Biblio

In recent years we can observe that digital forensics is being applied to a variety of domains as nearly any data can become valuable forensic evidence. The sheer scope of web-based investigations provides a vast amount of information. Due to a rapid increase in the number of cybercrimes the importance of application-specific forensics is greater than ever. Criminals use the application not only to communicate but also to facilitate crimes. It came to our attention that the gaming chat application Discord is one of them. Discord allows its users to send text messages as well as exchange image, video, and audio files. While Discord's community is not as large as that of the most popular messaging apps the stable growth of its userbase and recent incidents indicate that it is used by criminals. This paper presents our research into the digital forensic analysis of Discord client-side artefacts and presents experimental development of a tool for extraction, analysis, and presentation of the data from Discord application. The work then proposes a solution in form of a tool, `DiscFor', that can retrieve information from the application's local files and cache storage.

As the web keeps increasing in size, the number of vulnerable and poorly-managed websites increases commensurately. Attackers rely on armies of malicious bots to discover these vulnerable websites, compromising their servers, and exfiltrating sensitive user data. It is, therefore, crucial for the security of the web to understand the population and behavior of malicious bots.In this paper, we report on the design, implementation, and results of Aristaeus, a system for deploying large numbers of "honeysites", i.e., websites that exist for the sole purpose of attracting and recording bot traffic. Through a seven-month-long experiment with 100 dedicated honeysites, Aristaeus recorded 26.4 million requests sent by more than 287K unique IP addresses, with 76,396 of them belonging to clearly malicious bots. By analyzing the type of requests and payloads that these bots send, we discover that the average honeysite received more than 37K requests each month, with more than 50% of these requests attempting to brute-force credentials, fingerprint the deployed web applications, and exploit large numbers of different vulnerabilities. By comparing the declared identity of these bots with their TLS handshakes and HTTP headers, we uncover that more than 86.2% of bots are claiming to be Mozilla Firefox and Google Chrome, yet are built on simple HTTP libraries and command-line tools.

Internet security is constantly at risk as a result of the fast developing and highly sophisticated exploitation methods. These attacks use numerous media to take advantage of the most vulnerable of Internet users. Phishing, spam calling, unsecure content and other means of intrusion threaten Internet users every day. In order to maintain the security and privacy of sensitive user data, the user must pay for services that include the storage and generation of secure passwords, monitoring internet traffic to discourage navigation to malicious websites, among other services. Some people do not have the money to purchase privacy protection services and others find convoluted euphemisms baked into privacy policies quite confusing. In response to this problem, we developed an Internet security software package, Secure Suite, which we provide as open source and hence free of charge. Users can easily deploy and manage Secure Suite. It is composed of a password manager, a malicious URL detection service, dubbed MalURLNet, a URL extender, data visualization tools, a browser extension to interact with the web app, and utility tools to maintain data integrity. MalURLNet is one of the main components of Secure Suite. It utilizes deep learning and other open-source software to mitigate security threats by identifying malicious URLs. We exhaustively tested our proposed MalURLNet service. Our studies show that MalURLNet outperforms four other well-known URL classifiers in terms of accuracy, loss, precision, recall, and F1-Score.

A good website quality is needed to meet user satisfaction. The value of the benefits of the web will be felt by many users if the web has very good quality. The ease of accessing the website is a reflection of the good quality of the website. The positive image of the web owner can be seen from the quality of the website. When doing research on the website of the Ministry of Agriculture, Directorate General of Food Crops, the researcher found several pages that did not meet the website category which were said to be of good quality. Based on these findings, the authors are interested in analyzing user satisfaction with the website to measure the quality of the website of the Ministry of Agriculture, Directorate General of Food Crops using the PIECES method (Performance, Information, Economy, Control/Security, Efficiency, Service). The results of the study indicate that the level of user satisfaction with the website has been indicated as SATISFIED on each indicator, however, in measuring the quality of the website using YSlow (the GTMetrix tools, Pingdom Website Speed Tools), and (Web of Trust) WOT found many deficiencies such as loading the website takes a long time, there are some pages that cannot be found (page not found) and so on. Therefore, the authors provide several recommendations for better website development.

