Biblio

Geospatial fog computing system offers various benefits as a platform for geospatial computing services closer to the end users, including very low latency, good mobility, precise position awareness, and widespread distribution. In recent years, it has grown quickly. Fog nodes' security is susceptible to a number of assaults, including denial of service and resource abuse, because to their widespread distribution, complex network environments, and restricted resource availability. This paper proposes a Quantum Key Distribution (QKD)-based geospatial quantum fog computing environment that offers a symmetric secret key negotiation protocol that can preserve information-theoretic security. In QKD, after being negotiated between any two fog nodes, the secret keys can be given to several users in various locations to maintain forward secrecy and long-term protection. The new geospatial quantum fog computing environment proposed in this work is able to successfully withstand a variety of fog computing assaults and enhances information security.

The internet has developed and transformed the world dramatically in recent years, which has resulted in several cyberattacks. Cybersecurity is one of society’s most serious challenge, costing millions of dollars every year. The research presented here will look into this area, focusing on malware that can establish botnets, and in particular, detecting connections made by infected workstations connecting with the attacker’s machine. In recent years, the frequency of network security incidents has risen dramatically. Botnets have previously been widely used by attackers to carry out a variety of malicious activities, such as compromising machines to monitor their activities by installing a keylogger or sniffing traffic, launching Distributed Denial of Service (DDOS) attacks, stealing the identity of the machine or credentials, and even exfiltrating data from the user’s computer. Botnet detection is still a work in progress because no one approach exists that can detect a botnet’s whole ecosystem. A detailed analysis of a botnet, discuss numerous parameter’s result of detection methods related to botnet attacks, as well as existing work of botnet identification in field of machine learning are discuss here. This paper focuses on the comparative analysis of various classifier based on design of botnet detection technique which are able to detect P2P botnet using machine learning classifier.

A good website quality is needed to meet user satisfaction. The value of the benefits of the web will be felt by many users if the web has very good quality. The ease of accessing the website is a reflection of the good quality of the website. The positive image of the web owner can be seen from the quality of the website. When doing research on the website of the Ministry of Agriculture, Directorate General of Food Crops, the researcher found several pages that did not meet the website category which were said to be of good quality. Based on these findings, the authors are interested in analyzing user satisfaction with the website to measure the quality of the website of the Ministry of Agriculture, Directorate General of Food Crops using the PIECES method (Performance, Information, Economy, Control/Security, Efficiency, Service). The results of the study indicate that the level of user satisfaction with the website has been indicated as SATISFIED on each indicator, however, in measuring the quality of the website using YSlow (the GTMetrix tools, Pingdom Website Speed Tools), and (Web of Trust) WOT found many deficiencies such as loading the website takes a long time, there are some pages that cannot be found (page not found) and so on. Therefore, the authors provide several recommendations for better website development.

Deep Learning (DL) algorithms have gained popularity owing to their practical problem-solving capacity. However, they suffer from a serious integrity threat, i.e., their vulnerability to adversarial attacks. In the quest for DL trustworthiness, recent works claimed the inherent robustness of Spiking Neural Networks (SNNs) to these attacks, without considering the variability in their structural spiking parameters. This paper explores the security enhancement of SNNs through internal structural parameters. Specifically, we investigate the SNNs robustness to adversarial attacks with different values of the neuron's firing voltage thresholds and time window boundaries. We thoroughly study SNNs security under different adversarial attacks in the strong white-box setting, with different noise budgets and under variable spiking parameters. Our results show a significant impact of the structural parameters on the SNNs' security, and promising sweet spots can be reached to design trustworthy SNNs with 85% higher robustness than a traditional non-spiking DL system. To the best of our knowledge, this is the first work that investigates the impact of structural parameters on SNNs robustness to adversarial attacks. The proposed contributions and the experimental framework is available online 11https://github.com/rda-ela/SNN-Adversarial-Attacks to the community for reproducible research.

This article describes attacks methods, vectors and technics used by threat actors during pandemic situations in the world. Identifies common targets of threat actors and cyber-attack tactics. The article analyzes cybersecurity challenges and specifies possible solutions and improvements in cybersecurity. Defines cybersecurity controls, which should be taken against analyzed attack vectors.

