Biblio

Mobile security remains a concern for multiple stakeholders. Safe user behavior is crucial key to avoid and mitigate mobile threats. The research used a survey design to capture key constructs of mobile user threat avoidance behavior. Analysis revealed that there is no significant difference between the two key drivers of secure behavior, threat appraisal and coping appraisal, for Android and iOS users. However, statistically significant differences in avoidance motivation and avoidance behavior of users of the two operating systems were displayed. This indicates that existing threat avoidance models may be insufficient to comprehensively deal with factors that affect mobile user behavior. A newly introduced variable, perceived security, shows a difference in the perceptions of their level of protection among the users of the two operating systems, providing a new direction for research into mobile security.

At present, the security protection of distribution automation system is faced with complex and diverse operating environment, and the main use of public network may bring greater security risks, there are still some deficiencies. According to the actual situation of distribution automation of China Southern Power Grid, this paper designs multiple protection technology, carries out encryption distribution terminal research, and realizes end-to-end longitudinal security protection of distribution automation system, which is effectively improving the anti-attack ability of distribution terminal.

In order to improve the security and reliability of information system and reduce the risk of vulnerability intrusion and attack, an automatic patch installation method of operating systems based on deep learning is proposed, If the installation is successful, the basic information of the system will be returned to the visualization server. If the installation fails, it is recommended to upgrading manually and display it on the patch detection visualization server. Through the practical application of statistical analysis, the statistical results show that the proposed method is significantly better than the original and traditional installation methods, which can effectively avoid the problem of client repeated download, and greatly improve the success rate of patch automatic upgrades. It effectively saves the upgrade cost and ensures the security and reliability of the information system.

The popularity of Unmanned Aerial Vehicles (UAV) or more commonly known as Drones is increasing recently. UAVs have tremendous potential in various industries, e.g., military, agriculture, transportation, movie, supply chain, and surveillance. UAVs are also popular among hobbyists for photography, racing, etc. Despite the possibilities, many UAV related security incidents are reported nowadays. UAVs can be targeted by malicious parties and if compromised, life-threatening activities can be performed using them. As a result, governments around the world have started to regulate the use of UAVs. We believe that UAVs need an intelligent and automated defense mechanism to ensure the safety of humans, properties, and the UAVs themselves. A major component where we can incorporate the defense mechanism is the operating system. In this paper, we investigate the security of existing operating systems used in consumer and commercial UAVs. We then survey various security issues of UAV operating systems and possible solutions. Finally, we discuss several research challenges for developing a secure operating system for UAVs.

In the continuously evolving environment, computer security has become a convenient challenge because of the rapid rise and expansion of the Internet. One of the most significant challenges to networks is attacks on network resources caused by inadequate network security. DHCP is defenseless to a number of attacks, such as DHCP rogue server attacks. This work is focused on developing a method of detecting these attacks and granting active host protection on GNU/Linux operating systems. Unauthorized DHCP servers can be easily arranged and compete with the legitimate server on the local network that can be the result of distributing incorrect IP addresses, malicious DNS server addresses, invalid routing information to unsuspecting clients, intercepting and eavesdropping on communications, and so on. The goal is to prevent the situations described above by recognizing untrusted DHCP servers and providing active host protection on the local network.