Software Defined Networking (SDN) is an innovative technology, which can be applied in a plethora of applications and areas. Recently, SDN has been identified as one of the most promising solutions for industrial applications as well. The key features of SDN include the decoupling of the control plane from the data plane and the programmability of the network through application development. Researchers are looking at these features in order to enhance the Quality of Service (QoS) provisioning of modern network applications. To this end, the following work presents the development of an SDN application, capable of mitigating attacks and maximizing the network’s QoS, by implementing mixed integer linear programming but also using genetic algorithms. Furthermore, a low-cost, physical SDN testbed was developed in order to evaluate the aforementioned application in a more realistic environment other than only using simulation tools.

Bluetooth Low Energy mesh networks are emerging as new standard of short burst communications. While security of the messages is guaranteed thought standard encryption techniques, little has been done in terms of actively protecting the overall network in case of attacks aiming to undermine its integrity. Although many network analysis and risk mitigation techniques are currently available, they require considerable amounts of data coming from both legitimate and attack scenarios to sufficiently discriminate among them, which often turns into the requirement of a complete description of the traffic flowing through the network. Furthermore, there are no publicly available datasets to this extent for BLE mesh networks, due most to the novelty of the standard and to the absence of specific implementation tools. To create a reliable mechanism of network analysis suited for BLE in this paper we propose a machine learning Intrusion Detection System (IDS) based on pattern classification and recognition of the most classical denial of service attacks affecting this kind of networks, working on a single internal node, thus requiring a small amount of information to operate. Moreover, in order to overcome the gap created by the absence of data, we present our data collection system based on ESP32 that allowed the collection of the packets from the Network and the Model layers of the BLE Mesh stack, together with a set of experiments conducted to get the necessary data to train the IDS. In the last part, we describe some preliminary results obtained by the experimental setups, focusing on its strengths, as well as on the aspects where further analysis is required, hence proposing some improvements of the classification model as future work. Index Terms-Bluetooth, BLE Mesh, Intrusion Detection System, IoT, network security.

Threat Intelligence has been a key part of the success of Intrusion Detection, with several trusted sources leading to wide adoption and greater understanding of new and trending threats to computer networks. Identifying potential threats and live attacks on networks is only half the battle, knowing how to correctly respond to these threats and attacks requires in-depth and domain specific knowledge, which may be unique to subject experts and software vendors. Network Incident Responders and Intrusion Response Systems can benefit from a similar approach to Threat Intel, with a focus on potential Response actions. A qualitative comparison of current Threat Intel Sources and prominent Intrusion Response Systems is carried out to aid in the identification of key requirements to be met to enable the adoption of Response Intel. Building on these requirements, a template for Response Intel is proposed which incorporates standardised models developed by MITRE. Similarly, to facilitate the automated use of Response Intel, a structure for automated Response Actions is proposed.

Containers technology radically changed the ways for packaging applications and deploying them as services in cloud environments. According to the recent report on security predictions of 2020 by Trend Micro, the vulnerabilities in container components deployed with cloud architecture have been one of the top security concerns for development and operations teams in enterprises. Docker is one of the leading container technologies that automate the deployment of applications into containers. Docker Hub is a public repository by Docker for storing and sharing the Docker images. These Docker images are pulled from the Docker Hub repository and the security of images being used from the repositories in any cloud environment could be at risk. Vulnerabilities in Docker images could have a detrimental effect on enterprise applications. In this paper, the focus is on securing the Docker images using vulnerability centric approach (VCA) to detect the vulnerabilities. A set of use cases compliant with the NIST SP 800-190 Application Container Security Guide is developed for audit compliance of Docker container images with the OWASP Container Security Verification Standards (CSVS). In this paper, firs vulnerabilities of Docker container images are identified and assessed using the VCA. Then, a set of use cases to identify presence of the vulnerabilities is developed to facilitate the security audit of the container images. Finally, it is illustrated how the proposed use cases can be mapped with the requirements of the OWASP Container Security Verification Standards. The use cases can serve as a security auditing tool during the development, deployment, and maintenance of cloud microservices applications.

