Malware Analysis - Android malware is continuously evolving at an alarming rate due to the growing vulnerabilities. This demands more effective malware detection methods. This paper presents DynaMalDroid, a dynamic analysis-based framework to detect malicious applications in the Android platform. The proposed framework contains three modules: dynamic analysis, feature engineering, and detection. We utilized the well-known CICMalDroid2020 dataset, and the system calls of apps are extracted through dynamic analysis. We trained our proposed model to recognize malware by selecting features obtained through the feature engineering module. Further, with these selected features, the detection module applies different Machine Learning classifiers like Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, Naïve-Bayes, K-Nearest Neighbour, and AdaBoost, to recognize whether an application is malicious or not. The experiments have shown that several classifiers have demonstrated excellent performance and have an accuracy of up to 99\%. The models with Support Vector Machine and AdaBoost classifiers have provided better detection accuracy of 99.3\% and 99.5\%, respectively.

Malware Analysis - The rising use of smartphones each year is matched by the development of the smartphone s operating system, Android. Due to the immense popularity of the Android operating system, many unauthorized users (in this case, the attackers) wish to exploit this vulnerability to get sensitive data from every Android user. The flubot malware assault, which happened in 2021 and targeted Android devices practically globally, is one of the attacks on Android smartphones. It was known at the time that the flubot virus stole information, particularly from banking applications installed on the victim s device. To prevent this from happening again, we research the signature and behavior of flubot malware. In this study, a hybrid analysis will be conducted on three samples of flubot malware that are available on the open-source Hatching Triage platform. Using the Android Virtual Device (AVD) as the primary environment for malware installation, the analysis was conducted with the Android Debug Bridge (ADB) and Burpsuite as supporting tools for dynamic analysis. During the static analysis, the Mobile Security Framework (MobSF) and the Bytecode Viewer were used to examine the source code of the three malware samples. Analysis of the flubot virus revealed that it extracts or drops dex files on the victim s device, where the file is the primary malware. The Flubot virus will clone the messaging application or Short Message Service (SMS) on the default device. Additionally, we discovered a form of flubot malware that operates as a Domain Generation Algorithm (DGA) and communicates with its Command and Control (C\&C) server.

Malware Analysis - The effective security system improvement from malware attacks on the Android operating system should be updated and improved. Effective malware detection increases the level of data security and high protection for the users. Malicious software or malware typically finds a means to circumvent the security procedure, even when the user is unaware whether the application can act as malware. The effectiveness of obfuscated android malware detection is evaluated by collecting static analysis data from a data set. The experiment assesses the risk level of which malware dataset using the hash value of the malware and records malware behavior. A set of hash SHA256 malware samples has been obtained from an internet dataset and will be analyzed using static analysis to record malware behavior and evaluate which risk level of the malware. According to the results, most of the algorithms provide the same total score because of the multiple crime inside the malware application.

Industrial Control Systems - With the introduction of the national “carbon peaking and carbon neutrality” strategic goals and the accelerated construction of the new generation of power systems, cloud applications built on advanced IT technologies play an increasingly important role in meeting the needs of digital power business. In view of the characteristics of the current power industrial control system operation support cloud platform with wide coverage, large amount of log data, and low analysis intelligence, this paper proposes a cloud platform network security behavior audit method based on FP-Growth association rule algorithm, aiming at the uniqueness of the operating data of the cloud platform that directly interacts with the isolated system environment of power industrial control system. By using the association rule algorithm to associate and classify user behaviors, our scheme formulates abnormal behavior judgment standards, establishes an automated audit strategy knowledge base, and improves the security audit efficiency of power industrial control system operation support cloud platform. The intelligent level of log data analysis enables effective discovery, traceability and management of internal personnel operational risks.

Android malware is continuously evolving at an alarming rate due to the growing vulnerabilities. This demands more effective malware detection methods. This paper presents DynaMalDroid, a dynamic analysis-based framework to detect malicious applications in the Android platform. The proposed framework contains three modules: dynamic analysis, feature engineering, and detection. We utilized the well-known CICMalDroid2020 dataset, and the system calls of apps are extracted through dynamic analysis. We trained our proposed model to recognize malware by selecting features obtained through the feature engineering module. Further, with these selected features, the detection module applies different Machine Learning classifiers like Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, Naïve-Bayes, K-Nearest Neighbour, and AdaBoost, to recognize whether an application is malicious or not. The experiments have shown that several classifiers have demonstrated excellent performance and have an accuracy of up to 99\%. The models with Support Vector Machine and AdaBoost classifiers have provided better detection accuracy of 99.3\% and 99.5\%, respectively.

