Biblio

The secure Internet of Things (loT) increasingly relies on digital cryptographic signatures which require a private signature and public verification key. By their intrinsic nature, public keys are meant to be accessible to any interested party willing to verify a given signature. Thus, the storing of such keys is of great concern, since an adversary shall not be able to tamper with the public keys, e.g., on a local filesystem. Commonly used public-key infrastructures (PKIs), which handle the key distribution and storage, are not feasible in most use-cases, due to their resource intensity and high complexity. Thus, the general storing of the public verification keys is of notable interest for low-resource loT networks. By using the Distributed Ledger Technology (DLT), this paper proposes a decentralized concept for storing public signature verification keys in a tamper-resistant, secure, and resilient manner. By combining lightweight public-key exchange protocols with the proposed approach, the storing of verification keys becomes scalable and especially suitable for low-resource loT devices. This paper provides a Proof-of-Concept implementation of the DLT public-key store by extending our previously proposed NFC-Key Exchange (NFC-KE) protocol with a decentralized Hyperledger Fabric public-key store. The provided performance analysis shows that by using the decentralized keystore, the NFC- KE protocol gains an increased tamper resistance and overall system resilience while also showing expected performance degradations with a low real-world impact.

Supervisory control and data acquisition (SCADA) systems play pivotal role in the operation of modern critical infrastructures (CIs). Technological advancements, innovations, economic trends, etc. have continued to improve SCADA systems effectiveness and overall CIs’ throughput. However, the trends have also continued to expose SCADA systems to security menaces. Intrusions and attacks on SCADA systems can cause service disruptions, equipment damage or/and even fatalities. The use of conventional intrusion detection models have shown trends of ineffectiveness due to the complexity and sophistication of modern day SCADA attacks and intrusions. Also, SCADA characteristics and requirement necessitate exceptional security considerations with regards to intrusive events’ mitigations. This paper explores the viability of supervised learning algorithms in detecting intrusions specific to SCADA systems and their communication protocols. Specifically, we examine four supervised learning algorithms: Random Forest, Naïve Bayes, J48 Decision Tree and Sequential Minimal Optimization-Support Vector Machines (SMO-SVM) for evaluating SCADA datasets. Two SCADA datasets were used for evaluating the performances of our approach. To improve the classification performances, feature selection using principal component analysis was used to preprocess the datasets. Using prominent classification metrics, the SVM-SMO presented the best overall results with regards to the two datasets. In summary, results showed that supervised learning algorithms were able to classify intrusions targeted against SCADA systems with satisfactory performances.

Emails are widely used as a form of communication and sharing files in an organization. However, email is widely used by cybercriminals to spread malware and carrying out cyber-attacks. We implemented an open-source email gateway in conjunction with a security sandbox for securing emails against malicious attachments. The email gateway scans all incoming and outgoing emails and stops emails containing suspicious files. An automated python script would then send the suspected email to the sandboxing element through sandbox API for further analysis, while the script is used also for the prevention of duplicate results. Moreover, the mail server administrator receives notifications from the email gateway about suspicious attachments. If detected attachment is a true positive based on the sandbox analysis result, email is deleted, otherwise, the email is delivered to the recipient. The paper describes in an empirical way the steps followed during the implementation, results, and conclusions of our research.

To train new staff to be efficient and ready for the tasks assigned is vital. They must be equipped with knowledge and skills so that they can carry out their responsibility to ensure smooth daily working activities. As transitioning to IPv6 has taken place for more than a decade, it is understood that having a dual-stack network is common in any organization or enterprise. However, many Internet users may not realize the importance of IPv6 security due to a lack of awareness and knowledge of cyber and computer security. Therefore, this paper presents an approach to educating people by introducing a security mechanisms model that can be applied in handling security challenges via network sandboxing by setting up an isolated dual stack network testbed using GNS3 to perform network security analysis. The finding shows that applying security mechanisms such as access control lists (ACLs) and host-based firewalls can help counter the attacks. This proves that knowledge and skills to handle dual-stack security are crucial. In future, more kinds of attacks should be tested and also more types of security mechanisms can be applied on a dual-stack network to provide more information and to provide network engineers insights on how they can benefit from network sandboxing to sharpen their knowledge and skills.