Vulnerability detection and exploitation serves as a milestone for secure development and identifying major threats in software applications. Automated exploit generation helps in easier identification of bugs, the attack vectors and the various possibilities of generation of the exploit payload. Thus, we introduce AngErza which uses dynamic and symbolic execution to identify hot-spots in the code, formulate constraints and generate a payload based on those constraints. Our tool is entirely based on angr which is an open-sourced offensive binary analysis framework. The work around AngErza focuses on exploit and vulnerability detection in CTF-style C binaries compiled on 64-bit Intel architecture for the early-phase of this project.

This prototype demonstrates the viability of manipulating both physical and virtual objects with the same tool in order to maintain object permanence across both modes of interaction. Using oppositional force feedback, provided by a servo, and an augmented visual interface, provided by the user’s smartphone, this tool simulates the look and feel of a physical object within an augmented environment. Additionally, the tool is also able to manipulate physical objects that are not part of the augmented reality, such as a physical nut. By integrating both modes of interaction into the same tool, users can fluidly move between these different modes of interaction, manipulating both physical and virtual objects as the need arises. By overlaying this kind of visual and haptic augmentation onto a common tool such as a pair of pliers, we hope to further explore scenarios for collaborative telepresence in future work.

Advances in artificial intelligence (AI) have provided a variety of solutions in several real-world complex problems. One of the current trends contains the integration of various AI tools to improve the proposed solutions. The question that has to be revisited is how tools may be put together to form efficient systems suitable for the problem at hand. This paper frames itself in the area of nuclear security where an agent uses a radiation sensor to survey an area for radiological threats. The main goal of this application is to identify anomalies in the measured data that designate the presence of nuclear material that may consist of a threat. To that end, we propose the integration of two kernel modeled Gaussian processes (GP) by using a fuzzy inference system. The GP models utilize different types of information to make predictions of the background radiation contribution that will be used to identify an anomaly. The integration of the prediction of the two GP models is performed with means of fuzzy rules that provide the degree of existence of anomalous data. The proposed system is tested on a set of real-world gamma-ray spectra taken with a low-resolution portable radiation spectrometer.

Over the past decade, an infotelecommunication technology has made significant strides forward. With the advent of new generation wireless networks and the massive digitalization of industries, the object of protection has changed. The digital transformation has led to an increased opportunity for cybercriminals. The ability of computational intelligence to quickly process large amounts of data makes the intrusions tailored to specific environments. Polymorphic attacks that have mutations in their sequences of acts adapt to the communication environments, operating systems and service frameworks, and also try to deceive the defense tools. The poor protection of most Internet of Things devices allows the attackers to take control over them creating the megabotnets. In this regard, traditional methods of network protection become rigid and low-effective. The paper reviews a computational intelligence (CI) enabled software- defined network (SDN) for the network management, providing dynamic network reconfiguration to improve network performance and security control. Advanced machine learning and artificial neural networks are promising in detection of false data injections. Bioinformatics methods make it possible to detect polymorphic attacks. Swarm intelligence detects dynamic routing anomalies. Quantum machine learning is effective at processing the large volumes of security-relevant datasets. The CI technology stack provides a comprehensive protection against a variative cyberthreats scope.

In this work, security analysis of a Smart Home System (SHS) is inspected. The paper focuses on describing common and likely cyber security threats against SHS. This includes both their influence on human privacy and safety. The SHS is properly presented and formed applying Architecture Analysis and Design Language (AADL), exhibiting the system layout, weaknesses, attack practices, besides their requirements and post settings. The obtained model is later inspected along with a security requirement with JKind model tester software for security endangerment. The overall attack graph causing system compromise is graphically given using Graphviz.

This research presents a model for assessing information systems cybersecurity maturity level. The main purpose of the model is to provide comprehensive support for information security specialists and auditors in checking information systems security level, checking security policy implementation, and compliance with security standards. The model synthesized based on controls and practices present in ISO 27001 and ISO 27002 and the neural network of direct signal propagation. The methodology described in this paper can also be extended to synthesis a model for different security control sets and, consequently, to verify compliance with another security standard or policy. The resulting model describes a real non-automated process of assessing the maturity of an IS at an acceptable level and it can be recommended to be used in the process of real audit of Information Security Management Systems.

