Biblio

In this paper, we proposed a data security model of a big data analytical environment in the financial sector. Big Data can be seen as a trend in the advancement of technology that has opened the door to a new approach to understanding and decision making that is used to describe the vast amount of data (structured, unstructured and semi-structured) that is too time consuming and costly to load a relational database for analysis. The increase in cybercriminal attacks on an organization’s assets results in organizations beginning to invest in and care more about their cybersecurity points and controls. The management of business-critical data is an important point for which robust cybersecurity controls should be considered. The proposed model is applied in a datalake and allows the identification of security gaps on an analytical repository, a cybersecurity risk analysis, design of security components and an assessment of inherent risks on high criticality data in a repository of a regulated financial institution. The proposal was validated in financial entities in Lima, Peru. Proofs of concept of the model were carried out to measure the level of maturity focused on: leadership and commitment, risk management, protection control, event detection and risk management. Preliminary results allowed placing the entities in level 3 of the model, knowing their greatest weaknesses, strengths and how these can affect the fulfillment of business objectives.

Critical infrastructure is a key area in cybersecurity. In the U.S., it was front and center in 1997 with the report from the President’s Commission on Critical Infrastructure Protection (PCCIP), and now affects countries worldwide. Critical Infrastructure Protection must address all types of cybersecurity threats - insider threat, ransomware, supply chain risk management issues, and so on. Unsurprisingly, in the past 25 years, the risks and incidents have increased rather than decreased and appear in the news daily. As an important component of critical infrastructure protection, secure supply chain risk management must be integrated into development projects. Both areas have important implications for security requirements engineering.

Cloud computing is a unified management and scheduling model of computing resources. To satisfy multiple resource requirements for various application, edge computing has been proposed. One challenge of edge computing is cross-domain data security sharing problem. Ciphertext policy attribute-based encryption (CP-ABE) is an effective way to ensure data security sharing. However, many existing schemes focus on could computing, and do not consider the features of edge computing. In order to address this issue, we propose a cross-domain data security sharing approach for edge computing based on CP-ABE. Besides data user attributes, we also consider access control from edge nodes to user data. Our scheme first calculates public-secret key peer of each edge node based on its attributes, and then uses it to encrypt secret key of data ciphertext to ensure data security. In addition, our scheme can add non-user access control attributes such as time, location, frequency according to the different demands. In this paper we take time as example. Finally, the simulation experiments and analysis exhibit the feasibility and effectiveness of our approach.

Multi-label image classification task aims to predict multiple object labels in a given image and faces the challenge of variable-sized objects. Limited by the size of CNN convolution kernels, existing CNN-based methods have difficulty capturing global dependencies and effectively fusing multiple layers features, which is critical for this task. Recently, transformers have utilized multi-head attention to extract feature with long range dependencies. Inspired by this, this paper proposes a Cross-layer Aggregation with Transformers (CAT) framework, which leverages transformers to capture the long range dependencies of CNN-based features with Long Range Dependencies module and aggregate the features layer by layer with Cross-Layer Fusion module. To make the framework efficient, a multi-head pre-max attention is designed to reduce the computation cost when fusing the high-resolution features of lower-layers. On two widely-used benchmarks (i.e., VOC2007 and MS-COCO), CAT provides a stable improvement over the baseline and produces a competitive performance.

With the intelligent development of power system, due to the double-layer structure of smart grid and the characteristics of failure propagation across layers, the attack path also changes significantly: from single-layer to multi-layer and from static to dynamic. In response to the shortcomings of the single-layer attack path of traditional attack path identification methods, this paper proposes the idea of cross-layer attack, which integrates the threat propagation mechanism of the information layer and the failure propagation mechanism of the physical layer to establish a forward-backward bi-directional detection model. The model is mainly used to predict possible cross-layer attack paths and evaluate their path generation probabilities to provide theoretical guidance and technical support for defenders. The experimental results show that the method proposed in this paper can well identify the dynamic cross-layer attacks in the smart grid.