The Internet, originally an academic network for the rapid exchange of information, has moved over time into the commercial media, business and later industrial communications environment. Recently, it has been included as a part of cyberspace as a combat domain. Any device connected to the unprotected Internet is thus exposed to possible attacks by various groups and individuals pursuing various criminal, security and political objectives. Therefore, each such device must be set up to be as resistant as possible to these attacks. For the implementation of small home, academic or industrial systems, people very often use small computing system Raspberry PI, which is usually equipped with the operating system Raspbian Linux. Such a device is often connected to an unprotected Internet environment and if successfully attacked, can act as a gateway for an attacker to enter the internal network of an organization or home. This paper deals with security configuration of Raspbian Linux operating system for operation on public IP addresses in an unprotected Internet environment. The content of this paper is the conduction and analysis of an experiment in which five Raspbian Linux/Raspberry PI accounts were created with varying security levels; the easiest to attack is a simulation of the device of a user who has left the system without additional security. The accounts that follow gradually add further protection and security. These accounts are used to simulate a variety of experienced users, and in a practical experiment the effects of these security measures are evaluated; such as the number of successful / unsuccessful attacks; where the attacks are from; the type and intensity of the attacks; and the target of the attack. The results of this experiment lead to formulated conclusions containing an analysis of the attack and subsequent design recommendations and settings to secure such a device. The subsequent section of the paper discusses the implementation of a simple TCP server that is configured to listen to incoming traffic on preset ports; it simulates the behaviour of selected services on these ports. This server's task is to intercept unauthorized connection attempts to these ports and intercepting attempts to communicate or attack these services. These recorded attack attempts are analyzed in detail and formulated in the conclusion, including implications for the security settings of such a device. The overall result of this paper is the recommended set up of operating system Raspbian Linux to work on public IP addresses in an unfiltered Internet environment.

The course of operating system's labs usually fall behind the state of art technology. In this paper, we propose a Software Diversity-Assisted Defense (SDAD) lab based on software diversity, mainly targeting for students majoring in cyber security and computer science. This lab is consisted of multiple modules and covers most of the important concepts and principles in operating systems. Thus, the knowledge learned from the theoretical course will be deepened with the lab. For students majoring in cyber security, they can learn this new software diversity-based defense technology and understand how an exploit works from the attacker's side. The experiment is also quite stretchable, which can fit all level students.

Android kernel fuzzing is a research area of interest specifically for detecting kernel vulnerabilities which may allow attackers to obtain the root privilege. The number of Android mobile phones is increasing rapidly with the explosive growth of Android kernel drivers. Interface aware fuzzing is an effective technique to test the security of kernel driver. Existing researches rely on static analysis with kernel source code. However, in fact, there exist millions of Android mobile phones without public accessible source code. In this paper, we propose a hybrid interface recovery method for fuzzing kernels which can recover kernel driver interface no matter the source code is available or not. In white box condition, we employ a dynamic interface recover method that can automatically and completely identify the interface knowledge. In black box condition, we use reverse engineering to extract the key interface information and use similarity computation to infer argument types. We evaluate our hybrid algorithm on on 12 Android smartphones from 9 vendors. Empirical experimental results show that our method can effectively recover interface argument lists and find Android kernel bugs. In total, 31 vulnerabilities are reported in white and black box conditions. The vulnerabilities were responsibly disclosed to affected vendors and 9 of the reported vulnerabilities have been already assigned CVEs.

Existing cyber security solutions have been basically developed using knowledge-based models that often cannot trigger new cyber-attack families. With the boom of Artificial Intelligence (AI), especially Deep Learning (DL) algorithms, those security solutions have been plugged-in with AI models to discover, trace, mitigate or respond to incidents of new security events. The algorithms demand a large number of heterogeneous data sources to train and validate new security systems. This paper presents the description of new datasets, the so-called ToNİoT, which involve federated data sources collected from Telemetry datasets of IoT services, Operating system datasets of Windows and Linux, and datasets of Network traffic. The paper introduces the testbed and description of TONİoT datasets for Windows operating systems. The testbed was implemented in three layers: edge, fog and cloud. The edge layer involves IoT and network devices, the fog layer contains virtual machines and gateways, and the cloud layer involves cloud services, such as data analytics, linked to the other two layers. These layers were dynamically managed using the platforms of software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Windows datasets were collected from audit traces of memories, processors, networks, processes and hard disks. The datasets would be used to evaluate various AI-based cyber security solutions, including intrusion detection, threat intelligence and hunting, privacy preservation and digital forensics. This is because the datasets have a wide range of recent normal and attack features and observations, as well as authentic ground truth events. The datasets can be publicly accessed from this link [1].

This paper presents an introduction to functional safety through ISO 26262 focusing on system, software and hardware possible failures that bring security threats and discussion on DO 254. It discusses the approach to bridge the gap between different other hazard level and system ability to identify the particular fault and resolve it minimum time span possible. Results are analyzed by designing models to check and avoid all the failures, loophole prior development.