ROS 2 is rapidly becoming a standard in the robotics industry. Built upon DDS as its default communication middleware and used in safety-critical scenarios, adding secu-rity to robots and ROS computational graphs is increasingly becoming a concern. The present work introduces SROS2, a series of developer tools and libraries that facilitate adding security to ROS 2 graphs. Focusing on a usability-centric approach in SROS2, we present a methodology for securing graphs systematically while following the DevSecOps model. We also demonstrate the use of our security tools by presenting an application case study that considers securing a graph using the popular Navigation2 and SLAM Toolbox stacks applied in a TurtieBot3 robot. We analyse the current capabilities of SROS2 and discuss the shortcomings, which provides insights for future contributions and extensions. Ultimately, we present SROS2 as usable security tools for ROS 2 and argue that without usability, security in robotics will be greatly impaired.

Robot Operating System 2 (ROS2) is the latest release of a framework for enabling robot applications. Data Distribution Service (DDS) middleware is used for communication between nodes in a ROS2 cluster. The DDS middleware provides a distributed discovery system, message definitions and serialization, and security. In ROS2, the DDS middleware is accessed through an abstraction layer, making it easy to switch from one implementation to another. The existing middleware implementations differ in a number of ways, e.g., in how they are supported in ROS2, in their support for the security features, their ease of use, their performance, and their interoperability. In this work, the focus is on the ease of use, interoperability, and security features aspects of ROS2 DDS middleware. We compare the ease of installation and ease of use of three different DDS middleware, and test the interoperability of different middleware combinations in simple deployment scenarios. We highlight the difference that enabling the security option makes to interoperability, and conduct performance experiments that show the effect that turning on security has on the communication performance. Our results provide guidelines for choosing and deploying DDS middleware on a ROS2 cluster.

AbuSaif is a human-like social robot designed and built at the UAE University's Artificial Intelligence and Robotics Lab. AbuSaif was initially operated by a classical personal computer (PC), like most of the existing social robots. Thus, most of the robot's functionalities are limited to the capacity of that mounted PC. To overcome this, in this study, we propose a web-based platform that shall take the benefits of clustering in cloud computing. Our proposed platform will increase the operational capability and functionality of AbuSaif, especially those needed to operate artificial intelligence algorithms. We believe that the robot will become more intelligent and autonomous using our proposed web platform.

The Robotic Operating System (ROS) is a popular framework for robotics research and development. It's a system that provides hardware abstraction with low-level device management to handle communications and services. ROS is a distributed system, which allows various nodes in a network to communicate using a method such as message passing. When integrating systems using ROS, it is vital to consider the security and privacy of the data and information shared across ROS nodes, which is considered to be one of the most challenging aspects of ROS systems. The goal of this study is to examine the ROS architecture, primary components, and versions, as well as the types of vulnerabilities that might compromise the system. In order to achieve the CIA's three fundamental security criteria on a ROS-based platform, we categorized these vulnerabilities and looked into various security solutions proposed by researchers. We provide a comparative analysis of the ROS-related security solutions, the security threats and issues they addressed, the targeted architecture of the protection or defense system, the solution's evaluation methodology and the evaluation metric, and the limitations that might be viewed as unresolved issues for the future course of action. Finally, we look into future possibilities and open challenges to assist researchers to develop more secure and efficient ROS systems.

