Visible to the public Biblio

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2022-01-10
Jianhua, Xing, Jing, Si, Yongjing, Zhang, Wei, Li, Yuning, Zheng.  2021.  Research on Malware Variant Detection Method Based on Deep Neural Network. 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP). :144–147.
To deal with the increasingly serious threat of industrial information malicious code, the simulations and characteristics of the domestic security and controllable operating system and office software were implemented in the virtual sandbox environment based on virtualization technology in this study. Firstly, the serialization detection scheme based on the convolution neural network algorithm was improved. Then, the API sequence was modeled and analyzed by the improved convolution neural network algorithm to excavate more local related information of variant sequences. Finally the variant detection of malicious code was realized. Results showed that this improved method had higher efficiency and accuracy for a large number of malicious code detection, and could be applied to the malicious code detection in security and controllable operating system.
Moonamaldeniya, Menaka, Priyashantha, V.R.S.C., Gunathilake, M.B.N.B., Ransinghe, Y.M.P.B., Ratnayake, A.L.S.D., Abeygunawardhana, Pradeep K.W..  2021.  Prevent Data Exfiltration on Smart Phones Using Audio Distortion and Machine Learning. 2021 Moratuwa Engineering Research Conference (MERCon). :345–350.
Attacks on mobile devices have gained a significant amount of attention lately. This is because more and more individuals are switching to smartphones from traditional non-smartphones. Therefore, attackers or cybercriminals are now getting on the bandwagon to have an opportunity at obtaining information stored on smartphones. In this paper, we present an Android mobile application that will aid to minimize data exfiltration from attacks, such as, Acoustic Side-Channel Attack, Clipboard Jacking, Permission Misuse and Malicious Apps. This paper will commence its inception with an introduction explaining the current issues in general and how attacks such as side-channel attacks and clipboard jacking paved the way for data exfiltration. We will also discuss a few already existing solutions that try to mitigate these problems. Moving on to the methodology we will emphasize how we came about the solution and what methods we followed to achieve the end goal of securing the smartphone. In the final section, we will discuss the outcomes of the project and conclude what needs to be done in the future to enhance this project so that this mobile application will continue to keep the user's data safe from the criminals' grasps.
Govender, Castello, van Niekerk, Brett.  2021.  Secure Key Exchange by NFC for Instant Messaging. 2021 Conference on Information Communications Technology and Society (ICTAS). :27–33.
This study offers an alternative to current implementations of key exchange by utilizing NFC technologies within android mobile devices. Supporting key exchange protocols along with cryptographic algorithms are offered, which meet current security standards whilst maintaining a short key length for optimal transfer between devices. Peer-to-peer and Host Card Emulation operational modes are observed to determine the best suited approach for key exchange. The proposed model offers end to end encryption between Client-Client as opposed to the usual Client-Server encryption offered by most Instant Messaging applications.
2021-11-29
Andarzian, Seyed Behnam, Ladani, Behrouz Tork.  2020.  Compositional Taint Analysis of Native Codes for Security Vetting of Android Applications. 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE). :567–572.
Security vetting of Android applications is one of the crucial aspects of the Android ecosystem. Regarding the state of the art tools for this goal, most of them doesn't consider analyzing native codes and only analyze the Java code. However, Android concedes its developers to implement a part or all of their applications using C or C++ code. Thus, applying conservative manners for analyzing Android applications while ignoring native codes would lead to less precision in results. Few works have tried to analyze Android native codes, but only JN-SAF has applied taint analysis using static techniques such as symbolic execution. However, symbolic execution has some problems when is used in large programs. One of these problems is the exponential growth of program paths that would raise the path explosion issue. In this work, we have tried to alleviate this issue by introducing our new tool named CTAN. CTAN applies new symbolic execution methods to angr in a particular way that it can make JN-SAF more efficient and faster. We have introduced compositional taint analysis in CTAN by combining satisfiability modulo theories with symbolic execution. Our experiments show that CTAN is 26 percent faster than its previous work JN-SAF and it also leads to more precision by detecting more data-leakage in large Android native codes.
Taghanaki, Saeid Rafiei, Arzandeh, Shohreh Behnam, Bohlooli, Ali.  2021.  A Decentralized Method for Detecting Clone ID Attacks on the Internet of Things. 2021 5th International Conference on Internet of Things and Applications (IoT). :1–6.
One of the attacks in the RPL protocol is the Clone ID attack, that the attacker clones the node's ID in the network. In this research, a Clone ID detection system is designed for the Internet of Things (IoT), implemented in Contiki operating system, and evaluated using the Cooja emulator. Our evaluation shows that the proposed method has desirable performance in terms of energy consumption overhead, true positive rate, and detection speed. The overhead cost of the proposed method is low enough that it can be deployed in limited-resource nodes. The proposed method in each node has two phases, which are the steps of gathering information and attack detection. In the proposed scheme, each node detects this type of attack using control packets received from its neighbors and their information such as IP, rank, Path ETX, and RSSI, as well as the use of a routing table. The design of this system will contribute to the security of the IoT network.