Unmanned Aerial Vehicle (UAV)-based streaming media transmission may become unstable when the bit rate generated by the source load exceeds the channel capacity owing to the UAV location and speed change. The change of the location can affect the network connection, leading to reduced transmission rate; the change of the flying speed can increase the video payload due to more I-frames. To improve the transmission reliability, in this paper we design a Client-Server-Ground&User (C-S-G&U) framework, and propose an algorithm of splitting-merging stream (SMS) for multi-link concurrent transmission. We also establish multiple transport links and configure the routing rules for the cross-layer design. The multi-link transmission can achieve higher throughput and significantly smaller end-to-end delay than a single-link especially in a heavy load situation. The audio and video data are packaged into the payload by the Real-time Transport Protocol (RTP) before being transmitted over the User Datagram Protocol (UDP). The forward error correction (FEC) algorithm is implemented to promote the reliability of the UDP transmission, and an encryption algorithm to enhance security. In addition, we propose a Quality of Service (QoS) strategy so that the server and the user can control the UAV to adapt its transmission mode dynamically, according to the load, delay, and packet loss. Our design has been implemented on an engineering platform, whose efficacy has been verified through comprehensive experiments.

Security of Internet of Things (IoT) is one of the most prevalent crucial challenges ever since. The diversified devices and their specification along with resource constrained protocols made it more complex to address over all security need of IoT. Denial of Service attacks, being the most powerful and frequent attacks on IoT have been considered so forth. However, the attack happens on multiple layers and thus a single detection technique for each layer is not sufficient and effective to combat these attacks. Current study focuses on cross layer intrusion detection system (IDS) for detection of multiple Denial of Service (DoS) attacks. Presently, two attacks at Transmission Control Protocol (TCP) and Routing Protocol are considered for Low power and Lossy Networks (RPL) and a neural network-based IDS approach has been proposed for the detection of such attacks. The attacks are simulated on NetSim and detection and the performance shows up to 80% detection probabilities.

In the Smart Grid paradigm, this critical infrastructure operation is increasingly exposed to cyber-threats due to the increased dependency on communication networks. An adversary can launch an attack on a power grid operation through False Data Injection into system measurements and/or through attacks on the communication network, such as flooding the communication channels with unnecessary data or intercepting messages. A cross-layered strategy that combines power grid data, communication grid monitoring and Machine Learning-based processing is a promising solution for detecting cyber-threats. In this paper, an implementation of an integrated solution of a cross-layer framework is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection of anomalies in the operation of power grid. IEEE 118-bus system is built in Simulink to provide a power grid testing environment and communication network data is emulated using SimComponents. The performance of the framework is investigated under various FDI and communication attacks.

To prevent all sorts of attacks, the technology of security service function chains (SFC) is proposed in recent years, it becomes an attractive research highlights. Dynamic orchestration algorithm can create SFC according to the resource usage of network security functions. The current research on creating SFC focuses on a single domain. However in reality the large and complex networks are divided into security domains according to different security levels and managed separately. Therefore, we propose a cross-security domain dynamic orchestration algorithm to create SFC for network security functions based on ant colony algorithm(ACO) and consider load balancing, shortest path and minimum delay as optimization objectives. We establish a network security architecture based on the proposed algorithm, which is suitable for the industrial vertical scenarios, solves the deployment problem of the dynamic orchestration algorithm. Simulation results verify that our algorithm achieves the goal of creating SFC across security domains and demonstrate its performance in creating service function chains to resolve abnormal traffic flows.

Side-channel attacks have been a constant threat to computing systems. In recent times, vulnerabilities in the architecture were discovered and exploited to mount and execute a state-of-the-art attack such as Spectre. The Spectre attack exploits a vulnerability in the Intel-based processors to leak confidential data through the covert channel. There exist some defenses to mitigate the Spectre attack. Among multiple defenses, hardware-assisted attack/intrusion detection (HID) systems have received overwhelming response due to its low overhead and efficient attack detection. The HID systems deploy machine learning (ML) classifiers to perform anomaly detection to determine whether the system is under attack. For this purpose, a performance monitoring tool profiles the applications to record hardware performance counters (HPC), utilized for anomaly detection. Previous HID systems assume that the Spectre is executed as a standalone application. In contrast, we propose an attack that dynamically generates variations in the injected code to evade detection. The attack is injected into a benign application. In this manner, the attack conceals itself as a benign application and gen-erates perturbations to avoid detection. For the attack injection, we exploit a return-oriented programming (ROP)-based code-injection technique that reuses the code, called gadgets, present in the exploited victim's (host) memory to execute the attack, which, in our case, is the CR-Spectre attack to steal sensitive data from a target victim (target) application. Our work focuses on proposing a dynamic attack that can evade HID detection by injecting perturbations, and its dynamically generated variations thereof, under the cloak of a benign application. We evaluate the proposed attack on the MiBench suite as the host. From our experiments, the HID performance degrades from 90% to 16%, indicating our Spectre-CR attack avoids detection successfully.