This study purpose was to examine the determinant factors that affect the Micro, Small, and Medium Enterprise (MSME) merchants who had the intention to use Quick Response Code Indonesian Standard (QRIS) as a payment system. QRIS was expected to be applied by merchants to diminish the virus spread and keep the circulation of money safe; but there were not many merchants using the QRIS as a payment method. The factors MSME merchant might not use the QRIS were related to perceived usefulness, perceived security, perceived ease of use, and trust. The population was MSMEs in South Tangerang City who did not use QRIS yet and the population was unknown. Using the Lemeshow formula, obtained a sample of 115 people, and the sampling technique used purposive sampling. Then data were analyzed using multi-regression analysis and processed by SPSS. The results indicated that perceived usefulness and perceived security had a significant affect on trust, whereas trust and ease of use significant affect the intention to use QRIS. Moreover, trust was able to mediate the perceived usefulness to intention to use. Since ease of use had no significant affect on trust, then the mediation given by trust to perceived ease of use had no significant affect on intention to use.

Internet speeds and technological advancements have made individuals increasingly concerned about their personal information being compromised by criminals. There have been a slew of new steganography and data concealment methods suggested in recent years. Steganography is the art of hiding information in plain sight (text, audio, image and video). Unauthorized users now have access to steganographic analysis software, which may be used to retrieve the carrier files valuable secret information. Unfortunately, because to their inefficiency and lack of security, certain steganography techniques are readily detectable by steganalytical detectors. We present a video steganography technique based on the linear block coding concept that is safe and secure. Data is protected using a binary graphic logo but also nine uncompressed video sequences as cover data and a secret message. It's possible to enhance the security by rearranging pixels randomly in both the cover movies and the hidden message. Once the secret message has been encoded using the Hamming algorithm (7, 4) before being embedded, the message is even more secure. The XOR function will be used to add the encoded message's result to a random set of values. Once the message has been sufficiently secured, it may be inserted into the video frames of the cover. In addition, each frame's embedding region is chosen at random so that the steganography scheme's resilience can be improved. In addition, our experiments have shown that the approach has a high embedding efficiency. The video quality of stego movies is quite close to the original, with a PSNR (Pick Signal to Noise Ratio) over 51 dB. Embedding a payload of up to 90 Kbits per frame is also permissible, as long as the quality of the stego video is not noticeably degraded.

Global traffic data are proliferating, including in Indonesia. The number of internet users in Indonesia reached 205 million in January 2022. This data means that 73.7% of Indonesia’s population has used the internet. The median internet speed for mobile phones in Indonesia is 15.82 Mbps, while the median internet connection speed for Wi-Fi in Indonesia is 20.13 Mbps. As predicted by many, real-time traffic such as multimedia streaming dominates more than 79% of traffic on the internet network. This condition will be a severe challenge for the internet network, which is required to improve the Quality of Experience (QoE) for user mobility, such as reducing delay, data loss, and network costs. However, IP-based networks are no longer efficient at managing traffic. Named Data Network (NDN) is a promising technology for building an agile communication model that reduces delays through a distributed and adaptive name-based data delivery approach. NDN replaces the ‘where’ paradigm with the concept of ‘what’. User requests are no longer directed to a specific IP address but to specific content. This paradigm causes responses to content requests to be served by a specific server and can also be served by the closest device to the requested data. NDN router has CS to cache the data, significantly reducing delays and improving the internet network’s quality of Service (QoS). Motivated by this, in 2019, we began intensive research to achieve a national flagship product, an NDN router with different functions from ordinary IP routers. NDN routers have cache, forwarding, and routing functions that affect data security on name-based networks. Designing scalable NDN routers is a new challenge as NDN requires fast hierarchical name-based lookups, perpackage data field state updates, and large-scale forward tables. We have a research team that has conducted NDN research through simulation, emulation, and testbed approaches using virtual machines to get the best NDN router design before building a prototype. Research results from 2019 show that the performance of NDN-based networks is better than existing IP-based networks. The tests were carried out based on various scenarios on the Indonesian network topology using NDNsimulator, MATLAB, Mininet-NDN, and testbed using virtual machines. Various network performance parameters, such as delay, throughput, packet loss, resource utilization, header overhead, packet transmission, round trip time, and cache hit ratio, showed the best results compared to IP-based networks. In addition, NDN Testbed based on open source is free, and the flexibility of creating topology has also been successfully carried out. This testbed includes all the functions needed to run an NDN network. The resource capacity on the server used for this testbed is sufficient to run a reasonably complex topology. However, bugs are still found on the testbed, and some features still need improvement. The following exploration of the NDN testbed will run with more new strategy algorithms and add Artificial Intelligence (AI) to the NDN function. Using AI in cache and forwarding strategies can make the system more intelligent and precise in making decisions according to network conditions. It will be a step toward developing NDN router products by the Bandung Institute of Technology (ITB) Indonesia.

