Biblio

A digital certificate is by far the most widely used artifact to establish secure electronic communication over the Internet. It certifies to its user that the public key encapsulated in it is associated with the subject of the certificate. A Public Key Infrastructure (PKI) is responsible to create, store, distribute, and revoke digital certificates. To establish a secure communication channel two unfamiliar entities rely on a common certificate issuer (a part of PKI) that vouches for both entities' certificates - thus authenticating each other via public keys listed in each other's certificates. Therefore, PKIs act as a trusted third party for two previously unfamiliar entities. Certificates are static data structures, their revocation status must be checked before usage; this step inadvertently involves a PKI for every secure channel establishment - leading to privacy violations of relying parties. As PKIs act as trust anchors for their subjects, any inadvertent event or malfeasance in PKI setup breaches the trust relationship leading to identity theft. Alternative PKI trust models, like PGP and SPKI, have been proposed but with limited deployment. With several retrofitting amendments to the prevalent X.509 standard, the standard has been serving its core objective of entity authentication but with modern requirements of contextual authentication, it is falling short to accommodate the evolving requirements. With the advent of blockchain as a trust management protocol, the time has come to rethink flexible alternatives to PKI core functionality; keeping in mind the modern-day requirements of contextual authentication-cum-authorization, weighted trust anchors, privacy-preservation, usability, and cost-efficient key management. In this paper, we assess this technology's complementary role in modern-day evolving security requirements. We discuss the feasibility of re-engineering PKIs with the help of blockchains, and identity networks.

Network Time Security (NTS) standardizes mechanisms that allow clients to authenticate timing information received via Network Time Protocol (NTP). NTS includes a new key establishment protocol, NTS-KE, and extension fields for NTPv4 which, when utilized together, allow clients to authenticate messages from time servers. Utilizing an open source implementation of each, we determine the existence and severity of any performance or scalability impact introduced by NTS when compared to NTP. We found that conducting individual authenticated time transfer takes approximately 116% longer when utilizing NTS over NTP. Additionally, we found that NTS-KE can only support approximately 2000 requests per second before a substantial and consistent increase in turnaround time is observed.

The paper considers the important problem of information protection in complex energy systems. The expert assessment of information protection in complex energy systems method has been developed. Based on the conducted research and data processing, a method of forming the analytical basis for decision-making aimed at ensuring the competitiveness of complex information protection systems has been developed.

Insider threats are steadily increasing, and the damage is also enormous. To prevent insider threats, security solutions, such as DLP, SIEM, etc., are being steadily developed. However, they have limitations due to the high rate of false positives. In this paper, we propose a data analysis method and methodology for responding to a technology leak incident. The future study may be performed based on the proposed methodology.

As IoT technologies mature, they are increasingly finding their way into more sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices continues to increase annually, they still present severe cyber-security vulnerabilities, turning them into potential targets and entry points to support further attacks. Naturally, as these nodes are compromised, attackers aim at setting up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes of a botnet in a cloaked fashion. Network covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT, and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim at analysing the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol. This will enable a better understanding of the involved risk and help supporting the development of further cyber-security mechanisms to mitigate this threat.

Security evaluation can be performed using a variety of analysis methods, such as attack trees, attack graphs, threat propagation models, stochastic Petri nets, and so on. These methods analyze the effect of attacks on the system, and estimate security attributes from different perspectives. However, they require information from experts in the application domain for properly capturing the key elements of an attack scenario: i) the attack paths a system could be subject to, and ii) the different characteristics of the possible adversaries. For this reason, some recent works focused on the generation of low-level security models from a high-level description of the system, hiding the technical details from the modeler.In this paper we build on an existing ontology framework for security analysis, available in the ADVISE Meta tool, and we extend it in two directions: i) to cover the attack patterns available in the CAPEC database, a comprehensive dictionary of known patterns of attack, and ii) to capture all the adversaries’ profiles as defined in the Threat Agent Library (TAL), a reference library for defining the characteristics of external and internal threat agents ranging from industrial spies to untrained employees. The proposed extension supports a richer combination of adversaries’ profiles and attack paths, and provides guidance on how to further enrich the ontology based on taxonomies of attacks and adversaries.