The rising use of smartphones each year is matched by the development of the smartphone s operating system, Android. Due to the immense popularity of the Android operating system, many unauthorized users (in this case, the attackers) wish to exploit this vulnerability to get sensitive data from every Android user. The flubot malware assault, which happened in 2021 and targeted Android devices practically globally, is one of the attacks on Android smartphones. It was known at the time that the flubot virus stole information, particularly from banking applications installed on the victim s device. To prevent this from happening again, we research the signature and behavior of flubot malware. In this study, a hybrid analysis will be conducted on three samples of flubot malware that are available on the open-source Hatching Triage platform. Using the Android Virtual Device (AVD) as the primary environment for malware installation, the analysis was conducted with the Android Debug Bridge (ADB) and Burpsuite as supporting tools for dynamic analysis. During the static analysis, the Mobile Security Framework (MobSF) and the Bytecode Viewer were used to examine the source code of the three malware samples. Analysis of the flubot virus revealed that it extracts or drops dex files on the victim s device, where the file is the primary malware. The Flubot virus will clone the messaging application or Short Message Service (SMS) on the default device. Additionally, we discovered a form of flubot malware that operates as a Domain Generation Algorithm (DGA) and communicates with its Command and Control (C\&C) server.

The effective security system improvement from malware attacks on the Android operating system should be updated and improved. Effective malware detection increases the level of data security and high protection for the users. Malicious software or malware typically finds a means to circumvent the security procedure, even when the user is unaware whether the application can act as malware. The effectiveness of obfuscated android malware detection is evaluated by collecting static analysis data from a data set. The experiment assesses the risk level of which malware dataset using the hash value of the malware and records malware behavior. A set of hash SHA256 malware samples has been obtained from an internet dataset and will be analyzed using static analysis to record malware behavior and evaluate which risk level of the malware. According to the results, most of the algorithms provide the same total score because of the multiple crime inside the malware application.

Due to the widespread use of the Internet of Things (IoT) in recent years, the need for IoT technologies to handle communications with the rest of the globe has grown dramatically. Wireless sensor networks (WSNs) play a vital role in the operation of the IoT. The creation of Internet of Things operating systems (OS), which can handle the newly constructed IoT hardware, as well as new protocols and procedures for all communication levels, all of which are now in development, will pave the way for the future. When compared to other devices, these gadgets require a comparatively little amount of electricity, memory, and other resources. This has caused the scientific community to become more aware of the relevance of IoT device operating systems as a result of their findings. These devices may be made more versatile and powerful by including an operating system that contains real-time capabilities, kernel, networking, and other features, among other things. IEEE 802.15.4 networks are linked together using IPv6, which has a wide address space and so enables more devices to connect to the internet using the 6LoWPAN protocol. It is necessary to address some privacy and security issues that have arisen as a result of the widespread use of the Internet, notwithstanding the great benefits that have resulted. For the Internet of Things operating systems, this research has provided a network security architecture that ensures secure communication by utilizing the Cooja network simulator in combination with the Contiki operating system and demonstrate and explained how the nodes can protect from the network layer and physical layer attacks. Also, this research has depicted the energy consumption results of each designated node type during the authentication and communication process. Finally, proposed a few further improvements for the architecture which will enhance the network layer protection.

In recent years, the detection of illegal and harmful messages which plays an significant role in Internet service is highly valued by the government and society. Although artificial intelligence technology is increasingly applied to actual operating systems, it is still a big challenge to be applied to systems that require high real-time performance. This paper provides a real-time detection system solution based on artificial intelligence technology. We first introduce the background of real-time detection of illegal and harmful messages. Second, we propose a complete set of intelligent detection system schemes for real-time detection, and conduct technical exploration and innovation in the media classification process including detection model optimization, traffic monitoring and automatic configuration algorithm. Finally, we carry out corresponding performance verification.