Nowadays, there is a flood of data such as naked body photos and child pornography, which is making people bloodless. In addition, people also distribute drugs through unknown dark channels. In particular, most transactions are being made through the Deep Web, the dark path. “Deep Web refers to an encrypted network that is not detected on search engine like Google etc. Users must use Tor to visit sites on the dark web” [4]. In other words, the Dark Web uses Tor's encryption client. Therefore, users can visit multiple sites on the dark Web, but not know the initiator of the site. In this paper, we propose the key idea based on the current status of such crimes and a crime information visual system for Deep Web has been developed. The status of deep web is analyzed and data is visualized using Java. It is expected that the program will help more efficient management and monitoring of crime in unknown web such as deep web, torrent etc.

The recent malware outbreaks have shown that the existing end-point security solutions are not robust enough to secure the systems from getting compromised. The techniques, like code obfuscation along with one or more zero-days, are used by malware developers for evading the security systems. These malwares are used for large-scale attacks involving Advanced Persistent Threats(APT), Botnets, Cryptojacking, etc. Cryptojacking poses a severe threat to various organizations and individuals. We are summarising multiple methods available for the detection of malware.

This article presents results and overview of conducted testing of active optical network devices. The base for the testing is originating in Kali Linux and penetration testing generally. The goal of tests is to either confirm or disprove a vulnerability of devices used in the tested polygon. The first part deals with general overview and topology of testing devices, the next part is dedicated to active and passive exploration and exploits. The last part provides a summary of the results.

This paper provides a Common Vulnerability Scoring System (CVSS) metric-based technique for classifying and analysing the prevailing Computer Network Security Vulnerabilities and Threats (CNSVT). The problem that is addressed in this paper, is that, at the time of writing this paper, there existed no effective approaches for analysing and classifying CNSVT for purposes of assessments based on CVSS metrics. The authors of this paper have achieved this by generating a CVSS metric-based dynamic Vulnerability Analysis Classification Countermeasure (VACC) criterion that is able to rank vulnerabilities. The CVSS metric-based VACC has allowed the computation of vulnerability Similarity Measure (VSM) using the Hamming and Euclidean distance metric functions. Nevertheless, the CVSS-metric based on VACC also enabled the random measuring of the VSM for a selected number of vulnerabilities based on the [Ma-Ma], [Ma-Mi], [Mi-Ci], [Ma-Ci] ranking score. This is a technique that is aimed at allowing security experts to be able to conduct proper vulnerability detection and assessments across computer-based networks based on the perceived occurrence by checking the probability that given threats will occur or not. The authors have also proposed high-level countermeasures of the vulnerabilities that have been listed. The authors have evaluated the CVSS-metric based VACC and the results are promising. Based on this technique, it is worth noting that these propositions can help in the development of stronger computer and network security tools.

Modern malware applies a rich arsenal of evasion techniques to render dynamic analysis ineffective. In turn, dynamic analysis tools take great pains to hide themselves from malware; typically this entails trying to be as faithful as possible to the behavior of a real run. We present a novel approach to malware analysis that turns this idea on its head, using an extreme abstraction of the operating system that intentionally strays from real behavior. The key insight is that the presence of malicious behavior is sufficient evidence of malicious intent, even if the path taken is not one that could occur during a real run of the sample. By exploring multiple paths in a system that only approximates the behavior of a real system, we can discover behavior that would often be hard to elicit otherwise. We aggregate features from multiple paths and use a funnel-like configuration of machine learning classifiers to achieve high accuracy without incurring too much of a performance penalty. We describe our system, TAMALES (The Abstract Malware Analysis LEarning System), in detail and present machine learning results using a 330K sample set showing an FPR (False Positive Rate) of 0.10% with a TPR (True Positive Rate) of 99.11%, demonstrating that extreme abstraction can be extraordinarily effective in providing data that allows a classifier to accurately detect malware.

The process for determining how to respond to a cyberattack involves evaluating many factors, including some with competing risks. Consequentially, decision makers in the private sector and policymakers in the U.S. government (USG) need a framework in order to make effective response decisions. The authors' research identified two competing risks: 1) the risk of not responding forcefully enough to deter a suspected attacker, and 2) responding in a manner that escalates a situation with an attacker. The authors also identified three primary factors that influence these risks: attribution confidence/time, the scale of the attack, and the relationship with the suspected attacker. This paper provides a framework to help decision makers understand how these factors interact to influence the risks associated with potential response options to cyberattacks. The views expressed do not reflect the official policy or position of the National Intelligence University, the Department of Defense, the U.S. Intelligence Community, or the U.S. Government.