Big Data is a concept used in various sectors today, including the government sector in the Smart Government initiative. With a large amount of structured and unstructured data being managed, information assurance becomes important in adopting Big Data. However, so far, no research has focused on information assurance for Big Data. This paper identified information assurance factors for Big Data. This research used the systematic snapshot mapping approach to examine factors relating to information assurance from the literature related to Big Data from 2011 through 2021. The data extraction process in gathering 15 relevant papers. The findings revealed ten factors influencing the information assurance implementation for Big Data, with the security factor becoming the most concentrated factor with 18 sub-factors. The findings are expected to serve as a foundation for adopting information assurance for Big Data to develop an information assurance framework for Smart Government.

Modular exponentiation (ME) is a complex operation for several public-key cryptosystems (PKCs). Moreover, ME is expensive for resource-constrained devices in terms of computation time and energy consumption, especially when the exponent is large. ME is defined as the task of raising an integer x to power k and reducing the result modulo some integer n. Several methods to calculate ME have been proposed. In this paper, we present the efficient ME methods. We then implement the methods using different security levels of RSA keys on a Raspberry Pi. Finally, we give the fast ME method.

The rapid growth of number of devices that are connected to internet of things (IoT) networks, increases the severity of security problems that need to be solved in order to provide safe environment for network data exchange. The discovery of new vulnerabilities is everyday challenge for security experts and many novel methods for detection and prevention of intrusions are being developed for dealing with this issue. To overcome these shortcomings, artificial intelligence (AI) can be used in development of advanced intrusion detection systems (IDS). This allows such system to adapt to emerging threats, react in real-time and adjust its behavior based on previous experiences. On the other hand, the traffic classification task becomes more difficult because of the large amount of data generated by network systems and high processing demands. For this reason, feature selection (FS) process is applied to reduce data complexity by removing less relevant data for the active classification task and therefore improving algorithm's accuracy. In this work, hybrid version of recently proposed sand cat swarm optimizer algorithm is proposed for feature selection with the goal of increasing performance of extreme learning machine classifier. The performance improvements are demonstrated by validating the proposed method on two well-known datasets - UNSW-NB15 and CICIDS-2017, and comparing the results with those reported for other cutting-edge algorithms that are dealing with the same problems and work in a similar configuration.

Research data sharing requires provision of adequate security. The requirements for data privacy are extremely demanding for medical data that is reused for research purposes. To address these requirements, the research institutions must implement adequate security measurements, and this demands large effort and costs to do it properly. The usage of adequate access controls and data encryption are key approaches to effectively protect research data confidentiality; however, the management of the encryption keys is challenging. There are novel mechanisms that can be explored for managing access to the encryption keys and encrypted files. These mechanisms guarantee that data are accessed by authorised users and that auditing is possible. In this paper we explore these mechanisms to implement a secure research medical data sharing system. In the proposed system, the research data are stored on a secure cloud system. The data are partitioned into subsets, each one encrypted with a unique key. After the authorisation process, researchers are given rights to use one or more of the keys and to selectively access and decrypt parts of the dataset. Our proposed solution offers automated fine-grain access control to research data, saving time and work usually made manually. Moreover, it maximises and fortifies users' trust in data sharing through secure clouds solutions. We present an initial evaluation and conclude with a discussion about the limitations, open research questions and future work around this challenging topic.

Companies store increasing amounts of data, requiring the implementation of mechanisms to protect them from malicious people. There are techniques and procedures that aim to increase the security of computer systems, such as network protection services, firewalls. They are intended to filter packets that enter and leave a network. Its settings depend on security policies, which consist of documents that describe what is allowed to travel on the network and what is prohibited. The transcription of security policies into rules, written in native firewall language, that represent them, is the main source of errors in firewall configurations. In this work, concepts related to security between networks and firewalls are presented. Related works on security policies and their translations into firewall rules are also referenced. Furthermore, the developed tool, named Fireasy, is presented, which allows the modeling of security policies through graphic elements, and the maintenance of rules written in native firewall language, also representing them in graphic elements. Finally, a controlled experiment was conducted to validate the approach, which indicated, in addition to the correct functioning of the tool, an improvement in the translation of security policies into firewall rules using the tool. In the task of understanding firewall rules, there was a homogenization of the participants' performance when they used the tool.