Smart Security Solutions are in high demand with the ever-increasing vulnerabilities within the IT domain. Adjusting to a Work-From-Home (WFH) culture has become mandatory by maintaining required core security principles. Therefore, implementing and maintaining a secure Smart Home System has become even more challenging. ARGUS provides an overall network security coverage for both incoming and outgoing traffic, a firewall and an adaptive bandwidth management system and a sophisticated CCTV surveillance capability. ARGUS is such a system that is implemented into an existing router incorporating cloud and Machine Learning (ML) technology to ensure seamless connectivity across multiple devices, including IoT devices at a low migration cost for the customer. The aggregation of the above features makes ARGUS an ideal solution for existing Smart Home System service providers and users where hardware and infrastructure is also allocated. ARGUS was tested on a small-scale smart home environment with a Raspberry Pi 4 Model B controller. Its intrusion detection system identified an intrusion with 96% accuracy while the physical surveillance system predicts the user with 81% accuracy.

This paper shows that the modern high customizable Xtensa architecture for embedded devices is exploitable by Return-Oriented Programming (ROP) attacks. We used a simple Hello-World application written with the RIOT OS as an almost minimal code basis for determining if the number of gadgets that can be found in this code base is sufficient to build a reasonably complex attack. We determined 859 found gadgets which are sufficient to create a gadget catalog for the Xtensa. Despite the code basis used being really small, the presented gadget catalog provides Turing completeness, which allows an arbitrary computation of any exploit program.

Control flow integrity (CFI) checks are used in desktop systems, in order to protect them from various forms of attacks, but they are rarely investigated for embedded systems, due to their introduced overhead. The contribution of this paper is an efficient software implementation of a CFI-check for ARM-and Xtensa processors. Moreover, we propose the combination of this CFI-check with another defense mechanism against return-oriented-programming (ROP). We show that by this combination the security is significantly improved. Moreover, it will also in-crease the safety of the system, since the combination can detect a failed ROP-attack and bring the system in a safe state, which is not possible when using each technique separately. We will also report on the introduced overhead in code size and run time.

Human safety has always been the main priority when working near an industrial robot. With the rise of Human-Robot Collaborative environments, physical barriers to avoiding collisions have been disappearing, increasing the risk of accidents and the need for solutions that ensure a safe Human-Robot Collaboration. This paper proposes a safety system that implements Speed and Separation Monitoring (SSM) type of operation. For this, safety zones are defined in the robot's workspace following current standards for industrial collaborative robots. A deep learning-based computer vision system detects, tracks, and estimates the 3D position of operators close to the robot. The robot control system receives the operator's 3D position and generates 3D representations of them in a simulation environment. Depending on the zone where the closest operator was detected, the robot stops or changes its operating speed. Three different operation modes in which the human and robot interact are presented. Results show that the vision-based system can correctly detect and classify in which safety zone an operator is located and that the different proposed operation modes ensure that the robot's reaction and stop time are within the required time limits to guarantee safety.

The success of human-robot interaction is strongly affected by the people’s ability to infer others’ intentions and behaviours, and the level of people’s trust that others will abide by their same principles and social conventions to achieve a common goal. The ability of understanding and reasoning about other agents’ mental states is known as Theory of Mind (ToM). ToM and trust, therefore, are key factors in the positive outcome of human-robot interaction. We believe that a robot endowed with a ToM is able to gain people’s trust, even when this may occasionally make errors.In this work, we present a user study in the field in which participants (N=123) interacted with a robot that may or may not have a ToM, and may or may not exhibit erroneous behaviour. Our findings indicate that a robot with ToM is perceived as more reliable, and they trusted it more than a robot without a ToM even when the robot made errors. Finally, ToM results to be a key driver for tuning people’s trust in the robot even when the initial condition of the interaction changed (i.e., loss and regain of trust in a longer relationship).