Resilience and antifragility under duress present significant challenges for autonomic and self-adaptive systems operating in contested environments. In such settings, the system has to continually plan ahead, accounting for either an adversary or an environment that may negate its actions or degrade its capabilities. This will involve projecting future states, as well as assessing recovery options, counter-measures, and progress towards system goals. For antifragile systems to be effective, we envision three self-* properties to be of key importance: self-exploration, self-learning and self-training. Systems should be able to efficiently self-explore – using adversarial search – the potential impact of the adversary’s attacks and compute the most resilient responses. The exploration can be assisted by prior knowledge of the adversary’s capabilities and attack strategies, which can be self-learned – using opponent modelling – from previous attacks and interactions. The system can self-train – using reinforcement learning – such that it evolves and improves itself as a result of being attacked. This paper discusses those visions and outlines their realisation in AWaRE, a cyber-resilient and self-adaptive multi-agent system.

Security incident handling and response are essen-tial parts of every organization's information and cyber security. Security incident handling consists of several phases, among which digital forensic analysis has an irreplaceable place. Due to particular digital evidence being recorded at a specific time, timelines play an essential role in analyzing this digital evidence. One of the vital tasks of the digital forensic investigator is finding relevant records in this timeline. This operation is performed manually in most cases. This paper focuses on the possibilities of automatically identifying digital evidence pertinent to the case and proposes a model that identifies this digital evidence. For this purpose, we focus on Windows operating system and the NTFS file system and use outlier detection (Local Outlier Factor method). Collected digital evidence is preprocessed, transformed to binary values, and aggregated by file system inodes and names. Subsequently, we identify digital records (file inodes, file names) relevant to the case. This paper analyzes the combinations of attributes, aggregation functions, local outlier factor parameters, and their impact on the resulting selection of relevant file inodes and file names.

The study focused on assessing and testing Windows 10 to identify possible vulnerabilities and their ability to withstand cyber-attacks. CVE data, alongside other vulnerability reports, were instrumental in measuring the operating system's performance. Metasploit and Nmap were essential in penetration and intrusion experiments in a simulated environment. The study applied the following testing procedure: information gathering, scanning and results analysis, vulnerability selection, launch attacks, and gaining access to the operating system. Penetration testing involved eight attacks, two of which were effective against the different Windows 10 versions. Installing the latest version of Windows 10 did not guarantee complete protection against attacks. Further research is essential in assessing the system's vulnerabilities are recommending better solutions.

The era of technology has seen many rising inventions and with that rise, comes the need to secure our systems. In this paper we have discussed how the old generation of people are falling behind at being updated in tandem with technology, and losing track of the knowledge required to process the same. In addition this factor leads to leakage of critical personal information. This paper throws light upon the steps taken in order to exploit the pre-existing operating system, Windows 7, Ultimate, using a ubiquitous framework used by everyone, i.e. Metasploit. It involves installation of a backdoor on the victim machine, from a remote setup, mostly Kali Linux operating machine. This backdoor allows the attackers to create executable files and deploy them in the windows system to gain access on the machine, remotely. After gaining access, manipulation of sensitive data becomes easy. Access to the admin rights of any system is a red alert because it means that some outsider has intense access to personal information of a human being and since data about someone explains a lot of things about them. It basically is exposing and human hate that. It depraves one of their personal identity. Therefore security is not something that should be taken lightly. It is supposed to be dealt with utmost care.

Data leakage by employees is a matter of concern for companies and organizations today. Previous studies have shown that existing Data Leakage Protection (DLP) systems on the market, the more secure they are, the more intrusive and tedious they are to work with. This paper proposes and assesses the implementation of four technologies that enable the development of secure file systems for insider threat-focused, low-intrusive and user-transparent DLP tools. Two of these technologies are configurable features of the Windows operating system (Minifilters and Server Message Block), the other two are virtual file systems (VFS) Dokan and WinFsp, which mirror the real file system (RFS) allowing it to incorporate security techniques. In the assessment of the technologies, it was found that the implementation of VFS was very efficient and simple. WinFsp and Dokan presented a performance of 51% and 20% respectively, with respect to the performance of the operations in the RFS. This result may seem relatively low, but it should be taken into account that the calculation includes read and write encryption and decryption operations as appropriate for each prototype. Server Message Block (SMB) presented a low performance (3%) so it is not considered viable for a solution like this, while Minifilters present the best performance but require high programming knowledge for its evolution. The prototype presented in this paper and its strategy provides an acceptable level of comfort for the user, and a high level of security.