Cloud computing is a new term that refers to the service provisioned over the Internet. It is considered one of the foremost prevailing standards within the Data Innovation (IT) industry these days. It offers capable handling and capacity assets as on-demand administrations at diminished fetched, and progressed productivity. It empowers sharing computing physical assets among cloud computing tents and offers on-demand scaling with taken toll effectiveness. Moreover, cloud computing plays an important role in data centers because they house virtually limitless computational and storage capacities that businesses and end-users can access and use via the Internet. In the context of cloud computing, fog computing refers to bringing services to the network’s edge. Fog computing gives cloud-like usefulness, such as information capacity space, systems, and compute handling control, yet with a more noteworthy scope and nearness since fog nodes are found close to d-user edge gadgets, leveraging assets and diminishing inactivity. The concepts of cloud computing and fog computing will be explored in this paper, and their features will be contrasted to determine the differences between them. Over 25 factors have been used to compare them.

The popularity of portable web browsers is increasing due to its convenient and compact nature along with the benefit of the data being stored and transferred easily using a USB drive. As technology gets updated frequently, developers are working on web browsers that can be portable in nature with additional security features like private mode browsing, built in ad blockers etc. The increased probability of using portable web browsers for carrying out nefarious activities is a result of cybercriminals with the thought that if they use portable web browsers in private mode it won't leave a digital footprint. Hence, the research paper aims at performing a comparative study of four portable web browsers namely Brave, TOR, Vivaldi, and Maxthon along with various memory acquisition tools to understand the quantity and quality of the data that can be recovered from the memory dump in two different conditions that is when the browser tabs were open and when the browser tabs were closed in a system to aid the forensic investigators.

The goals, objectives and criteria of the effectiveness of the creation, maintenance and use of the Digital Information Fund of Intellectual Property (DIFIP) are considered. A formalized methodology is proposed for designing DIFIPs, increasing its efficiency and quality, based on a set of interconnected models, methods and algorithms for analysis, synthesis and normalization distributed information management of DIFIP's structure; classification of databases users of patent and scientific and technical information; synthesis of optimal logical structures of the DIFIP database and thematic databases; assessing the quality of the database and ensuring the required level of data security.

This paper explores high throughput architectures for the substitution modules, which are an integral component of encryption algorithms. The security algorithms chosen belong to the category of lightweight crypto-primitives suitable for pervasive computing. The focus of this work is on the implementation of encryption algorithms on hardware platforms to improve speed and facilitate optimization in the area and power consumption of the design. In this work, the architecture for the encryption algorithms' substitution box (S-box) is modified using switching circuits (i.e., MUX-based) along with a logic generator and included in the overall cipher design. The modified architectures exhibit high throughput and consume less energy in comparison to the state-of-the-art designs. The percentage increase in throughput or maximum frequency differs according to the chosen algorithms discussed elaborately in this paper. The evaluation of various metrics specific to the design are executed at RFID-specific frequency so that they can be deployed in an IoT environment. The designs are mainly simulated and compared on Nexys4 DDR FPGA platform, along with a few other FPGAs, to meet similar design and implementation environments for a fair comparison. The application of the proposed S-box modification is explored for the healthcare scenario with promising results.