This paper studies the current status and future directions of RTOS (Real-Time Operating Systems) for time-sensitive CPS (Cyber-Physical Systems). GPOS (General Purpose Operating Systems) existed before RTOS but did not meet performance requirements for time sensitive CPS. Many GPOS have put forward adaptations to meet the requirements of real-time performance, and this paper compares RTOS and GPOS and shows their pros and cons for CPS applications. Furthermore, comparisons among select RTOS such as VxWorks, RTLinux, and FreeRTOS have been conducted in terms of scheduling, kernel, and priority inversion. Various tools for WCET (Worst-Case Execution Time) estimation are discussed. This paper also presents a CPS use case of RTOS, i.e. JetOS for avionics, and future advancements in RTOS such as multi-core RTOS, new RTOS architecture and RTOS security for CPS.

Information security is everyone's concern. Computer systems are used to store sensitive data. Any weakness in their reliability and security makes them vulnerable. The Common Vulnerability Scoring System (CVSS) is a commonly used scoring system, which helps in knowing the severity of a software vulnerability. In this research, we show the effectiveness of common machine learning algorithms in predicting the computer operating systems security using the published vulnerability data in Common Vulnerabilities and Exposures and National Vulnerability Database repositories. The Random Forest algorithm has the best performance, compared to other algorithms, in predicting the computer operating system vulnerability severity levels based on precision, recall, and F-measure evaluation metrics. In addition, a predictive model was developed to predict whether a newly discovered computer operating system vulnerability would allow attackers to cause denial of service to the subject system.

Cloud-based infrastructures have grown in popularity over the last decade leveraging virtualisation, server, storage, compute power and network components to develop flexible applications. The requirements for instantaneous deployment and reduced costs have led the shift from virtual machine deployment to containerisation, increasing the overall flexibility of applications and increasing performances. However, containers require a fully fleshed operating system to execute, increasing the attack surface of an application. Unikernels, on the other hand, provide a lightweight memory footprint, ease of application packaging and reduced start-up times. Moreover, Unikernels reduce the attack surface due to the self-contained environment only enabling low-level features. In this work, we provide an exhaustive description of the unikernel ecosystem; we demonstrate unikernel vulnerabilities and further discuss the security implications of Unikernel-enabled environments through different use-cases.

An exceptional feature of the development of modern operating systems based on the Linux kernel is their leading use in cloud technologies, mobile devices and the Internet of things, which is accompanied by the emergence of more and more security threats at the kernel level. In order to improve the security of existing and future Linux distributions, it is necessary to analyze the existing approaches and tools for automated vulnerability detection and to conduct experimental security testing of some current versions of the kernel. The research is based on fuzzing - a software testing technique, which consists in the automated detection of implementation errors by sending deliberately incorrect data to the input of the fuzzer and analyzing the program's response at its output. Using the Syzkaller software tool, which implements a code coverage approach, vulnerabilities of the Linux kernel level were identified in stable versions used in modern distributions. The direction of this research is relevant and requires further development in order to detect zero-day vulnerabilities in new versions of the kernel, which is an important and necessary link in increasing the security of the Linux operating system family.