In an increasingly connected world where products are just a click away, there is a growing need for systems that seek to equip consumers with the necessary tools to identify misrepresented products. Sub-standard ingredients used in the production of sanitary towels can pose a serious health risk to the consumer. Informal retailers or Spaza-shops have been accused of selling counterfeit food products to unsuspecting consumers. In this paper, we propose a system that can be used by consumers to scan a quick response (QR) code printed on the product. Built into an android application, is a system that applies the RSA public key encryption algorithm to secure the data prior to encoding into the QR code. The proposed system is also responsible for updating location data of previous scans on a dedicated cloud database. Upon completion of a field test, having collected months of consumer data, counterfeit prediction can be improved. In addition, a timely warning can be sent to a customer and relevant authorities if a unique product batch number is scanned outside of an expected area.

Security in today's world is one of the most important considerations when one wants to send, receive and store files containing private information or files simply too large for an email attachment. People are becoming more and more dependent on their mobile phones for performing the mentioned critical functionalities. Therefore, it is very important to protect sensitive information when the mobile is lost or stolen. There are many algorithms and methods used to accomplish data security in mobile devices. In general, cryptography and steganography are two common methods used to secure communications. Recently, the field of biology has been combined with the field of cryptography to produce a new field called deoxyribonucleic acid (DNA) cryptography which is one of the most powerful tools to solve security problems.This paper proposes a DNA cryptography technique for securing data stored offline in the Android device where users are not aware of the confidentiality of their private data. It is very difficult to predict the one-time pad key that is used as randomly generated and just for one-time. The proposed algorithm uses DNA mapping for dealing with the data as a DNA sequence. Two approaches have been proposed for achieving desired outcomes.

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods can become crucial. Inductive Logic Programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the process of data. Learning from Interpretation Transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given blackbox system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains.

Automated teller machines are affected by two kinds of attacks: physical and logical. It is common for most banks to look for zero-day protection for their devices. The most secure solutions available are based on complex security policies that are extremely hard to configure. The goal of this article is to present a concept of using the modified MajorClust algorithm for generating a sandbox-based security policy based on ATM usage data. The results obtained from the research prove the effectiveness of the used techniques and confirm that it is possible to create a division into sandboxes in an automated way.

Generally, the defense measures taken against new cyber-attack methods are insufficient for cybersecurity risk management. Contrary to classical attack methods, the existence of undiscovered attack types called' zero-day attacks' can invalidate the actions taken. It is possible with honeypot systems to implement new security measures by recording the attacker's behavior. The purpose of the honeypot is to learn about the methods and tools used by the attacker or malicious activity. In particular, it allows us to discover zero-day attack types and develop new defense methods for them. Attackers have made protocols such as SSH (Secure Shell) and Telnet, which are widely used for remote access to devices, primary targets. In this study, SSHTelnet honeypot was established using Cowrie software. Attackers attempted to connect, and attackers record their activity after providing access. These collected attacker log records and files uploaded to the system are published on Github to other researchers1. We shared the observations and analysis results of attacks on SSH and Telnet protocols with honeypot.

Anonymous Zether, proposed by Bünz, Agrawal, Zamani, and Boneh (FC'20), is a private payment design whose wallets demand little bandwidth and need not remain online; this unique property makes it a compelling choice for resource-constrained devices. In this work, we describe an efficient construction of Anonymous Zether. Our protocol features proofs which grow only logarithmically in the size of the "anonymity sets" used, improving upon the linear growth attained by prior efforts. It also features competitive transaction sizes in practice (on the order of 3 kilobytes).Our central tool is a new family of extensions to Groth and Kohlweiss's one-out-of-many proofs (Eurocrypt 2015), which efficiently prove statements about many messages among a list of commitments. These extensions prove knowledge of a secret subset of a public list, and assert that the commitments in the subset satisfy certain properties (expressed as linear equations). Remarkably, our communication remains logarithmic; our computation increases only by a logarithmic multiplicative factor. This technique is likely to be of independent interest.We present an open-source, Ethereum-based implementation of our Anonymous Zether construction.