2021-11-08
Qian, Dazan, Guo, Songhui, Sun, Lei, Liu, Haidong, Hao, Qianfang, Zhang, Jing.  2020.  Trusted Virtual Network Function Based on vTPM. 2020 7th International Conference on Information Science and Control Engineering (ICISCE). :1484–1488.
Mobile communication technology is developing rapidly, and this is integrated with technologies such as Software Defined Network (SDN), cloud computing, and Network Function Virtualization (NFV). Network Functions (NFs) are no longer deployed on dedicated hardware devices, while deployed in Virtual Machines (VMs) or containers as Virtual Network Functions (VNFs). If VNFs are tampered with or replaced, the communication system will not function properly. Our research is to enhance the security of VNFs using trusted computing technology. By adding Virtual Trusted Platform Module (vTPM) to the virtualization platform, the chain of trust extends from the VM operating system to VNFs within the VM. Experimental results prove that the solution can effectively protect the integrity of VNFs from being attacked.
2021-10-04
Moustafa, Nour, Keshky, Marwa, Debiez, Essam, Janicke, Helge.  2020.  Federated TONİoT Windows Datasets for Evaluating AI-Based Security Applications. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :848–855.
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].
2021-09-30
Serino, Anthony, Cheng, Liang.  2020.  Real-Time Operating Systems for Cyber-Physical Systems: Current Status and Future Research. 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). :419–425.
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.
2021-09-21
Ramadhan, Beno, Purwanto, Yudha, Ruriawan, Muhammad Faris.  2020.  Forensic Malware Identification Using Naive Bayes Method. 2020 International Conference on Information Technology Systems and Innovation (ICITSI). :1–7.
Malware is a kind of software that, if installed on a malware victim's device, might carry malicious actions. The malicious actions might be data theft, system failure, or denial of service. Malware analysis is a process to identify whether a piece of software is a malware or not. However, with the advancement of malware technologies, there are several evasion techniques that could be implemented by malware developers to prevent analysis, such as polymorphic and oligomorphic. Therefore, this research proposes an automatic malware detection system. In the system, the malware characteristics data were obtained through both static and dynamic analysis processes. Data from the analysis process were classified using Naive Bayes algorithm to identify whether the software is a malware or not. The process of identifying malware and benign files using the Naive Bayes machine learning method has an accuracy value of 93 percent for the detection process using static characteristics and 85 percent for detection through dynamic characteristics.
2021-09-16
Li, Minglei, Lu, Yuliang, Huang, Hui, Zhao, Jun, Lu, CanJu.  2020.  A Method of ROP Decentralized Layout. 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC). :369–372.
Return-oriented programming (ROP)is a technique used to break data execution protection(DEP). Existing ROP chain automatic construction technology cannot effectively use program controllable memory area. In order to improve the utilization of memory space, this paper proposes a method of ROP chain fragmentation layout. By searching the controllable memory area of the program, a set of layoutable space is formed, and the overall ROP chain is segmented to add jump instructions at the end of each segment, thereby achieving a fragmented layout of the ROP chain. The prototype system ROP-chip based on S2E proved the effectiveness of the fragmented layout of the ROP chain.
2021-08-31
KARA, Ilker, AYDOS, Murat.  2020.  Cyber Fraud: Detection and Analysis of the Crypto-Ransomware. 2020 11th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :0764–0769.
Currently as the widespread use of virtual monetary units (like Bitcoin, Ethereum, Ripple, Litecoin) has begun, people with bad intentions have been attracted to this area and have produced and marketed ransomware in order to obtain virtual currency easily. This ransomware infiltrates the victim's system with smartly-designed methods and encrypts the files found in the system. After the encryption process, the attacker leaves a message demanding a ransom in virtual currency to open access to the encrypted files and warns that otherwise the files will not be accessible. This type of ransomware is becoming more popular over time, so currently it is the largest information technology security threat. In the literature, there are many studies about detection and analysis of this cyber-bullying. In this study, we focused on crypto-ransomware and investigated a forensic analysis of a current attack example in detail. In this example, the attack method and behavior of the crypto-ransomware were analyzed and it was identified that information belonging to the attacker was accessible. With this dimension, we think our study will significantly contribute to the struggle against this threat.
Manavi, Farnoush, Hamzeh, Ali.  2020.  A New Method for Ransomware Detection Based on PE Header Using Convolutional Neural Networks. 2020 17th International ISC Conference on Information Security and Cryptology (ISCISC). :82–87.
