Biblio

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.

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.

Malware detection and analysis can be a burdensome task for incident responders. As such, research has turned to machine learning to automate malware detection and malware family classification. Existing work extracts and engineers static and dynamic features from the malware sample to train classifiers. Despite promising results, such techniques assume that the analyst has access to the malware executable file. Self-deleting malware invalidates this assumption and requires analysts to find forensic evidence of malware execution for further analysis. In this paper, we present and evaluate an approach to detecting malware that executed on a Windows target and further classify the malware into its associated family to provide semantic insight. Specifically, we engineer features from the Windows prefetch file, a file system forensic artifact that archives process information. Results show that it is possible to detect the malicious artifact with 99% accuracy; furthermore, classifying the malware into a fine-grained family has comparable performance to techniques that require access to the original executable. We also provide a thorough security discussion of the proposed approach against adversarial diversity.

In the computer field, cybersecurity has always been the focus of attention. How to detect malware is one of the focuses and difficulties in network security research effectively. Traditional existing malware detection schemes can be mainly divided into two methods categories: database matching and the machine learning method. With the rise of deep learning, more and more deep learning methods are applied in the field of malware detection. Deeper semantic features can be extracted via deep neural network. The main tasks of this paper are as follows: (1) Using machine learning methods and one-dimensional convolutional neural networks to detect malware (2) Propose a machine The method of combining learning and deep learning is used for detection. Machine learning uses LGBM to obtain an accuracy rate of 67.16%, and one-dimensional CNN obtains an accuracy rate of 72.47%. In (2), LGBM is used to screen the importance of features and then use a one-dimensional convolutional neural network, which helps to further improve the detection result has an accuracy rate of 78.64%.

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.

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.

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.

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.

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.

Network Address Translation (NAT) is an address remapping technique placed at the borders of stub domains. It is present in almost all routers and CPEs. Most NAT devices implement Port Address Translation (PAT), which allows the mapping of multiple private IP addresses to one public IP address. Based on port number information, PAT matches the incoming traffic to the corresponding "hidden" client. In an enterprise context, and with the proliferation of unauthorized wired and wireless NAT routers, NAT can be used for re-distributing an Intranet or Internet connection or for deploying hidden devices that are not visible to the enterprise IT or under its oversight, thus causing a problem known as shadow IT. Thus, it is important to detect NAT devices in an intranet to prevent this particular problem. Previous methods in identifying NAT behavior were based on features extracted from traffic traces per flow. In this paper, we propose a method to identify NAT devices using a machine learning approach from aggregated flow features. The approach uses multiple statistical features in addition to source and destination IPs and port numbers, extracted from passively collected traffic data. We also use aggregated features extracted within multiple window sizes and feed them to a machine learning classifier to study the effect of timing on NAT detection. Our approach works completely passively and achieves an accuracy of 96.9% when all features are utilized.

Intrusion detection is a traditional practice of security experts, however, there are several issues which still need to be tackled. Therefore, in this paper, after highlighting these issues, we present an architecture for a hybrid Intrusion Detection System (IDS) for an adaptive and incremental detection of both known and unknown attacks. The IDS is composed of supervised and unsupervised modules, namely, a Deep Neural Network (DNN) and the K-Nearest Neighbors (KNN) algorithm, respectively. The proposed system is near-autonomous since the intervention of the expert is minimized through the active learning (AL) approach. A query strategy for the labeling process is presented, it aims at teaching the supervised module to detect unknown attacks and improve the detection of the already-known attacks. This teaching is achieved through sliding windows (SW) in an incremental fashion where the DNN is retrained when the data is available over time, thus rendering the IDS adaptive to cope with the evolutionary aspect of the network traffic. A set of experiments was conducted on the CICIDS2017 dataset in order to evaluate the performance of the IDS, promising results were obtained.

The growth in the use of virtualization in the last ten years has contributed to the improvement of this technology. The practice of implementing and managing this type of isolated environment raises doubts about the security of such systems. Considering the host's proximity to a container, approaches that use anomaly detection systems attempt to monitor and detect unexpected behavior. Our work aims to use system calls to identify threats within a container environment, using machine learning based strategies to distinguish between expected and unexpected behaviors (possible threats).

As a new service-oriented computing paradigm, cloud computing facilitates users to share and use resources. However, due to the dynamic and openness of its operating environment, only relying on traditional identity authentication technology can no longer fully meet the security requirements of cloud computing. The trust evaluation of user behavior has become the key to improve the security of cloud computing. Therefore, in view of some problems existing in our current research on user behavior trust, this paper optimizes and improves the construction of the evaluation index system and the calculation of trust value, and proposes a cloud end-user behavior trust evaluation model based on sliding window. Finally, the model is proved to be scientific and effective by simulation experiments, which has certain significance for the security protection of cloud resources.