A promising paradigm in protocol design is to hold parties accountable for misbehavior, instead of postulating that they are trustworthy. Recent approaches in defining this property, called accountability, characterized malicious behavior as a deviation from the protocol that causes a violation of the desired security property, but did so under the assumption that all deviating parties are controlled by a single, centralized adversary. In this work, we investigate the setting where multiple parties can deviate with or without coordination in a variant of the applied-π calculus.We first demonstrate that, under realistic assumptions, it is impossible to determine all misbehaving parties; however, we show that accountability can be relaxed to exclude causal dependencies that arise from the behavior of deviating parties, and not from the protocol as specified. We map out the design space for the relaxation, point out protocol classes separating these notions and define conditions under which we can guarantee fairness and completeness. Most importantly, we discover under which circumstances it is correct to consider accountability in the single-adversary setting, where this property can be verified with off-the-shelf protocol verification tools.

Advanced Persistent Threat (APT) is a professional stealthy threat actor who uses continuous and sophisticated attack techniques which have not been well mitigated by existing defense strategies. This paper proposes an APT-style cyber-attack tested for distributed energy resources (DER) in cyber-physical environments. The proposed security testbed consists of: 1) a real-time DER simulator; 2) a real-time cyber system using real network systems and a server; and 3) penetration testing tools generating APT-style attacks as cyber events. Moreover, this paper provides a cyber kill chain model for a DER system based on a latest MITRE’s cyber kill chain model to model possible attack stages. Several real cyber-attacks are created and their impacts in a DER system are provided to validate the feasibility of the proposed security testbed for DER systems.

The paper considers the issue of recurrent neural networks applicability for detecting industrial process anomalies to detect intrusion in Industrial Control Systems. Cyberattack on Industrial Control Systems often leads to appearing of anomalies in industrial process. Thus, it is proposed to detect such anomalies by forecasting the state of an industrial process using a recurrent neural network and comparing the predicted state with actual process' state. In the course of experimental research, a recurrent neural network with one-dimensional convolutional layer was implemented. The Secure Water Treatment dataset was used to train model and assess its quality. The obtained results indicate the possibility of using the proposed method in practice. The proposed method is characterized by the absence of the need to use anomaly data for training. Also, the method has significant interpretability and allows to localize an anomaly by pointing to a sensor or actuator whose signal does not match the model's prediction.

Cryptographic (crypto) algorithms are the essential ingredients of all secure systems: crypto hash functions and encryption algorithms, for example, can guarantee properties such as integrity and confidentiality. Developers, however, can misuse the application programming interfaces (API) of such algorithms by using constant keys and weak passwords. This paper presents CRYLOGGER, the first open-source tool to detect crypto misuses dynamically. CRYLOGGER logs the parameters that are passed to the crypto APIs during the execution and checks their legitimacy offline by using a list of crypto rules. We compared CRYLOGGER with CryptoGuard, one of the most effective static tools to detect crypto misuses. We show that our tool complements the results of CryptoGuard, making the case for combining static and dynamic approaches. We analyzed 1780 popular Android apps downloaded from the Google Play Store to show that CRYLOGGER can detect crypto misuses on thousands of apps dynamically and automatically. We reverse-engineered 28 Android apps and confirmed the issues flagged by CRYLOGGER. We also disclosed the most critical vulnerabilities to app developers and collected their feedback.

Python libraries are widely used for machine learning and scientific computing tasks today. APIs in Python libraries are deprecated due to feature enhancements and bug fixes in the same way as in other languages. These deprecated APIs are discouraged from being used in further software development. Manually detecting and replacing deprecated APIs is a tedious and time-consuming task due to the large number of API calls used in the projects. Moreover, the lack of proper documentation for these deprecated APIs makes the task challenging. To address this challenge, we propose an algorithm and a tool APIScanner that automatically detects deprecated APIs in Python libraries. This algorithm parses the source code of the libraries using abstract syntax tree (ASTs) and identifies the deprecated APIs via decorator, hard-coded warning or comments. APIScanner is a Visual Studio Code Extension that highlights and warns the developer on the use of deprecated API elements while writing the source code. The tool can help developers to avoid using deprecated API elements without the execution of code. We tested our algorithm and tool on six popular Python libraries, which detected 838 of 871 deprecated API elements. Demo of APIScanner: https://youtu.be/1hy\_ugf-iek. Documentation, tool, and source code can be found here: https://rishitha957.github.io/APIScanner.