With the spread of information technology in human life, data protection is a critical task. On the other hand, malicious programs are developed, which can manipulate sensitive and critical data and restrict access to this data. Ransomware is an example of such a malicious program that encrypts data, restricts users' access to the system or their data, and then request a ransom payment. Many types of research have been proposed for ransomware detection. Most of these methods attempt to identify ransomware by relying on program behavior during execution. The main weakness of these methods is that it is not clear how long the program should be monitored to show its real behavior. Therefore, sometimes, these researches cannot early detect ransomware. In this paper, a new method for ransomware detection is proposed that does not require running the program and uses the PE header of the executable files. To extract effective features from the PE header files, an image based on PE header is constructed. Then, according to the advantages of Convolutional Neural Networks in extracting features from images and classifying them, CNN is used. The proposed method achieves 93.33% accuracy. Our results indicate the usefulness and practicality method for ransomware detection.
2021-08-11
MILLAR, KYLE, CHENG, ADRIEL, CHEW, HONG GUNN, LIM, CHENG-CHEW.  2020.  Operating System Classification: A Minimalist Approach. 2020 International Conference on Machine Learning and Cybernetics (ICMLC). :143—150.
Operating system (OS) classification is of growing importance to network administrators and cybersecurity analysts alike. The composition of OSs on a network allows for a better quality of device management to be achieved. Additionally, it can be used to identify devices that pose a security risk to the network. However, the sheer number and diversity of OSs that comprise modern networks have vastly increased this management complexity. We leverage insights from social networking theory to provide an encryption-invariant OS classification technique that is quick to train and widely deployable on various network configurations. In particular, we show how an affiliation graph can be used as an input to a machine learning classifier to predict the OS of a device using only the IP addresses for which the device communicates with.We examine the effectiveness of our approach through an empirical analysis of 498 devices on a university campus’ wireless network. In particular, we show our methodology can classify different OS families (i.e., Apple, Windows, and Android OSs) with an accuracy of 99.3%. Furthermore, we extend this study by: 1) examining distinct OSs (e.g., iOS, OS X, and Windows 10); 2) investigating the interval of time required to make an accurate prediction; and, 3) determining the effectiveness of our approach after six months.
Nazarenko, Maxim A..  2020.  What is Mobile Operation System Quality? 2020 International Conference Quality Management, Transport and Information Security, Information Technologies (IT QM IS). :145—147.
There are some modern mobile operation systems. The main two of them are iOS and Android. However, in the past, there were two more commonly used ones: Windows Mobile and Symbian. Each of these systems has its own pros and cons, whereas none of them is the best or the worst one in different criterions. In this paper the main criterions of operation system quality are discussed. The paper defines what the mobile operating system quality is.
Alshaikh, Mansour, Zohdy, Mohamed.  2020.  Sentiment Analysis for Smartphone Operating System: Privacy and Security on Twitter Data. 2020 IEEE International Conference on Electro Information Technology (EIT). :366—369.
The aim of the study was to investigate the privacy and security of the user data on Twitter. For gathering the essential information, more than two million relevant tweets through the span of two years were used to conduct the study. In addition, we are classifying sentiment of Twitter data by exhibiting results of a machine learning by using the Naive Bayes algorithm. Although this algorithm is time consuming compared to the listing method yet can lead to effective estimation relatively. The tweets are extracted and pre-processed and then categorized them in neutral, negative and positive sentiments. By applying the chosen methodology, the study would end up in identifying the most effective mobile operating systems according to the sentiments of social media users. Additionally, the application of the algorithm needs to meet the privacy and security needs of Twitter users in order to optimize the use of social media intelligence. The approach will help in assessing the competitive intelligence of the Twitter data and the challenges in the form of privacy and- security of the user content and their contextual information simultaneously. The findings of the empirical research show that users are more concerned about the privacy and security of iOS compared to Android and Windows phone.
2021-07-08
Sato, Masaya, Taniguchi, Hideo, Nakamura, Ryosuke.  2020.  Virtual Machine Monitor-based Hiding Method for Access to Debug Registers. 2020 Eighth International Symposium on Computing and Networking (CANDAR). :209—214.
To secure a guest operating system running on a virtual machine (VM), a monitoring method using hardware breakpoints by a virtual machine monitor is required. However, debug registers are visible to guest operating systems; thus, malicious programs on a guest operating system can detect or disable the monitoring method. This paper presents a method to hide access to debug registers from programs running on a VM. Our proposed method detects programs' access to debug registers and disguises the access as having succeeded. The register's actual value is not visible or modifiable to programs, so the monitoring method is hidden. This paper presents the basic design and evaluation results of our method.
2021-07-07
Moustafa, Nour, Ahmed, Mohiuddin, Ahmed, Sherif.  2020.  Data Analytics-Enabled Intrusion Detection: Evaluations of ToNİoT Linux Datasets. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :727–735.
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].
2021-06-24
Stöckle, Patrick, Grobauer, Bernd, Pretschner, Alexander.  2020.  Automated Implementation of Windows-related Security-Configuration Guides. 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE). :598—610.
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.
2021-05-05
Đuranec, A., Gruičić, S., Žagar, M..  2020.  Forensic analysis of Windows 10 Sandbox. 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). :1224—1229.