In the last decade, numerous Industrial IoT systems have been deployed. Attack vectors and security solutions for these are an active area of research. However, to the best of our knowledge, only very limited insight in the applicability and real-world comparability of attacks exists. To overcome this widespread problem, we have developed and realized an approach to collect attack traces at a larger scale. An easily deployable system integrates well into existing networks and enables the investigation of attacks on unmodified commercial devices.

People are increasingly sharing their details online as internet usage grows. Therefore, fraudsters have access to a massive amount of information and financial activities. The attackers create web pages that seem like reputable sites and transmit the malevolent content to victims to get them to provide subtle information. Prevailing phishing security measures are inadequate for detecting new phishing assaults. To accomplish this aim, objective to meet for this research is to analyses and compare phishing website and legitimate by analyzing the data collected from open-source platforms through a survey. Another objective for this research is to propose a method to detect fake sites using Decision Tree and Random Forest approaches. Microsoft Form has been utilized to carry out the survey with 30 participants. Majority of the participants have poor awareness and phishing attack and does not obverse the features of interface before accessing the search browser. With the data collection, this survey supports the purpose of identifying the best phishing website detection where Decision Tree and Random Forest were trained and tested. In achieving high number of feature importance detection and accuracy rate, the result demonstrates that Random Forest has the best performance in phishing website detection compared to Decision Tree.

Phishing emails are becoming more and more sophisticated, making current detection techniques ineffective. The reporting of phishing emails from users is, thus, crucial for organizations to detect phishing attacks and mitigate their effect. Despite extensive research on how the believability of a phishing email affects detection rates, there is little to no research about the relationship between the believability of a phishing email and the associated reporting rate. In this work, we present a controlled experiment with 446 subjects to evaluate how the reporting rate of a phishing email is linked to its believability and detection rate. Our results show that the reporting rate decreases as the believability of the email increases and that around half of the subjects who detect the mail as phishing, have an intention to report the email. However, the group intending to report an email is not a subset of the group detecting the mail as phishing, suggesting that reporting is still a concept misunderstood by many.

Many organizations use internal phishing campaigns to gauge awareness and coordinate training efforts based on those findings. Ongoing content design is important for phishing training tools due to the influence recency has on phishing susceptibility. Traditional approaches for content development require significant investment and can be prohibitively costly, especially during the requirements engineering phase of software development and for applications that are constantly evolving. While prior research primarily depends upon already known phishing cues curated by experts, our project, Phish Finders, uses crowdsourcing to explore phishing cues through the unique perspectives and thought processes of everyday users in a realistic yet safe online environment, Zooniverse. This paper contributes qualitative analysis of crowdsourced comments that identifies novel cues, such as formatting and typography, which were identified by the crowd as potential phishing indicators. The paper also shows that crowdsourcing may have the potential to scale as a requirements engineering approach to meet the needs of content labeling for improved training tool development.