We propose a methodology for the simulation of electrostatic confinement wells in transistors at cryogenic temperatures. This is considered in the context of 22-nm fully depleted silicon-on-insulator transistors due to their potential for imple-menting quantum bits in scalable quantum computing systems. To overcome thermal fluctuations and improve decoherence times in most quantum bit implementations, they must be operated at cryogenic temperatures. We review the dominant sources of electric field at these low temperatures, including material interface work function differences and trapped interface charges. Intrinsic generation and dopant ionisation are shown to be negligible at cryogenic temperatures when using a mode of operation suitable for confinement. We propose studying cryogenic electrostatic confinement wells in transistors using a finite-element model simulation, and decoupling carrier transport generated fields.

While 5G Edge Computing along with IoT technology has transformed the future of healthcare data transmission, it presents security vulnerabilities and risks when transmitting patients' confidential information. Currently, there are very few reliable security solutions available for healthcare data that routes through SDN routers in 5G Edge Computing. These solutions do not provide cryptographic security from IoT sensor devices. In this paper, we studied how 5G edge computing integrated with IoT network helps healthcare data transmission for remote medical treatment, explored security risks associated with unsecured data transmission, and finally proposed a cryptographic end-to-end security solution initiated at IoT sensor devices and routed through SDN routers. Our proposed solution with cryptographic security initiated at IoT sensor goes through SDN control plane and data plane in 5G edge computing and provides an end-to-end secured communication from IoT device to doctor's office. A prototype built with two-layer encrypted communication has been lab tested with promising results. This analysis will help future security implementation for eHealth in 5G and beyond networks.

Critical infrastructures such as the electricity grid can be severely impacted by cyber-attacks on its supply chain. Hence, having a robust cybersecurity infrastructure and management system for the electricity grid is a high priority. This paper proposes a cyber-security protocol for defense against man-in-the-middle (MiTM) attacks to the supply chain, which uses encryption and cryptographic multi-party authentication. A cyber-physical simulator is utilized to simulate the power system, control system, and security layers. The correctness of the attack modeling and the cryptographic security protocol against this MiTM attack is demonstrated in four different attack scenarios.

There is a stark contrast between the state of cyber security of national infrastructure in Ireland and the efforts underway to support cyber security technologists to work in the country. Notable attacks have recently occurred against the national health service, universities, and various other state bodies, prompting an interest in changing the current situation. This paper presents an overview of the security projects, commercial establishments, and policy in Ireland.

Digital forensics is essential when performing in-depth crime investigations and evidence extraction, especially in the field of the Internet of Things, where there is a ton of information every second boosted with latest and smartest technological devices. However, the enormous growth of data and the nature of its complexity could constrain the data examination process since traditional data acquisition techniques are not applicable nowadays. Therefore, if the knowledge gap between digital forensics and the Internet of Things is not bridged, investigators will jeopardize the loss of a possible rich source of evidence that otherwise could act as a lead in solving open cases. The work aims to introduce examples of employing the latest Internet of Things forensics approaches as a panacea in this regard. The paper covers a variety of articles presenting the new Blockchain, fog, and video-based applications that can aid in easing the process of digital forensics investigation with a focus on the Internet of Things. The results of the review indicated that the above current trends are very promising procedures in the field of Internet of Things digital forensics and need to be explored and applied more actively.

Common Vulnerability Scoring System (CVSS) is intended to capture the key characteristics of a vulnerability and correspondingly produce a numerical score to indicate the severity. Important efforts are conducted for building a CVSS stochastic model in order to provide a high-level risk assessment to better support cybersecurity decision-making. However, these efforts consider nothing regarding HPC (High-Performance Computing) networks using a Science Demilitary Zone (DMZ) architecture that has special design principles to facilitate data transition, analysis, and store through in a broadband backbone. In this paper, an HPCvul (CVSS-based vulnerability and risk assessment) approach is proposed for HPC networks in order to provide an understanding of the ongoing awareness of the HPC security situation under a dynamic cybersecurity environment. For such a purpose, HPCvul advocates the standardization of the collected security-related data from the network to achieve data portability. HPCvul adopts an attack graph to model the likelihood of successful exploitation of a vulnerability. It is able to merge multiple attack graphs from different HPC subnets to yield a full picture of a large HPC network. Substantial results are presented in this work to demonstrate HPCvul design and its performance.