In this paper, we discuss challenges when we try to automatically classify privacy policies using machine learning with words as the features. Since it is difficult for general public to understand privacy policies, it is necessary to support them to do that. To this end, the authors believe that machine learning is one of the promising ways because users can grasp the meaning of policies through outputs by a machine learning algorithm. Our final goal is to develop a system which automatically translates privacy policies into privacy labels [1]. Toward this goal, we classify sentences in privacy policies with category labels, using popular machine learning algorithms, such as a naive Bayes classifier.We choose these algorithms because we could use trained classifiers to evaluate keywords appropriate for privacy labels. Therefore, we adopt words as the features of those algorithms. Experimental results show about 85% accuracy. We think that much higher accuracy is necessary to achieve our final goal. By changing learning settings, we identified one reason of low accuracies such that privacy policies include many sentences which are not direct description of information about categories. It seems that such sentences are redundant but maybe they are essential in case of legal documents in order to prevent misinterpreting. Thus, it is important for machine learning algorithms to handle these redundant sentences appropriately.

PHP is one of the most popular web development tools in use today. A major concern though is the improper and insecure uses of the language by application developers, motivating the development of various static analyses that detect security vulnerabilities in PHP programs. However, many of these approaches do not handle recent, important PHP features such as object orientation, which greatly limits the use of such approaches in practice. In this paper, we present OOPIXY, a security analysis tool that extends the PHP security analyzer PIXY to support reasoning about object-oriented features in PHP applications. Our empirical evaluation shows that OOPIXY detects 88% of security vulnerabilities found in micro benchmarks. When used on real-world PHP applications, OOPIXY detects security vulnerabilities that could not be detected using state-of-the-art tools, retaining a high level of precision. We have contacted the maintainers of those applications, and two applications' development teams verified the correctness of our findings. They are currently working on fixing the bugs that lead to those vulnerabilities.

We present an overview of the field of malware analysis with emphasis on issues related to document engineering. We will introduce the field with a discussion of the types of malware, including executable binaries, malicious PDFs, polymorphic malware, ransomware, and exploit kits. We will conclude with our view of important research questions in the field. This is an updated version of last year's tutorial, with more information about web-based malware and malware targeting the Android market.

We introduce a system-level Simulation and Analysis Engine (SAE) framework based on dynamic binary instrumentation for fine-grained and customizable instruction-level introspection of everything that executes on the processor. SAE can instrument the BIOS, kernel, drivers, and user processes. It can also instrument multiple systems simultaneously using a single instrumentation interface, which is essential for studying scale-out applications. SAE is an x86 instruction set simulator designed specifically to enable rapid prototyping, evaluation, and validation of architectural extensions and program analysis tools using its flexible APIs. It is fast enough to execute full platform workloads–-a modern operating system can boot in a few minutes–-thus enabling research, evaluation, and validation of complex functionalities related to multicore configurations, virtualization, security, and more. To reach high speeds, SAE couples tightly with a virtual platform and employs both a just-in-time (JIT) compiler that helps simulate simple instructions efficiently and a fast interpreter for simulating new or complex instructions. We describe SAE's architecture and instrumentation engine design and show the framework's usefulness for single- and multi-system architectural and program analysis studies.

Cyber security operations centre (CSOC) is an essential business control aimed to protect ICT systems and support an organisation's Cyber Defense Strategy. Its overarching purpose is to ensure that incidents are identified and managed to resolution swiftly, and to maintain safe & secure business operations and services for the organisation. A CSOC framework is proposed comprising Log Collection, Analysis, Incident Response, Reporting, Personnel and Continuous Monitoring. Further, a Cyber Defense Strategy, supported by the CSOC framework, is discussed. Overlaid atop the strategy is the well-known Her Majesty's Government (HMG) Protective Monitoring Controls (PMCs). Finally, the difficulty and benefits of operating a CSOC are explained.

Design-time analysis and verification of distributed real-time embedded systems necessitates the modeling of the time-varying performance of the network and comparing that to application requirements. Earlier work has shown how to build a system network model that abstracted away the network's physical medium and protocols which govern its access and multiplexing. In this work we show how to apply a network medium channel access protocol, such as Time-Division Multiple Access (TDMA), to our network analysis methods and use the results to show that the abstracted model without the explicit model of the protocol is valid.