Biblio

With the rapid development of information science and technology, the role of the Internet in daily life is becoming more and more important, but while bringing speed and convenience to the experience, network security issues are endless, and fighting cybercrime will be an eternal topic. In recent years, new types of cyberattacks have made defense and analysis difficult. For example, the memory of network attacks makes some key array evidence only temporarily exist in physical memory, which puts forward higher requirements for attack detection. The traditional memory forensic analysis method for persistent data is no longer suitable for a new type of network attack analysis. The continuous development of memory forensics gives people hope. This paper proposes a network attack detection model based on memory forensic analysis to detect whether the system is under attack. Through experimental analysis, this model can effectively detect network attacks with low overhead and easy deployment, providing a new idea for network attack detection.

As information and communication technologies evolve every day, so does the use of technology in our daily lives. Along with our increasing dependence on digital information assets, security vulnerabilities are becoming more and more apparent. Passwords are a critical component of secure access to digital systems and applications. They not only prevent unauthorized access to these systems, but also distinguish the users of such systems. Research on password predictability often relies on surveys or leaked data. Therefore, there is a gap in the literature for studies that consider real data in this regard. This study investigates the password security awareness of 161 computer engineering students enrolled in a Linux-based undergraduate course at Ataturk University. The study is conducted in two phases, and in the first phase, 12 dictionaries containing also real student data are formed. In the second phase of the study, a dictionary-based brute-force attack is utilized by means of a serial and parallel version of a Bash script to crack the students’ passwords. In this respect, the /etc/shadow file of the Linux system is used as a basis to compare the hashed versions of the guessed passwords. As a result, the passwords of 23 students, accounting for 14% of the entire student group, were cracked. We believe that this is an unacceptably high prediction rate for such a group with high digital literacy. Therefore, due to this important finding of the study, we took immediate action and shared the results of the study with the instructor responsible for administering the information security course that is included in our curriculum and offered in one of the following semesters.

Android controls the majority of the global OS market. Android Open Source Project (AOSP) is a very complex system with many layers including the apps, the Application Framework, the middle-ware, the customized Linux kernel, and the trusted components. Although security is implemented in every layer, the Application Framework forms an important of the attack surface due to managing the user interface and permissions. Android security has evolved over the years. The security flaws that have been found in the Application Framework led to a redesign of Android permissions. Part of this evolution includes fixes to the vulnerabilities that are publicly released in the monthly Android security bulletins. In this study, we analyze the CVEs listed in the Android security bulletin within the last 6 years. We focus on the Android application framework and investigate several research questions relating to 1) the security relevant components, 2) the type and amount of testing information for the security patches, and 3) the adequacy of the tests designed to test these patches. Our findings indicate that Android security testing practices can be further improved by designing security bulletin update specific tests, and by improving code coverage of patched files.

Exploring the efficient vulnerability scanning and detection technology of various tools is one fundamental aim of network security. This network security technique ameliorates the tremendous number of IoT security challenges and the threats they face daily. However, among various tools, Shodan Eye scanning technology has proven to be very helpful for network administrators and security personnel to scan, detect and analyze vulnerable ports and traffic in organizations' networks. This work presents a simulated network scanning activity and manual vulnerability analysis of an internet-connected industrial equipment of two chosen industrial networks (Industry A and B) by running Shodan on a virtually hosted (Oracle Virtual Box)-Linux-based operating system (Kali Linux). The result shows that the shodan eye is a a promising tool for network security and efficient vulnerability research.