Cyber threats have been a major issue in the cyber security domain. Every hacker follows a series of cyber-attack stages known as cyber kill chain stages. Each stage has its norms and limitations to be deployed. For a decade, researchers have focused on detecting these attacks. Merely watcher tools are not optimal solutions anymore. Everything is becoming autonomous in the computer science field. This leads to the idea of an Autonomous Cyber Resilience Defense algorithm design in this work. Resilience has two aspects: Response and Recovery. Response requires some actions to be performed to mitigate attacks. Recovery is patching the flawed code or back door vulnerability. Both aspects were performed by human assistance in the cybersecurity defense field. This work aims to develop an algorithm based on Reinforcement Learning (RL) with a Convoluted Neural Network (CNN), far nearer to the human learning process for malware images. RL learns through a reward mechanism against every performed attack. Every action has some kind of output that can be classified into positive or negative rewards. To enhance its thinking process Markov Decision Process (MDP) will be mitigated with this RL approach. RL impact and induction measures for malware images were measured and performed to get optimal results. Based on the Malimg Image malware, dataset successful automation actions are received. The proposed work has shown 98% accuracy in the classification, detection, and autonomous resilience actions deployment.

The healthcare industry is confronted with a slew of significant challenges, including stringent regulations, privacy concerns, and rapidly rising costs. Many leaders and healthcare professionals are looking to new technology and informatics to expand more intelligent forms of healthcare delivery. Numerous technologies have advanced during the last few decades. Over the past few decades, pharmacy has changed and grown, concentrating less on drugs and more on patients. Pharmaceutical services improve healthcare's affordability and security. The primary invention was a cyber-infrastructure made up of smart gadgets that are connected to and communicate with one another. These cyber infrastructures have a number of problems, including privacy, trust, and security. These gadgets create cyber-physical systems for pharmaceutical care services in p-health. In the present period, cyber-physical systems for pharmaceutical care services are dealing with a variety of important concerns and demanding conditions, i.e., problems and obstacles that need be overcome to create a trustworthy and effective medical system. This essay offers a thorough examination of CPS's architectural difficulties and emerging tendencies.

Still in many countries COVID19 virus is changing its structure and creating damages in terms of economy and education. In India during the period of January 2022 third wave is on its high peak. Many colleges and schools are still forced to teach online. This paper describes how cyber security actionable or practical fundamental were taught by school or college teachers. Various cyber security tools are used to explain the actionable insight of the subject. Main Topics or concepts covered are MITM (Man In the Middle Attack) using ethercap tool in Kali Linux, spoofing methods like ARP (Address Resolution Protocol) spoofing and DNS (Domain Name System) spoofing, network intrusion detection using snort , finding information about packets using wireshark tool and other tools like nmap and netcat for finding the vulnerability. Even brief details were given about how to crack password using wireshark.

With the development of technology, the invention of computers, the use of cyberspace created by information communication systems and networks, increasing the effectiveness of knowledge in all aspects and the gains it provides have increased further the importance of cyber security day by day. In parallel with the developments in cyber space, the need for cyber defense has emerged with active and passive defense approaches for cyber security against internal and external cyber-attacks of increasing type, severity and complexity. In this framework, proactive cyber defense and deterrence strategies have started to be implemented with new techniques and methods.

Cyber security is everybody's responsibility. It is the capability of the person to protect or secure the use of cyberspace from cyber-attacks. Cyber security awareness is the combination of both knowing and doing to safeguard one's personal information or assets. Online threats continue to rise in the Philippines which is the focus of this study, to identify the level of cyber security awareness among the students and teachers of Occidental Mindoro State College (OMSC) Philippines. Results shows that the level of cyber security awareness in terms of Knowledge, majority of the students and teachers got the passing score and above however there are almost fifty percent got below the passing score. In terms of Practices, both the teachers and the students need to strengthen the awareness of system and browser updates to boost the security level of the devices used. More than half of the IT students are aware of the basic cyber security protocol but there is a big percentage in the Non-IT students which is to be considered. Majority of the teachers are aware of the basic cyber security protocols however the remaining number must be looked into. There is a need to intensity the awareness of the students in the proper etiquette in using the social media. Boost the basic cyber security awareness training to all students and teachers to avoid cybercrime victims.