With each Windows operating system Microsoft introduces new features to its users. Newly added features present a challenge to digital forensics examiners as they are not analyzed or tested enough. One of the latest features, introduced in Windows 10 version 1909 is Windows Sandbox; a lightweight, temporary, environment for running untrusted applications. Because of the temporary nature of the Sandbox and insufficient documentation, digital forensic examiners are facing new challenges when examining this newly added feature which can be used to hide different illegal activities. Throughout this paper, the focus will be on analyzing different Windows artifacts and event logs, with various tools, left behind as a result of the user interaction with the Sandbox feature on a clear virtual environment. Additionally, the setup of testing environment will be explained, the results of testing and interpretation of the findings will be presented, as well as open-source tools used for the analysis.

2021-04-29
Belim, S. V., Belim, S. Y..  2020.  The Security Policies Optimization Problem for Composite Information Systems. 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). :1—4.

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‼!

2021-03-15
Piessens, F..  2020.  Security across abstraction layers: old and new examples. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :271–279.
A common technique for building ICT systems is to build them as successive layers of bstraction: for instance, the Instruction Set Architecture (ISA) is an abstraction of the hardware, and compilers or interpreters build higher level abstractions on top of the ISA.The functionality of an ICT application can often be understood by considering only a single level of abstraction. For instance the source code of the application defines the functionality using the level of abstraction of the source programming language. Functionality can be well understood by just studying this source code.Many important security issues in ICT system however are cross-layer issues: they can not be understood by considering the system at a single level of abstraction, but they require understanding how multiple levels of abstraction are implemented. Attacks may rely on, or exploit, implementation details of one or more layers below the source code level of abstraction.The purpose of this paper is to illustrate this cross-layer nature of security by discussing old and new examples of cross-layer security issues, and by providing a classification of these issues.
2021-03-09
Muslim, A. A., Budiono, A., Almaarif, A..  2020.  Implementation and Analysis of USB based Password Stealer using PowerShell in Google Chrome and Mozilla Firefox. 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE). :421—426.

Along with the development of the Windows operating system, browser applications to surf the internet are also growing rapidly. The most widely used browsers today are Google Chrome and Mozilla Firefox. Both browsers have a username and password management feature that makes users login to a website easily, but saving usernames and passwords in the browser is quite dangerous because the stored data can be hacked using brute force attacks or read through a program. One way to get a username and password in the browser is to use a program that can read Google Chrome and Mozilla Firefox login data from the computer's internal storage and then show those data. In this study, an attack will be carried out by implementing Rubber Ducky using BadUSB to run the ChromePass and PasswordFox program and the PowerShell script using the Arduino Pro Micro Leonardo device as a USB Password Stealer. The results obtained from this study are the username and password on Google Chrome and Mozilla Firefox successfully obtained when the USB is connected to the target device, the average time of the attack is 14 seconds then sending it to the author's email.

2021-03-04
Afreen, A., Aslam, M., Ahmed, S..  2020.  Analysis of Fileless Malware and its Evasive Behavior. 2020 International Conference on Cyber Warfare and Security (ICCWS). :1—8.

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.

Matin, I. Muhamad Malik, Rahardjo, B..  2020.  A Framework for Collecting and Analysis PE Malware Using Modern Honey Network (MHN). 2020 8th International Conference on Cyber and IT Service Management (CITSM). :1—5.

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.

Ferryansa, Budiono, A., Almaarif, A..  2020.  Analysis of USB Based Spying Method Using Arduino and Metasploit Framework in Windows Operating System. 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE). :437—442.

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.