Nowadays, dynamic testing tools have significantly expedited the discovery of bugs in the Linux kernel. When unveiling kernel bugs, they automatically generate reports, specifying the errors the Linux encounters. The error in the report implies the possible exploitability of the corresponding kernel bug. As a result, many security analysts use the manifested error to infer a bug’s exploitability and thus prioritize their exploit development effort. However, using the error in the report, security researchers might underestimate a bug’s exploitability. The error exhibited in the report may depend upon how the bug is triggered. Through different paths or under different contexts, a bug may manifest various error behaviors implying very different exploitation potentials. This work proposes a new kernel fuzzing technique to explore all the possible error behaviors that a kernel bug might bring about. Unlike conventional kernel fuzzing techniques concentrating on kernel code coverage, our fuzzing technique is more directed towards the buggy code fragment. It introduces an object-driven kernel fuzzing technique to explore various contexts and paths to trigger the reported bug, making the bug manifest various error behaviors. With the newly demonstrated errors, security researchers could better infer a bug’s possible exploitability. To evaluate our proposed technique’s effectiveness, efficiency, and impact, we implement our fuzzing technique as a tool GREBE and apply it to 60 real-world Linux kernel bugs. On average, GREBE could manifest 2+ additional error behaviors for each of the kernel bugs. For 26 kernel bugs, GREBE discovers higher exploitation potential. We report to kernel vendors some of the bugs – the exploitability of which was wrongly assessed and the corresponding patch has not yet been carefully applied – resulting in their rapid patch adoption.

Operating systems are essential software components for any computer. The goal of computer system manu-facturers is to provide a safe operating system that can resist a range of assaults. APTs (Advanced Persistent Threats) are merely one kind of attack used by hackers to penetrate organisations (APT). Here, we will apply the MITRE ATT&CK approach to analyze the security of Windows and Linux. Using the results of a series of vulnerability tests conducted on Windows 7, 8, 10, and Windows Server 2012, as well as Linux 16.04, 18.04, and its most current version, we can establish which operating system offers the most protection against future assaults. In addition, we have shown adversarial reflection in response to threats. We used ATT &CK framework tools to launch attacks on both platforms.

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.

Random numbers are essential for communications security, as they are widely employed as secret keys and other critical parameters of cryptographic algorithms. The Linux random number generator (LRNG) is the most popular open-source software-based random number generator (RNG). The security of LRNG is influenced by the overall design, especially the quality of entropy sources. Therefore, it is necessary to assess and quantify the quality of the entropy sources which contribute the main randomness to RNGs. In this paper, we perform an empirical study on the quality of entropy sources in LRNG with Linux kernel 5.6, and provide the following two findings. We first analyze two important entropy sources: jiffies and cycles, and propose a method to predict jiffies by cycles with high accuracy. The results indicate that, the jiffies can be correctly predicted thus contain almost no entropy in the condition of knowing cycles. The other important finding is the failure of interrupt cycles during system boot. The lower bits of cycles caused by interrupts contain little entropy, which is contrary to our traditional cognition that lower bits have more entropy. We believe these findings are of great significance to improve the efficiency and security of the RNG design on software platforms.

"Security first" is the most concerned issue of Linux administrators. Security refers to the integrity of data. The authentication security and integrity of data are higher than the privacy security of data. Firewall is used to realize the function of access control under Linux. It is divided into hardware or software firewall. No matter in which network, the firewall must work at the edge of the network. Our task is to define how the firewall works. This is the firewall's policies and rules, so that it can detect the IP and data in and out of the network. At present, there are three or four layers of firewalls on the market, which are called network layer firewalls, and seven layers of firewalls, which are actually the gateway of the agent layer. But for the seven layer firewall, no matter what your source port or target port, source address or target address is, it will check all your things. Therefore, the seven layer firewall is more secure, but it brings lower efficiency. Therefore, the usual firewall schemes on the market are a combination of the two. And because we all need to access from the port controlled by the firewall, the work efficiency of the firewall has become the most important control of how much data users can access. This paper introduces two types of firewalls iptables and TCP\_Wrappers. What are the differences between the use policies, rules and structures of the two firewalls? This is the problem to be discussed in this paper.