Phishing is a method of online fraud where attackers are targeted to gain access to the computer systems for monetary benefits or personal gains. In this case, the attackers pose themselves as legitimate entities to gain the users' sensitive information. Phishing has been significant concern over the past few years. The firms are recording an increase in phishing attacks primarily aimed at the firm's intellectual property and the employees' sensitive data. As a result, these attacks force firms to spend more on information security, both in technology-centric and human-centric approaches. With the advancements in cyber-security in the last ten years, many techniques evolved to detect phishing-related activities through websites and emails. This study focuses on the latest techniques used for detecting phishing attacks, including the usage of Visual selection features, Machine Learning (ML), and Artificial Intelligence (AI) to see the phishing attacks. New strategies for identifying phishing attacks are evolving, but limited standardized knowledge on phishing identification and mitigation is accessible from user awareness training. So, this study also focuses on the role of security-awareness movements to minimize the impact of phishing attacks. There are many approaches to train the user regarding these attacks, such as persona-centred training, anti-phishing techniques, visual discrimination training and the usage of spam filters, robust firewalls and infrastructure, dynamic technical defense mechanisms, use of third-party certified software to mitigate phishing attacks from happening. Therefore, the purpose of this paper is to carry out a systematic analysis of literature to assess the state of knowledge in prominent scientific journals on the identification and prevention of phishing. Forty-three journal articles with the perspective of phishing detection and prevention through awareness training were reviewed from 2011 to 2020. This timely systematic review also focuses on the gaps identified in the selected primary studies and future research directions in this area.

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform's virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans.

With the development of 5G networking technology on the Internet of Vehicle (IoV), there are new opportunities for numerous cyber-attacks, such as in-vehicle attacks like hijacking occurrences and data theft. While numerous attempts have been made to protect against the potential attacks, there are still many unsolved problems such as developing a fine-grained access control system. This is reflected by the granularity of security as well as the related data that are hosted on these platforms. Among the most notable trends is the increased usage of smart devices, IoV, cloud services, emerging technologies aim at accessing, storing and processing data. Most popular authentication protocols rely on knowledge-factor for authentication that is infamously known to be vulnerable to subversions. Recently, the zero-trust framework has drawn huge attention; there is an urgent need to develop further the existing Continuous Authentication (CA) technique to achieve the zero-trustiness framework. In this paper, firstly, we develop the static authentication process and propose a secured protocol to generate the smart key for user to unlock the vehicle. Then, we proposed a novel and secure continuous authentication system for IoVs. We present the proof-of-concept of our CA scheme by building a prototype that leverages the commodity fingerprint sensors, NFC, and smartphone. Our evaluations in real-world settings demonstrate the appropriateness of CA scheme and security analysis of our proposed protocol for digital key suggests its enhanced security against the known attack-vector.

With billions of devices already connected to the network's edge, the Internet of Things (IoT) is shaping the future of pervasive computing. Nonetheless, IoT applications still cannot escape the need for the computing resources available at the fog layer. This becomes challenging since the fog nodes are not necessarily secure nor reliable, which widens even further the IoT threat surface. Moreover, the security risk appetite of heterogeneous IoT applications in different domains or deploy-ment contexts should not be assessed similarly. To respond to this challenge, this paper proposes a new approach to optimize the allocation of secure and reliable fog computing resources among IoT applications with varying security risk level. First, the security and reliability levels of fog nodes are quantitatively evaluated, and a security risk assessment methodology is defined for IoT services. Then, an online, incentive-compatible mechanism is designed to allocate secure fog resources to high-risk IoT offloading requests. Compared to the offline Vickrey auction, the proposed mechanism is computationally efficient and yields an acceptable approximation of the social welfare of IoT devices, allowing to attenuate security risk within the edge network.

Nowadays, the messaging system is one of the most popular mobile applications, and therefore the authentication between clients is essential. Various kinds of such mobile applications are using encryption-based security protocols, but they are facing many security threat issues. It clearly defines the necessity for a trustful security procedure. Therefore, a blockchain-based messaging system could be an alternative to this problem. That is why, we have developed a secured peer-to-peer messaging system supported by blockchain. This proposed mechanism provides data security among the users. In a blockchain-based framework, all the information can be verified and controlled automatically and all the transactions are recorded that have been created already. In our paper, we have explained how the users can communicate through a blockchain-based messaging system that can maintain a secured network. We explored why blockchain would improve communication security in this post, and we proposed a model architecture for blockchain-based messaging that retains the performance and security of data stored on the blockchain. Our proposed architecture is completely decentralized and enables users to send and receive messages in an acceptable and secure manner.