Hardening is the process of configuring IT systems to ensure the security of the systems' components and data they process or store. The complexity of contemporary IT infrastructures, however, renders manual security hardening and maintenance a daunting task. In many organizations, security-configuration guides expressed in the SCAP (Security Content Automation Protocol) are used as a basis for hardening, but these guides by themselves provide no means for automatically implementing the required configurations. In this paper, we propose an approach to automatically extract the relevant information from publicly available security-configuration guides for Windows operating systems using natural language processing. In a second step, the extracted information is verified using the information of available settings stored in the Windows Administrative Template files, in which the majority of Windows configuration settings is defined. We show that our implementation of this approach can extract and implement 83% of the rules without any manual effort and 96% with minimal manual effort. Furthermore, we conduct a study with 12 state-of-the-art guides consisting of 2014 rules with automatic checks and show that our tooling can implement at least 97% of them correctly. We have thus significantly reduced the effort of securing systems based on existing security-configuration guides. In many organizations, security-configuration guides expressed in the SCAP (Security Content Automation Protocol) are used as a basis for hardening, but these guides by themselves provide no means for automatically implementing the required configurations. In this paper, we propose an approach to automatically extract the relevant information from publicly available security-configuration guides for Windows operating systems using natural language processing. In a second step, the extracted information is verified using the information of available settings stored in the Windows Administrative Template files, in which the majority of Windows configuration settings is defined. We show that our implementation of this approach can extract and implement 83% of the rules without any manual effort and 96% with minimal manual effort. Furthermore, we conduct a study with 12 state-of-the-art guides consisting of 2014 rules with automatic checks and show that our tooling can implement at least 97% of them correctly. We have thus significantly reduced the effort of securing systems based on existing security-configuration guides. In this paper, we propose an approach to automatically extract the relevant information from publicly available security-configuration guides for Windows operating systems using natural language processing. In a second step, the extracted information is verified using the information of available settings stored in the Windows Administrative Template files, in which the majority of Windows configuration settings is defined. We show that our implementation of this approach can extract and implement 83% of the rules without any manual effort and 96% with minimal manual effort. Furthermore, we conduct a study with 12 state-of-the-art guides consisting of 2014 rules with automatic checks and show that our tooling can implement at least 97% of them correctly. We have thus significantly reduced the effort of securing systems based on existing security-configuration guides. We show that our implementation of this approach can extract and implement 83% of the rules without any manual effort and 96% with minimal manual effort. Furthermore, we conduct a study with 12 state-of-the-art guides consisting of 2014 rules with automatic checks and show that our tooling can implement at least 97% of them correctly. We have thus significantly reduced the effort of securing systems based on existing security-configuration guides.

At present, the on-site safety problems of substations and critical power equipment are mainly through inspection methods. Still, manual inspection is difficult, time-consuming, and uninterrupted inspection is not possible. The current safety management is mainly guaranteed by rules and regulations and standardized operating procedures. In the on-site environment, it is very dependent on manual execution and confirmation, and the requirements for safety supervision and operating personnel are relatively high. However, the reliability, the continuity of control and patrol cannot be fully guaranteed, and it is easy to cause security vulnerabilities and cause security accidents due to personnel slackness. In response to this shortcoming, this paper uses edge computing and image processing techniques to discover security risks in time and designs a deep convolution attention mechanism network to perform image processing. Then the network is cropped and compressed so that it can be processed at the edge, and the results are aggregated to the cloud for unified management. A comprehensive security assessment module is designed in the cloud to conduct an overall risk assessment of the results reported by all edges, and give an alarm prompt. The experimental results in the real environment show the effectiveness of this method.

The problem of optimizing the security policy for the composite information system is formulated. Subject-object model for information system is used. Combining different types of security policies is formalized. The target function for the optimization task is recorded. The optimization problem for combining two discretionary security policies is solved. The case of combining two mandatory security policies is studied. The main problems of optimization the composite security policy are formulated. +50 CHMBOJIOB‼!

In the paper, an intrusion detection system to safeguard computer software is proposed. The detection is based on negative selection algorithm, inspired by the human immunity mechanism. It is composed of two stages, generation of receptors and anomaly detection. Experimental results of the proposed system are presented, analyzed, and concluded.

The security threats to mobile applications are growing explosively. Mobile apps flaws and security defects open doors for hackers to break in and access sensitive information. Defensive requirements analysis should be an integral part of secure mobile SDLC. Developers need to consider the information confidentiality and data integrity, to verify the security early in the development lifecycle rather than fixing the security holes after attacking and data leaks take place. Early eliminating known security vulnerabilities will help developers increase the security of apps and reduce the likelihood of exploitation. However, many software developers lack the necessary security knowledge and skills at the development stage, and that's why Secure Mobile Software Development education is very necessary for mobile software engineers. In this paper, we propose a guided security requirement analysis based on OWASP Mobile Top ten security risk recommendations for Android mobile software development and its traceability of the developmental controls in SDLC. Building secure apps immune to the OWASP Mobile Top ten risks would be an effective approach to provide very useful mobile security guidelines.