We demonstrate an in-house built Endpoint Detection and Response (EDR) for linux systems using open-sourced tools like Osquery and Elastic. The advantage of building an in-house EDR tools against using commercial EDR tools provides both the knowledge and the technical capability to detect and investigate security incidents. We discuss the architecture of the tools and advantages it offers. Specifically, in our method all the endpoint logs are collected at a common server which we leverage to perform correlation between events happening on different endpoints and automatically detect threats like pivoting and lateral movements. We discuss various attacks that can be detected by our tool.

While we cannot question the high performance capabilities of the kernel bypass approach in the network functions world, we recognize that the Linux kernel provides a rich ecosystem with an efficient resource management and an effective resource sharing ability that cannot be ignored. In this work we argue that by mixing kernel-bypass and in kernel processing can benefit applications and network function middleboxes. We leverage a high-performance user space TCP stack and recent additions to the Linux kernel to propose a hybrid approach (kernel-user space) to accelerate SDN/NFV deployments leveraging services of the reliable transport layer (i.e., stateful middleboxes, Layer 7 network functions and applications). Our results show that this approach enables highperformance, high CPU efficiency, and enhanced integration with the kernel ecosystem. We build our solution by extending mTCP which is the basis of some state-of-the-art L4-L7 NFV frameworks. By having more efficient CPU usage, NFV applications can have more CPU cycles available to run the network functions and applications logic. We show that for a CPU intense workload, mTCP/AF\_XDP can have up to 64% more throughput than the previous implementation. We also show that by receiving cooperation from the kernel, mTCP/AF\_XDP enables the creation of protection mechanisms for mTCP. We create a simulated DDoS attack and show that mTCP/AF\_XDP can maintain up to 287% more throughput than the unprotected system during the attack.

In this article, authors present the results of testing the modified HTB traffic control algorithm in an experimental setup. The algorithm is implemented as a Linux kernel module. An analysis of the experimental results revealed the effect of uneven packet loss in priority classes. In the second part of the article, the authors propose a solution to this problem by applying a distribution scheme for the excess of tokens, according to which excess class tokens are given to the leaf with the highest priority. The new modification of the algorithm was simulated in the AnyLogic environment. The results of an experimental study demonstrated that dividing the excess tokens of the parent class between daughter classes is less effective in terms of network performance than allocating the excess tokens to a high-priority class during the competition for tokens between classes. In general, a modification of the HTB algorithm that implements the proposed token surplus distribution scheme yields more consistent delay times for the high-priority class.

Much research is concentrated on improving models for host-based intrusion detection systems (HIDS). Typically, such research aims at improving a model's results (e.g., reducing the false positive rate) in the familiar static training/testing environment using the standard data sources. Matching advancements in the machine learning community, researchers in the syscall HIDS domain have developed many complex and powerful syscall-based models to serve as anomaly detectors. These models typically show an impressive level of accuracy while emphasizing on minimizing the false positive rate. However, with each proposed model iteration, we get further from the setting in which these models are intended to operate. As kernels become more ornate and hardened, the implementation space for anomaly detection models is narrowing. Furthermore, the rapid advancement of operating systems and the underlying complexity introduced dictate that the sometimes decades-old datasets have long been obsolete. In this paper, we attempt to bridge the gap between theoretical models and their intended application environments by examining the recent Linux kernel 5.7.0-rc1. In this setting, we examine the feasibility of syscall-based HIDS in modern operating systems and the constraints imposed on the HIDS developer. We discuss how recent advancements to the kernel have eliminated the previous syscall trace collect method of writing syscall table wrappers, and propose a new approach to generate data and place our detection model. Furthermore, we present the specific execution time and memory constraints that models must meet in order to be operable within their intended settings. Finally, we conclude with preliminary results from our model, which primarily show that in-kernel machine learning models are feasible, depending on their complexity.