Operating systems have various components that produce artifacts. These artifacts are the outcome of a user’s interaction with an application or program and the operating system’s logging capabilities. Thus, these artifacts have great importance in digital forensics investigations. For example, these artifacts can be utilized in a court of law to prove the existence of compromising computer system behaviors. One such component of the Microsoft Windows operating system is Shellbag, which is an enticing source of digital evidence of high forensics interest. The presence of a Shellbag entry means a specific user has visited a particular folder and done some customizations such as accessing, sorting, resizing the window, etc. In this work, we forensically analyze Shellbag as we talk about its purpose, types, and specificity with the latest version of the Windows 11 operating system and uncover the registry hives that contain Shellbag customization information. We also conduct in-depth forensics examinations on Shellbag entries using three tools of three different types, i.e., open-source, freeware, and proprietary tools. Lastly, we compared the capabilities of tools utilized in Shellbag forensics investigations.

Cyber-attacks against Industrial Control Systems (ICS) can lead to catastrophic events which can be prevented by the use of security measures such as the Intrusion Prevention Systems (IPS). In this work we experimentally demonstrate how to exploit the configuration vulnerabilities of SNORT one of the most adopted IPSs to significantly degrade the effectiveness of the IPS and consequently allowing successful cyber-attacks. We illustrate how to design a batch script able to retrieve and modify the configuration files of SNORT in order to disable its ability to detect and block Denial of Service (DoS) and ARP poisoning-based Man-In-The-Middle (MITM) attacks against a Programmable Logic Controller (PLC) in an ICS network. Experimental tests performed on a water distribution testbed show that, despite the presence of IPS, the DoS and ARP spoofed packets reach the destination causing respectively the disconnection of the PLC from the ICS network and the modification of packets payload.

Consumer IoT devices may suffer malware attacks, and be recruited into botnets or worse. There is evidence that generic advice to device owners to address IoT malware can be successful, but this does not account for emerging forms of persistent IoT malware. Less is known about persistent malware, which resides on persistent storage, requiring targeted manual effort to remove it. This paper presents a field study on the removal of persistent IoT malware by consumers. We partnered with an ISP to contrast remediation times of 760 customers across three malware categories: Windows malware, non-persistent IoT malware, and persistent IoT malware. We also contacted ISP customers identified as having persistent IoT malware on their network-attached storage devices, specifically QSnatch. We found that persistent IoT malware exhibits a mean infection duration many times higher than Windows or Mirai malware; QSnatch has a survival probability of 30% after 180 days, whereby most if not all other observed malware types have been removed. For interviewed device users, QSnatch infections lasted longer, so are apparently more difficult to get rid of, yet participants did not report experiencing difficulty in following notification instructions. We see two factors driving this paradoxical finding: First, most users reported having high technical competency. Also, we found evidence of planning behavior for these tasks and the need for multiple notifications. Our findings demonstrate the critical nature of interventions from outside for persistent malware, since automatic scan of an AV tool or a power cycle, like we are used to for Windows malware and Mirai infections, will not solve persistent IoT malware infections.

Forensic Science comprises a set of technical-scientific knowledge used to solve illicit acts. The increasing use of mobile devices as the main computing platform, in particular smartphones, makes existing information valuable for forensics. However, the blocking mechanisms imposed by the manufacturers and the variety of models and technologies make the task of reconstructing the data for analysis challenging. It is worth mentioning that the conclusion of a case requires more than the simple identification of evidence, as it is extremely important to correlate all the data and sources obtained, to confirm a suspicion or to seek new evidence. This work carries out a systematic review of the literature, identifying the different types of existing image acquisition and the main extraction and encryption methods used in smartphones with the Android operating system.

An approach to substantiating the choice of a discipline for the maintenance of a redundant computer system, with the possible use of node resources saved after failures, is considered. The choice is aimed at improving the reliability and profitability of the system, taking into account the operational costs of restoring nodes. Models of reliability of systems with service disciplines are proposed, providing both the possibility of immediate recovery of nodes after failures, and allowing degradation of the system when using node resources stored after failures in it. The models take into account the conditions of the admissibility or inadmissibility of the loss of information accumulated during the operation of the system. The operating costs are determined, taking into account the costs of restoring nodes for the system maintenance disciplines under consideration

The Time-Triggered Architecture (TTA) presents a blueprint for building safe and real-time constrained distributed systems, based on a set of orthogonal concepts that make extensive use of the availability of a globally consistent notion of time and a priori knowledge of events. Although the TTA tolerates arbitrary failures of any of its nodes by architectural means (active node replication, a membership service, and bus guardians), the design of these means considers only accidental faults. However, distributed safety- and real-time critical systems have been emerging into more open and interconnected systems, operating autonomously for prolonged times and interfacing with other possibly non-real-time systems. Therefore, the existence of vulnerabilities that adversaries may exploit to compromise system safety cannot be ruled out. In this paper, we discuss potential targeted attacks capable of bypassing TTA's fault-tolerance mechanisms and demonstrate how two well-known recovery techniques - proactive and reactive rejuvenation - can be incorporated into TTA to reduce the window of vulnerability for attacks without introducing extensive and costly changes.

