithm has been shown to achieve higher hardware efficiency and throughput in the literature. In this paper, we implement the AES algorithm on Field Programmable Gate Array (FPGA) and real-time performance factors of the AES algorithm were exploited to best fit the needs and requirements of 5G. In addition, several modifications such as partial pipelining and deep pipelining (partial pipelining with sub-module pipelining) are implemented on Virtex 6 FPGA ML60S board to improve the throughput of the proposed design.

Smart grid is the next generation for power generation, consumption and distribution. However, with the introduction of smart communication in such sensitive components, major risks from cybersecurity perspective quickly emerged. This survey reviews and reports on the state-of-the-art techniques for detecting cyber attacks in smart grids, mainly through machine learning techniques.

The growing maturity of orchestration languages is contributing to the elaboration of cloud composite services, whose resources may be deployed over different distributed infrastructures. These composite services are subject to changes over time, that are typically required to support cloud properties, such as scalability and rapid elasticity. In particular, the migration of their elementary resources may be triggered by performance constraints. However, changes induced by this migration may introduce vulnerabilities that may compromise the resources, or even the whole cloud service. In that context, we propose an automated SMT1-based security framework for supporting the migration of resources in cloud composite services, and preventing the occurrence of new configuration vulnerabilities. We formalize the underlying security automation based on SMT solving, in order to assess the migrated resources and select adequate counter-measures, considering both endogenous and exogenous security mechanisms. We then evaluate its benefits and limits through large series of experiments based on a proof-of-concept prototype implemented over the CVC4 commonly-used open-source solver. These experiments show a minimal overhead with regular operating systems deployed in cloud environments.

With the growing number of IoT applications and devices, IoT security breaches are a dangerous reality. Cost pressure and complexity of security tests for embedded systems and networked infrastructure are often the excuse for skipping them completely. In our paper we introduce SecLab security test lab to overcome that problem. Based on a flexible and lightweight architecture, SecLab allows developers and IoT security specialists to harden their systems with a low entry hurdle. The open architecture supports the reuse of existing external security test libraries and scalability for the assessment of complex IoT Systems. A reference implementation of security tests in a realistic IoT application scenario proves the approach.

Information Technology (IT) governance crosses the organization practices, culture, and policy that support IT management in controlling five key functions, which are strategic alignment, performance management, resource management, value delivery, and risk management. The line of sight is extended from the corporate strategy to the risk management, and risk controls are assessed against operational goals. Thus, the risk management model is concerned with ensuring that the corporate risks are sufficiently controlled and managed. Many organizations rely on IT services to facilitate and sustain their operations, which mandate the existence of a risk management model in their IT governance. This paper examines prior research based on IT governance by using a risk management framework. It also proposes a new method for calculating and classifying IT-related risks. Additionally, we assessed our technique with one of the critical IT services that proves the reliability and accuracy of the implemented model.

Design a new generation of smart power meter components, build a smart power network, implement power meter safety protection, and complete smart power meter network security protection. The new generation of smart electric energy meters mainly complete legal measurement, safety fee control, communication, control, calculation, monitoring, etc. The smart power utilization structure network consists of the master station server, front-end processor, cryptographic machine and master station to form a master station management system. Through data collection and analysis, the establishment of intelligent energy dispatching operation, provides effective energy-saving policy algorithms and strategies, and realizes energy-smart electricity use manage. The safety protection architecture of the electric energy meter is designed from the aspects of its own safety, full-scenario application safety, and safety management. Own security protection consists of hardware security protection and software security protection. The full-scene application security protection system includes four parts: boundary security, data security, password security, and security monitoring. Security management mainly provides application security management strategies and security responsibility division strategies. The construction of the intelligent electric energy meter network system lays the foundation for network security protection.

The purpose of this article is to consider one of the options for automating the process of collecting information from open sources when conducting penetration testing in an organization's information security audit using the capabilities of the Python programming language. Possible primary vectors for collecting information about the organization, personnel, software, and hardware are shown. The basic principles of operation of the software product are presented in a visual form, which allows automated analysis of information from open sources about the object under study.

Recently, research on AI-based network intrusion detection has been actively conducted. In previous studies, the machine learning models such as SVM (Support Vector Machine) and RF (Random Forest) showed consistently high performance, whereas the NB (Naïve Bayes) showed various performances with large deviations. In the paper, after analyzing the cause of the NB models showing various performances addressed in the several studies, we measured the performance of the Gaussian NB model according to the smoothing factor that is closely related to these causes. Furthermore, we compared the performance of the Gaussian NB model with that of the other models as a zero-day attack detection system. As a result of the experiment, the accuracy was 38.80% and 87.99% in case that the smoothing factor is 0 and default respectively, and the highest accuracy was 94.53% in case that the smoothing factor is 1e-01. In the experiment, we used only some types of the attack data in the NSL-KDD dataset. The experiments showed the applicability of the Gaussian NB model as a zero-day attack detection system in the future. In addition, it is clarified that the smoothing factor of the Gaussian NB model determines the shape of gaussian distribution that is related to the likelihood.

Dynamic Host Control Protocol (DHCP) is a protocol which provides IP addresses and network configuration parameters to the hosts present in the network. This protocol is deployed in small, medium, and large size organizations which removes the burden from network administrator to manually assign network parameters to every host in the network for establishing communication. Every vendor who plans to incorporate DHCP service in its device follows the working flow defined in Request for Comments (RFC). DHCP Starvation and DHCP Flooding attack are Denial of Service (DoS) attacks to prevents provision of IP addresses by DHCP. Port Security and DHCP snooping are built-in security features which prevents these DoS attacks. However, novel techniques have been devised to bypass these security features which uses ARP and ICMP protocol to perform the attack. The purpose of this research is to analyze implementation of DHCP in multiple devices to verify the involvement of both ARP and ICMP in the address acquisition process of DHCP as per RFC and to validate the results of prior research which assumes ARP or ICMP are used by default in all of devices.