The paper considers the issue of increasing the level of trust to the user of the information system by applying profiling actions. The authors have developed the model of user behavior, which allows to identify the user by his actions in the operating system. The model uses a user's characteristic metric instead of binary identification. The user's characteristic demonstrates the degree to which the current actions of the user corresponding to the user's behavior model. To calculate the user's characteristic, several formulas have been proposed. The authors propose to implement the developed behavior model into the access control model. For this purpose, the authors create the prototype of the user action profiling system for Windows family operating systems. This system should control access to protected resources by analyzing user behavior. The authors performed a series of tests with this system. This allowed to evaluate the accuracy of the system based on the proposed behavior model. Test results showed the type I errors. Therefore, the authors invented and described a polymodel approach to profiling actions. Potentially, the polymodel approach should solve the problem of the accuracy of the user action profiling system.

An Intrusion Detection System (IDS) is a core element for securing critical systems. An IDS can use signatures of known attacks, or an anomaly detection model for detecting unknown attacks. Attacking an IDS is often the entry point of an attack against a critical system. Consequently, the security of IDSs themselves is imperative. To secure model-based IDSs, we propose a method to authenticate the anomaly detection model. The anomaly detection model is an autoencoder for which we only have access to input-output pairs. Inputs consist of time windows of values from sensors and actuators of an Industrial Control System. Our method is based on a multipath Neural Network (NN) classifier, a newly proposed deep learning technique. The idea is to characterize errors of an IDS's autoencoder by using a multipath NN's confidence measure \$c\$. We use the Wilcoxon-Mann-Whitney (WMW) test to detect a change in the distribution of the summary variable \$c\$, indicating that the autoencoder is not working properly. We compare our method to two baselines. They consist in using other summary variables for the WMW test. We assess the performance of these three methods using simulated data. Among others, our analysis shows that: 1) both baselines are oblivious to some autoencoder spoofing attacks while 2) the WMW test on a multipath NN's confidence measure enables detecting eventually any autoencoder spoofing attack.

As malware detection algorithms and methods become more sophisticated, malware authors adopt equally sophisticated evasion mechanisms to defeat them. Anecdotal evidence claims Living-Off-The-Land (LotL) techniques are one of the major evasion techniques used in many malware attacks. These techniques leverage binaries already present in the system to conduct malicious actions. We present the first large-scale systematic investigation of the use of these techniques by malware on Windows systems.In this paper, we analyse how common the use of these native system binaries is across several malware datasets, containing a total of 31,805,549 samples. We identify an average 9.41% prevalence. Our results show that the use of LotL techniques is prolific, particularly in Advanced Persistent Threat (APT) malware samples where the prevalence is 26.26%, over twice that of commodity malware.To illustrate the evasive potential of LotL techniques, we test the usage of LotL techniques against several fully patched Windows systems in a local sandboxed environment and show that there is a generalised detection gap in 10 of the most popular anti-virus products.

Windows Communication Foundation (WCF) is a communication framework for building connected, service-oriented applications, initially released by Microsoft as part of.NET Framework, but now open source. The WCF message-based communication is a very popular solution used for sending asynchronous messages from one service endpoint to another. Because WCF provides many functionalities it has a large-consuming development model and often the security measures implemented in applications are not proper. In this study we propose a methodology for offensive security analysis of an WCF endpoint or service, from red team perspective. A step by step approach, empirical information, and detailed analysis report of WCF vulnerabilities are presented. We conclude by proposing recommendations for mitigating attacks and securing endpoints.

Process injection techniques on Windows appli-cations are considered a serious threat to software security specialists. The attackers use these techniques to exploit the targeted program or process and take advantage of it by injecting a malicious process within the address space of the hosted process. Such attacks could be carried out using the so-called reverse engineering realm” the code caves”. For that reason, detecting these code caves in a particular application/program is deemed crucial to prevent the adversary from exploiting the programs through them. Code caves are simply a sequence of null bytes inside the executable program. They form due to the unuse of uninitialized variables. This paper presents a tool that can detect code caves in Windows programs by disassembling the program and looking for the code caves inside it; additionally, the tool will also eliminate those code caves without affecting the program’s functionality. The tool has proven reliable and accurate when tested on various types of programs under the Windows operating system.

Malware is any software that causes harm to the user information, computer systems or network. Modern computing and internet systems are facing increase in malware threats from the internet. It is observed that different malware follows the same patterns in their structure with minimal alterations. The type of threats has evolved, from file-based malware to fileless malware, such kind of threats are also known as Advance Volatile Threat (AVT). Fileless malware is complex and evasive, exploiting pre-installed trusted programs to infiltrate information with its malicious intent. Fileless malware is designed to run in system memory with a very small footprint, leaving no artifacts on physical hard drives. Traditional antivirus signatures and heuristic analysis are unable to detect this kind of malware due to its sophisticated and evasive nature. This paper provides information relating to detection, mitigation and analysis for such kind of threat.