Modern connected vehicles are equipped with a large number of sensors, which enable a wide range of services that can improve overall traffic safety and efficiency. However, remote access to connected vehicles also introduces new security issues affecting both inter and intra-vehicle communications. In fact, existing intra-vehicle communication systems, such as Controller Area Network (CAN), lack security features, such as encryption and secure authentication for Electronic Control Units (ECUs). Instead, Original Equipment Manufacturers (OEMs) seek security through obscurity by keeping secret the proprietary format with which they encode the information. Recently, it has been shown that the reuse of CAN frame IDs can be exploited to perform CAN bus reverse engineering without physical access to the vehicle, thus raising further security concerns in a connected environment. This work investigates whether anonymizing the frames of each newly released vehicle is sufficient to prevent CAN bus reverse engineering based on frame ID matching. The results show that, by adopting Machine Learning techniques, anonymized CAN frames can still be fingerprinted and identified in an unknown vehicle with an accuracy of up to 80 %.

The damage or destruction of Critical Infrastructures (CIs) affect societies’ sustainable functioning. Therefore, it is crucial to have effective methods to assess the risk and resilience of CIs. Failure Mode and Effects Analysis (FMEA) and Failure Mode Effects and Criticality Analysis (FMECA) are two approaches to risk assessment and criticality analysis. However, these approaches are complex to apply to intricate CIs and associated Cyber-Physical Systems (CPS). We provide a top-down strategy, starting from a high abstraction level of the system and progressing to cover the functional elements of the infrastructures. This approach develops from FMECA but estimates risks and focuses on assessing resilience. We applied the proposed technique to a real-world CI, predicting how possible improvement scenarios may influence the overall system resilience. The results show the effectiveness of our approach in benchmarking the CI resilience, providing a cost-effective way to evaluate plausible alternatives concerning the improvement of preventive measures.

The security and privacy of the network in Internet of Things (IoT) systems are becoming more critical as we are more dependent on smart systems. Considering that packets are exchanged between the end user and the sensing devices, it is then important to ensure the security, privacy, and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for IoT systems. In this article, in order to improve the authentication and the encryption in IoT systems, we present a novel method of authentication and encryption based on elliptic curve cryptography (ECC) using random numbers generated by fuzzy logic. We evaluate our novel key generation method by using standard randomness tests, such as: frequency test, frequency test with mono block, run test, discrete Fourier transform (DFT) test, and advanced DFT test. Our results show superior performance compared to existing ECC based on shift registers. In addition, we apply some attack algorithms, such as Pollard’s \textbackslashrho and Baby-step Giant-step, to evaluate the vulnerability of the proposed scheme.

In today's power market, it's vital to keep electrical energy affordable to the vast majority of people while maintaining the highest degree of dependability. Due to which, the transmission network must operate beyond transfer limitations, generating congestion on transmission lines. These transmission line difficulties can be alleviated with the use of reactive power adjustment based on FACTS devices. Using a fuzzy tuned Static Synchronous Series Compensator [SSSC], this research proposes a novel method for calculating the effective damping oscillation control signals. The performance of the SSSC is compared to that of fuzzy logic-based controllers using PI controllers. According to the simulation results, the SSSC with fuzzy logic control effectively improves power flow under disrupted conditions

Considering the evidence that conditions accept a considerable place in each of the structures, owing to limited assets available at each sensor center, it is a difficult problem. Vitality safety is the primary concern in many of the implementations in remote sensor hubs. This is critical as the improvement in the lifetime of the device depends primarily on restricting the usage of vitality in sensor hubs. The rationing and modification of the usage of vitality are of the most serious value in this context. In a remote sensor arrangement, the fundamental test is to schedule measurements for the least use of vitality. These classification frameworks are used to frame the classes in the structure and help efficiently use the strength that burdens out the lifespan of the network. Besides, the degree of the center was taken into account in this work considering the measurement of cluster span as an improvement to the existing methods. The crucial piece of leeway of this suggested approach on affair clustering using fuzzy logic is which can increase the lifespan of the system by reducing the problem area problem word.

This paper aims to contribute towards creating ambient abnormal behavior detection for smart security system from real-time human pose estimation using fuzzy-based systems. Human poses from keypoint detected by pose estimation model are transformed to as angle positions of the axis between human bodies joints comparing to reference point in the axis x to deal with problem of the position change occurred when an individual move in the image. Also, the article attempts to resolve the problem of the ambiguity interpreting the poses with triangular fuzzy logic-based system that determines the detected individual behavior and compares to the poses previously learnt, trained, and recorded by the system. The experiment reveals that the accuracy of the system ranges between 90.75% (maximum) and 84% (minimum). This means that if the accuracy of the system at 85%. The system can be applied to guide future research for designing automatic visual human behavior detection systems.