Achieving agile and resilient autonomous capabilities for cyber defense requires moving past indicators and situational awareness into automated response and recovery capabilities. The objective of the AlphaSOC project is to use state of the art sequential decision-making methods to automatically investigate and mitigate attacks on cyber physical systems (CPS). To demonstrate this, we developed a simulation environment that models the distributed navigation control system and physics of a large ship with two rudders and thrusters for propulsion. Defending this control network requires processing large volumes of cyber and physical signals to coordi-nate defensive actions over many devices with minimal disruption to nominal operation. We are developing a Reinforcement Learning (RL)-based approach to solve the resulting sequential decision-making problem that has large observation and action spaces.

Blockchain and artificial intelligence are two technologies that, when combined, have the ability to help each other realize their full potential. Blockchains can guarantee the accessibility and consistent admittance to integrity safeguarded big data indexes from numerous areas, allowing AI systems to learn more effectively and thoroughly. Similarly, artificial intelligence (AI) can be used to offer new consensus processes, and hence new methods of engaging with Blockchains. When it comes to sensitive data, such as corporate, healthcare, and financial data, various security and privacy problems arise that must be properly evaluated. Interaction with Blockchains is vulnerable to data credibility checks, transactional data leakages, data protection rules compliance, on-chain data privacy, and malicious smart contracts. To solve these issues, new security and privacy-preserving technologies are being developed. AI-based blockchain data processing, either based on AI or used to defend AI-based blockchain data processing, is emerging to simplify the integration of these two cutting-edge technologies.

Throughout history, technological evolution has generated less desired side effects with impact on society. In the field of IT&C, there are ongoing discussions about the role of robots within economy, but also about their impact on the labour market. In the case of digital media systems, we talk about misinformation, manipulation, fake news, etc. Issues related to the protection of the citizen's life in the face of technology began more than 25 years ago; In addition to the many messages such as “the citizen is at the center of concern” or, “privacy must be respected”, transmitted through various channels of different entities or companies in the field of ICT, the EU has promoted a number of legislative and normative documents to protect citizens' rights and freedoms.

In the context of cybersecurity systems, trust is the firm belief that a system will behave as expected. Trustworthiness is the proven property of a system that is worthy of trust. Therefore, trust is ephemeral, i.e. trust can be broken; trustworthiness is perpetual, i.e. trustworthiness is verified and cannot be broken. The gap between these two concepts is one which is, alarmingly, often overlooked. In fact, the pressure to meet with the pace of operations for mission critical cross domain solution (CDS) development has resulted in a status quo of high-risk, ad hoc solutions. Trustworthiness, proven through formal verification, should be an essential property in any hardware and/or software security system. We have shown, in "vCDS: A Virtualized Cross Domain Solution Architecture", that developing a formally verified CDS is possible. virtual CDS (vCDS) additionally comes with security guarantees, i.e. confidentiality, integrity, and availability, through the use of a formally verified trusted computing base (TCB). In order for a system, defined by an architecture description language (ADL), to be considered trustworthy, the implemented security configuration, i.e. access control and data protection models, must be verified correct. In this paper we present the first and only security auditing tool which seeks to verify the security configuration of a CDS architecture defined through ADL description. This tool is useful in mitigating the risk of existing solutions by ensuring proper security enforcement. Furthermore, when coupled with the agile nature of vCDS, this tool significantly increases the pace of system delivery.

Security and Controls with Data privacy in Internet of Things (IoT) devices is not only a present and future technology that is projected to connect a multitude of devices, but it is also a critical survival factor for IoT to thrive. As the quantity of communications increases, massive amounts of data are expected to be generated, posing a threat to both physical device and data security. In the Internet of Things architecture, small and low-powered devices are widespread. Due to their complexity, traditional encryption methods and algorithms are computationally expensive, requiring numerous rounds to encrypt and decode, squandering the limited energy available on devices. A simpler cryptographic method, on the other hand, may compromise the intended confidentiality and integrity. This study examines two lightweight encryption algorithms for Android devices: AES and RSA. On the other hand, the traditional AES approach generates preset encryption keys that the sender and receiver share. As a result, the key may be obtained quickly. In this paper, we present an improved AES approach for generating dynamic keys.

In most PUF-based authentication schemes, a central server is usually engaged to verify the response of the device’s PUF to challenge bit-streams. However, the server availability may be intermittent in practice. To tackle such an issue, this paper proposes a new protocol for supporting distributed authentication while avoiding vulnerability to information leakage where CRPs could be retrieved from hacked devices and collectively used to model the PUF. The main idea is to provision for scrambling the challenge bit-stream in a way that is dependent on the verifier. The scrambling pattern varies per authentication round for each device and independently across devices. In essence, the scrambling function becomes node- and packetspecific and the response received by two verifiers of one device for the same challenge bit-stream could vary. Thus, neither the scrambling function can be reverted, nor the PUF can be modeled even by a collusive set of malicious nodes. The validation results using data of an FPGA-based implementation demonstrate the effectiveness of our approach in thwarting PUF modeling attacks by collusive actors. We also discuss the approach resiliency against impersonation, Sybil, and reverse engineering attacks.