Industrial Control Systems (ICS) have evolved in the last decade, shifting from proprietary software/hardware to contemporary embedded architectures paired with open-source operating systems. In contrast to the IT world, where continuous updates and patches are expected, decommissioning always-on ICS for security assessment can incur prohibitive costs to their owner. Thus, a solution for routinely assessing the cybersecurity posture of diverse ICS without affecting their operation is essential. Therefore, in this paper we introduce IFFSET, a platform that leverages full system emulation of Linux-based ICS firmware and utilizes fuzzing for security evaluation. Our platform extracts the file system and kernel information from a live ICS device, building an image which is emulated on a desktop system through QEMU. We employ fuzzing as a security assessment tool to analyze ICS specific libraries and find potential security threatening conditions. We test our platform with commercial PLCs, showcasing potential threats with no interruption to the control process.

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.

As a problematic, this article discusses the construction of a trusted computing environment (TCE) based on the introduction of digital watermarks (DW) into the modules of the software product of a Unix-like operating / Linux system (Linux OS). One of the threats faced by an information security operator is the illegal use of a program or its components by unscrupulous competitors as part of "foreign" programs. Thus, we are talking about the joint use of the license key and the DW, which can act as a comprehensive solution for protecting the Linux OS. The above confirms the relevance of creating a methodology for building a trusted environment in Unix-like based on the implementation of a digital watermark. In this paper, the parameters of using the digital watermark, the admissible memory of Unix-like systems are considered.

In the last couple of years ARM-based servers have been gradually adopted by cloud service providers and utilized in the data centers. Such tendency may provide great business opportunities for various companies in the industry. Hence, the ability to timely track the development trend of the ARM-based server ecosystem (ASE) from technical perspective is of great importance. In this paper the level of development of the ASE is quantitatively assessed based on open-source data analysis. In particular, statistical data is extracted from 42 Linux kernels to analyze the development process of the ASE. Furthermore, an estimate of the development trend of the ASE in the next 10 years is made based on the statistical data. The estimated results provide insight on when the ASE may become as mature as today's x86-based server ecosystem.

Today, Linux is one of the main operating systems (OS) used both on desktop computers and various mobile devices. This OS is also widely applied in state and municipal structures, including law enforcement agencies and automated control systems used in the Armed Forces of the Russian Federation. It's worth noting that the process of replacing the Linux OS with domestic protected OSs that use the Linux kernel has now begun. In this regard, the analysis of threats to information security of the Linux OS is highly relevant. In this article, the authors discuss the security problems of Linux OS associated with unauthorized user privileges increase, as a result of which an attacker can gain full control over the OS. The approaches to differentiating user privileges in Linux are analyzed and their advantages and disadvantages are considered. As an example, the causes of the vulnerability CVE-2018-14665 were identified and measures to eliminate it were proposed.

With the widespread of Artificial Intelligence (AI)-enabled security applications, there is a need for collecting heterogeneous and scalable data sources for effectively evaluating the performances of security applications. This paper presents the description of new datasets, named ToNİoT datasets that include distributed data sources collected from Telemetry datasets of Internet of Things (IoT) services, Operating systems datasets of Windows and Linux, and datasets of Network traffic. The paper aims to describe the new testbed architecture used to collect Linux datasets from audit traces of hard disk, memory and process. The architecture was designed in three distributed layers of edge, fog, and cloud. The edge layer comprises IoT and network systems, the fog layer includes virtual machines and gateways, and the cloud layer includes data analytics and visualization tools connected with the other two layers. The layers were programmatically controlled using Software-Defined Network (SDN) and Network-Function Virtualization (NFV) using the VMware NSX and vCloud NFV platform. The Linux ToNİoT datasets would be used to train and validate various new federated and distributed AI-enabled security solutions such as intrusion detection, threat intelligence, privacy preservation and digital forensics. Various Data analytical and machine learning methods are employed to determine the fidelity of the datasets in terms of examining feature engineering, statistics of legitimate and security events, and reliability of security events. The datasets can be publicly accessed from [1].

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.

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].