Cyber threats can cause severe damage to computing infrastructure and systems as well as data breaches that make sensitive data vulnerable to attackers and adversaries. It is therefore imperative to discover those threats and stop them before bad actors penetrating into the information systems.Threats hunting algorithms based on machine learning have shown great advantage over classical methods. Reinforcement learning models are getting more accurate for identifying not only signature-based but also behavior-based threats. Quantum mechanics brings a new dimension in improving classification speed with exponential advantage. The accuracy of the AI/ML algorithms could be affected by many factors, from algorithm, data, to prejudicial, or even intentional. As a result, AI/ML applications need to be non-biased and trustworthy.In this research, we developed a machine learning-based cyber threat detection and assessment tool. It uses two-stage (both unsupervised and supervised learning) analyzing method on 822,226 log data recorded from a web server on AWS cloud. The results show the algorithm has the ability to identify the threats with high confidence.

Cyber attacks keep states, companies and individuals at bay, draining precious resources including time, money, and reputation. Attackers thereby seem to have a first mover advantage leading to a dynamic defender attacker game. Automated approaches taking advantage of Cyber Threat Intelligence on past attacks bear the potential to empower security professionals and hence increase cyber security. Consistently, there has been a lot of research on automated approaches in cyber risk management including works on predictive attack algorithms and threat hunting. Combining data on countermeasures from “MITRE Detection, Denial, and Disruption Framework Empowering Network Defense” and adversarial data from “MITRE Adversarial Tactics, Techniques and Common Knowledge” this work aims at developing methods that enable highly precise and efficient automatic incident response. We introduce Attack Incident Responder, a methodology working with simple heuristics to find the most efficient sets of counter-measures for hypothesized attacks. By doing so, the work contributes to narrowing the attackers first mover advantage. Experimental results are promising high average precisions in predicting effiective defenses when using the methodology. In addition, we compare the proposed defense measures against a static set of defensive techniques offering robust security against observed attacks. Furthermore, we combine the approach of automated incidence response to an approach for threat hunting enabling full automation of security operation centers. By this means, we define a threshold in the precision of attack hypothesis generation that must be met for predictive defense algorithms to outperform the baseline. The calculated threshold can be used to evaluate attack hypothesis generation algorithms. The presented methodology for automated incident response may be a valuable support for information security professionals. Last, the work elaborates on the combination of static base defense with adaptive incidence response for generating a bio-inspired artificial immune system for computerized networks.

Owing to the decreasing costs of distributed energy resources (DERs) as well as decarbonization policies, power systems are undergoing a modernization process. The large deployment of DERs together with internet of things (IoT) devices provide a platform for peer-to-peer (P2P) energy trading in active distribution networks. However, P2P energy trading with IoT devices have driven the grid more vulnerable to cyber-physical threats. To this end, in this paper, a resilience-oriented P2P energy exchange model is developed considering three phase unbalanced distribution systems. In addition, various scenarios for vulnerability assessment of P2P energy exchanges considering adverse prosumers and consumers, who provide false information regarding the price and quantity with the goal of maximum financial benefit and system operation disruption, are considered. Techno-economic survivability analysis against these attacks are investigated on a IEEE 13-node unbalanced distribution test system. Simulation results demonstrate that adverse peers can affect the physical operation of grid, maximize their benefits, and cause financial loss of other agents.

Artificial intelligence (AI) and machine learning (ML) have been used in transforming our environment and the way people think, behave, and make decisions during the last few decades [1]. In the last two decades everyone connected to the Internet either an enterprise or individuals has become concerned about the security of his/their computational resources. Cybersecurity is responsible for protecting hardware and software resources from cyber attacks e.g. viruses, malware, intrusion, eavesdropping. Cyber attacks either come from black hackers or cyber warfare units. Artificial intelligence (AI) and machine learning (ML) have played an important role in developing efficient cyber security tools. This paper presents Latest Cyber Security Tools Based on Machine Learning which are: Windows defender ATP, DarckTrace, Cisco Network Analytic, IBM QRader, StringSifter, Sophos intercept X, SIME, NPL, and Symantec Targeted Attack Analytic.