The paper provides an analysis of the performance of an administrative component that helps the hypervisor to manage the resources of guest operating systems under fluctuation workload. The additional administrative component provides an extra layer of security to the guest operating systems and system as a whole. In this study, an administrative component was implemented by using Xen-hypervisor based para-virtualization technique and assigned some additional roles and responsibilities that reduce hypervisor workload. The study measured the resource utilizations of an administrative component when excessive input/output load passes passing through the system. Performance was measured in terms of bandwidth and CPU utilisation Based on the analysis of administrative component performance recommendations have been provided with the goal to improve system availability. Recommendations included detection of the performance saturation point that indicates the necessity to start load balancing procedures for the administrative component in the virtualized environment.

Android operating systems have become a prime target for attackers as most of the market is currently dominated by Android users. The situation gets worse when users unknowingly download or sideload cloning applications, especially gaming applications that look like benign games. In this paper, we present, a dynamic Android gaming malware detection system based on system call analysis to classify malicious and legitimate games. We performed the dynamic system call analysis on normal and malicious gaming applications while applications are in execution state. Our analysis reveals the similarities and differences between benign and malware game system calls and shows how dynamically analyzing the behavior of malicious activity through system calls during runtime makes it easier and is more effective to detect malicious applications. Experimental analysis and results shows the efficiency and effectiveness of our approach.

Modern mobile operating systems store a lot of excessive information that can be used against its owner or organization, like a call history or various system logs. This article describes a universal way of preventing any mobile operating system or application from saving its data in device's internal storage without reducing their functionality. The goal of this work is creation of a software that solves the described problem and works on the bootloading stage. A general algorithm of the designed software, along with its main solutions and requirements, is presented in this paper. Hardware requirement, software testing results and general applications of this software are also listed in this paper.

Operating data analysis means to analyze the operating system logs, user operation logs, various types of alarms and security relevant configurations, etc. The purpose is to find whether there is an attack event, suspicious behaviors or improper configurations. It is an important part of risk assessment for enterprise intranet. However, due to the lack of information security knowledge or relevant experience, many people do not know how to properly implement it. In this article, we provided guidance on conducting operating data analysis and how to determine the security risk with the analysis results.

Today, most embedded systems use Dynamic Voltage and Frequency Scaling (DVFS) to minimize energy consumption and maximize performance. The DVFS technique works by regulating the important parameters that govern the amount of energy consumed in a system, voltage and frequency. For the implementation of this technique, the operating system (OS) includes software applications that dynamically control a voltage regulator or a frequency regulator or both. In this paper, we demonstrate for the first time a malicious use of the frequency regulator against a TrustZone-enabled System-on-Chip (SoC). We demonstrate a use of frequency scaling to create covert channel in a TrustZone-enabled heterogeneous SoC. We present four proofs of concept to transfer sensitive data from a secure entity in the SoC to a non-secure one. The first proof of concept is from a secure ARM core to outside of SoC. The second is from a secure ARM core to a non-secure one. The third is from a non-trusted third party IP embedded in the programmable logic part of the SoC to a non-secure ARM core. And the last proof of concept is from a secure third party IP to a non-secure ARM core.

The growing use of smart phones has also given opportunity to the intruders to create malicious apps thereby the security and privacy concerns of a novice user has also grown. This research focuses on the privacy concerns of a user who unknowingly installs a malicious apps created by the programmer. In this paper we created an attack scenario and created an app capable of compromising the privacy of the users. After accepting all the permissions by the user while installing the app, the app allows us to track the live location of the Android device and continuously sends the GPS coordinates to the server. This spying app is also capable of sending the call log details of the user. This paper evaluates two leading smart phone operating systems- Android and IOS to find out the flexibility provided by the two operating systems to their programmers to create the malicious apps.