Computer virus detection technology is an important basic security technology in the information age. The current detection technology has a high success rate for the detection of known viruses and known virus infection technologies, but the development of detection technology often lags behind the development of computer virus infection technology. Under Windows system, there are many kinds of file viruses, which change rapidly, and pose a continuous security threat to users. The research of new file virus infection technology can provide help for the development of virus detection technology. In this paper, a new virus infection technology based on dynamic binary analysis is proposed to execute file virus infection. Using the new virus infection technology, the infected executable file can be detected in the experimental environment. At the same time, this paper discusses the detection method of new virus infection technology. We hope to provide help for the development of virus detection technology from the perspective of virus design.

The relevance of data protection is related to the intensive informatization of various aspects of society and the need to prevent unauthorized access to them. World spending on ensuring information security (IS) for the current state: expenses in the field of IS today amount to \$81.7 billion. Expenditure forecast by 2020: about \$105 billion [1]. Information protection of military facilities is the most critical in the public sector, in the non-state - financial organizations is one of the leaders in spending on information protection. An example of the importance of IS research is the Trojan encoder WannaCry, which infected hundreds of thousands of computers around the world, attacks are recorded in more than 116 countries. The attack of the encoder of WannaCry (Wana Decryptor) happens through a vulnerability in service Server Message Block (protocol of network access to file systems) of Windows OS. Then, a rootkit (a set of malware) was installed on the infected system, using which the attackers launched an encryption program. Then each vulnerable computer could become infected with another infected device within one local network. Due to these attacks, about \$70,000 was lost (according to data from 18.05.2017) [2]. It is assumed in the presented work, that the software level of information protection is fundamentally insufficient to ensure the stable functioning of critical objects. This is due to the possible hardware implementation of undocumented instructions, discussed later. The complexity of computing systems and the degree of integration of their components are constantly growing. Therefore, monitoring the operation of the computer hardware is necessary to achieve the maximum degree of protection, in particular, data processing methods.

Nowadays, Windows is an operating system that is very popular among people, especially users who have limited knowledge of computers. But unconsciously, the security threat to the windows operating system is very high. Security threats can be in the form of illegal exploitation of the system. The most common attack is using malware. To determine the characteristics of malware using dynamic analysis techniques and static analysis is very dependent on the availability of malware samples. Honeypot is the most effective malware collection technique. But honeypot cannot determine the type of file format contained in malware. File format information is needed for the purpose of handling malware analysis that is focused on windows-based malware. For this reason, we propose a framework that can collect malware information as well as identify malware PE file type formats. In this study, we collected malware samples using a modern honey network. Next, we performed a feature extraction to determine the PE file format. Then, we classify types of malware using VirusTotal scanning. As the results of this study, we managed to get 1.222 malware samples. Out of 1.222 malware samples, we successfully extracted 945 PE malware. This study can help researchers in other research fields, such as machine learning and deep learning, for malware detection.

File timestamps do not receive much attention from information security specialists and computer forensic scientists. It is believed that timestamps are extremely easy to fake, and the system time of a computer can be changed. However, operating system for synchronizing processes and working with file objects needs accurate time readings. The authors estimate that several million timestamps can be stored on the logical partition of a hard disk with the NTFS. The MFT stores four timestamps for each file object in \$STANDARDİNFORMATION and \$FILE\_NAME attributes. Furthermore, each directory in the İNDEX\_ROOT or İNDEX\_ALLOCATION attributes contains four more timestamps for each file within it. File timestamps are set and changed as a result of file operations. At the same time, some file operations differently affect changes in timestamps. This article presents the results of the tool-based observation over the creation and update of timestamps in the MFT resulting from the basic file operations. Analysis of the results is of interest with regard to computer forensic science.

The use of a very wide windows operating system is undeniably also followed by increasing attacks on the operating system. Universal Serial Bus (USB) is one of the mechanisms used by many people with plug and play functionality that is very easy to use, making data transfers fast and easy compared to other hardware. Some research shows that the Windows operating system has weaknesses so that it is often exploited by using various attacks and malware. There are various methods used to exploit the Windows operating system, one of them by using a USB device. By using a USB device, a criminal can plant a backdoor reverse shell to exploit the victim's computer just by connecting the USB device to the victim's computer without being noticed. This research was conducted by planting a reverse shell backdoor through a USB device to exploit the victim's device, especially the webcam and microphone device on the target computer. From 35 experiments that have been carried out, it was found that 83% of spying attacks using USB devices on the Windows operating system were successfully carried out.

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].