Due to the simplicity of implementation and high threat level, SQL injection attacks are one of the oldest, most prevalent, and most destructive types of security attacks on Web-based information systems. With the continuous development and maturity of artificial intelligence technology, it has been a general trend to use AI technology to detect SQL injection. The selection of the sample set is the deciding factor of whether AI algorithms can achieve good results, but dataset with tagged specific category labels are difficult to obtain. This paper focuses on data augmentation to learn similar feature representations from the original data to improve the accuracy of classification models. In this paper, deep convolutional generative adversarial networks combined with genetic algorithms are applied to the field of Web vulnerability attacks, aiming to solve the problem of insufficient number of SQL injection samples. This method is also expected to be applied to sample generation for other types of vulnerability attacks.

Modern web applications are getting more sophisticated by using frameworks that make development easy, but pose challenges for security analysis tools. New analysis techniques are needed to handle such frameworks that grow in number and popularity. In this paper, we describe Gelato that addresses the most crucial challenges for a security-aware client-side analysis of highly dynamic web applications. In particular, we use a feedback-driven and state-aware crawler that is able to analyze complex framework-based applications automatically, and is guided to maximize coverage of security-sensitive parts of the program. Moreover, we propose a new lightweight client-side taint analysis that outperforms the state-of-the-art tools, requires no modification to browsers, and reports non-trivial taint flows on modern JavaScript applications. Gelato reports vulnerabilities with higher accuracy than existing tools and achieves significantly better coverage on 12 applications of which three are used in production.

Recent years have witnessed a surge in ransomware attacks. Especially, many a new variant of ransomware has continued to emerge, employing more advanced techniques distributing the payload while avoiding detection. This renders the traditional static ransomware detection mechanism ineffective. In this paper, we present our Hardware Anomaly Realtime Detection - Lightweight (HARD-Lite) framework that employs semi-supervised machine learning method to detect ransomware using low-level hardware information. By using an LSTM network with a weighted majority voting ensemble and exponential moving average, we are able to take into consideration the temporal aspect of hardware-level information formed as time series in order to detect deviation in system behavior, thereby increasing the detection accuracy whilst reducing the number of false positives. Testing against various ransomware across multiple families, HARD-Lite has demonstrated remarkable effectiveness, detecting all cases tested successfully. What's more, with a hierarchical design that distributing the classifier from the user machine that is under monitoring to a server machine, Hard-Lite enables good scalability as well.

Audit systems maintain detailed logs of security-related events on enterprise machines to forensically analyze potential incidents. In principle, these logs should be safely stored in a secure location (e.g., network storage) as soon as they are produced, but this incurs prohibitive slowdown to a monitored machine. Hence, existing audit systems protect batched logs asynchronously (e.g., after tens of seconds), but this allows attackers to tamper with unprotected logs.This paper presents HARDLOG, a practical and effective system that employs a novel audit device to provide fine-grained log protection with minimal performance slowdown. HARDLOG implements criticality-aware log protection: it ensures that logs are synchronously protected in the audit device before an infrequent security-critical event is allowed to execute, but logs are asynchronously protected on frequent non-critical events to minimize performance overhead. Importantly, even on non-critical events, HARDLOG ensures bounded-asynchronous protection: it sends log entries to the audit device within a tiny, bounded delay from their creation using well-known real-time techniques. To demonstrate HARDLOG’S effectiveness, we prototyped an audit device using commodity components and implemented a reference audit system for Linux. Our prototype achieves a bounded protection delay of 15 milliseconds at non-critical events alongside undelayed protection at critical events. We also show that, for diverse real-world programs, HARDLOG incurs a geometric mean performance slowdown of only 6.3%, hence it is suitable for many real-world deployment scenarios.