Open Source Software plays an important role in many software ecosystems. Whether in operating systems, network stacks, or as low-level system drivers, software we encounter daily is permeated with code contributions from open source projects. Decentralized development and open collaboration in open source projects introduce unique challenges: code submissions from unknown entities, limited personpower for commit or dependency reviews, and bringing new contributors up-to-date in projects’ best practices & processes.In 27 in-depth, semi-structured interviews with owners, maintainers, and contributors from a diverse set of open source projects, we investigate their security and trust practices. For this, we explore projects’ behind-the-scene processes, provided guidance & policies, as well as incident handling & encountered challenges. We find that our participants’ projects are highly diverse both in deployed security measures and trust processes, as well as their underlying motivations. Based on our findings, we discuss implications for the open source software ecosystem and how the research community can better support open source projects in trust and security considerations. Overall, we argue for supporting open source projects in ways that consider their individual strengths and limitations, especially in the case of smaller projects with low contributor numbers and limited access to resources.

The growing maturity of orchestration languages is contributing to the elaboration of cloud composite services, whose resources may be deployed over different distributed infrastructures. These composite services are subject to changes over time, that are typically required to support cloud properties, such as scalability and rapid elasticity. In particular, the migration of their elementary resources may be triggered by performance constraints. However, changes induced by this migration may introduce vulnerabilities that may compromise the resources, or even the whole cloud service. In that context, we propose an automated SMT1-based security framework for supporting the migration of resources in cloud composite services, and preventing the occurrence of new configuration vulnerabilities. We formalize the underlying security automation based on SMT solving, in order to assess the migrated resources and select adequate counter-measures, considering both endogenous and exogenous security mechanisms. We then evaluate its benefits and limits through large series of experiments based on a proof-of-concept prototype implemented over the CVC4 commonly-used open-source solver. These experiments show a minimal overhead with regular operating systems deployed in cloud environments.

Autonomous vehicles (AVs) are capable of making driving decisions autonomously using multiple sensors and a complex autonomous driving (AD) software. However, AVs introduce numerous unique security challenges that have the potential to create safety consequences on the road. Security mechanisms require a benchmark suite and an evaluation framework to generate comparable results. Unfortunately, AVs lack a proper benchmarking framework to evaluate the attack and defense mechanisms and quantify the safety measures. This paper introduces BenchAV – a security benchmark suite and evaluation framework for AVs to address current limitations and pressing challenges of AD security. The benchmark suite contains 12 security and performance metrics, and an evaluation framework that automates the metric collection process using Carla simulator and Robot Operating System (ROS).

The integration of distributed energy resources (DERs) and expansion of complex network in the distribution grid requires an advanced two-level state estimator to monitor the grid health at micro-level. The distribution state estimator will improve the situational awareness and resiliency of distributed power system. This paper implements a synchrophasors-based master state awareness (MSA) estimator to enhance the cybersecurity in distribution grid by providing a real-time estimation of system operating states to control center operators. In this paper, the implemented MSA estimator utilizes only phasor measurements, bus magnitudes and angles, from phasor measurement units (PMUs), deployed in local substations, to estimate the system states and also detects data integrity attacks, such as load tripping attack that disconnects the load. To validate the proof of concept, we implement this methodology in cyber-physical testbed environment at the Idaho National Laboratory (INL) Electric Grid Security Testbed. Further, to address the "valley of death" and support technology commercialization, field demonstration is also performed at the Critical Infrastructure Test Range Complex (CITRC) at the INL. Our experimental results reveal a promising performance in detecting load tripping attack and providing an accurate situational awareness through an alert visualization dashboard in real-time.

Lock design is an important mechanism for scheduling management and security protection in operating systems. However, there is no effective way to identify the differences and connections among lock models, and users need to spend considerable time to understand different lock architectures. In this paper, we propose a classification scheme that abstracts lock design into three types of models: basic spinlock, semaphore amount extension, lock chain structure, and verify the effectiveness of these three types of lock models in the context of current mainstream applications. We also investigate the specific details of applying this classification method, which can be used as a reference for developers to design lock models, thus shorten the software development cycle.