Performance and improved packet handling capacity against high traffic load are important requirements for an effective intrusion detection system (IDS). Snort is one of the most popular open-source intrusion detection system which runs on Linux. This research article discusses ways of enhancing the performance of Snort by modifying Linux key parameters related to NAPI packet reception mechanism within the Linux kernel networking subsystem. Our enhancement overcomes the current limitations related to NAPI throughput. We experimentally demonstrate that current default budget B value of 300 does not yield the best performance of Snort throughput. We show that a small budget value of 14 gives the best Snort performance in terms of packet loss both at Kernel subsystem and at the application level. Furthermore, we compare our results to those reported in the literature, and we show that our enhancement through tuning certain parameters yield superior performance.

The safety of critical cyber-physical IoT devices hinges on the security of their embedded software that implements control algorithms for monitoring and control of the associated physical processes, e.g., robotics and drones. Reverse engineering of the corresponding embedded controller software binaries enables their security analysis by extracting high-level, domain-specific, and cyber-physical execution semantic information from executables. We present MISMO, a domain-specific reverse engineering framework for embedded binary code in emerging cyber-physical IoT control application domains. The reverse engineering outcomes can be used for firmware vulnerability assessment, memory forensics analysis, targeted memory data attacks, or binary patching for dynamic selective memory protection (e.g., important control algorithm parameters). MISMO performs semantic-matching at an algorithmic level that can help with the understanding of any possible cyber-physical security flaws. MISMO compares low-level binary symbolic values and high-level algorithmic expressions to extract domain-specific semantic information for the binary's code and data. MISMO enables a finer-grained understanding of the controller by identifying the specific control and state estimation algorithms used. We evaluated MISMO on 2,263 popular firmware binaries by 30 commercial vendors from 6 application domains including drones, self-driving cars, smart homes, robotics, 3D printers, and the Linux kernel controllers. The results show that MISMO can accurately extract the algorithm-level semantics of the embedded binary code and data regions. We discovered a zero-day vulnerability in the Linux kernel controllers versions 3.13 and above.

Linux is used in a large variety of situations, from private homes on personal machines to businesses storing personal data on servers. This operating system is often seen as more secure than Windows or Mac OS X, but this does not mean that there are no security concerns to be had when running it. Attackers can crack simple passwords over a network, vulnerabilities can be exploited if firewalls do not close enough ports, and malware can be downloaded and run on a Linux system. In addition, sensitive information can be accessed through physical or network access if proper permissions are not set on the files or directories containing it. However, most of these attacks can be prevented by keeping a system up to date, maintaining a secure firewall, using an antivirus, making complex passwords, and setting strong file permissions. This paper presents a list of methods for securing a Linux system from both external and internal threats.

Operating systems such as Linux break the power of the root user into separate privileges (which Linux calls capabilities) and give processes the ability to enable privileges only when needed and to discard them permanently when the program no longer needs them. However, there is no method of measuring how well the use of such facilities reduces the risk of privilege escalation attacks if the program has a vulnerability. This paper presents PrivAnalyzer, an automated tool that measures how effectively programs use Linux privileges. PrivAnalyzer consists of three components: 1) AutoPriv, an existing LLVM-based C/C++ compiler which uses static analysis to transform a program that uses Linux privileges into a program that safely removes them when no longer needed, 2) ChronoPriv, a new LLVM C/C++ compiler pass that performs dynamic analysis to determine for how long a program retains various privileges, and 3) ROSA, a new bounded model checker that can model the damage a program can do at each program point if an attacker can exploit the program and abuse its privileges. We use PrivAnalyzer to determine how long five privileged open source programs retain the ability to cause serious damage to a system and find that merely transforming a program to drop privileges does not significantly improve security. However, we find that simple refactoring can considerably increase the efficacy of Linux privileges. In two programs that we refactored, we reduced the percentage of execution in which a device file can be read and written from 97% and 88% to 4% and 1%, respectively.