Biblio

Electrical substations in power grid act as the critical interface points for the transmission and distribution networks. Over the years, digital technology has been integrated into the substations for remote control and automation. As a result, substations are more prone to cyber attacks and exposed to digital vulnerabilities. One of the notable cyber attack vectors is the malicious command injection, which can lead to shutting down of substations and subsequently power outages as demonstrated in Ukraine Power Plant Attack in 2015. Prevailing measures based on cyber rules (e.g., firewalls and intrusion detection systems) are often inadequate to detect advanced and stealthy attacks that use legitimate-looking measurements or control messages to cause physical damage. Additionally, defenses that use physics-based approaches (e.g., power flow simulation, state estimation, etc.) to detect malicious commands suffer from high latency. Machine learning serves as a potential solution in detecting command injection attacks with high accuracy and low latency. However, sufficient datasets are not readily available to train and evaluate the machine learning models. In this paper, focusing on this particular challenge, we discuss various approaches for the generation of synthetic data that can be used to train the machine learning models. Further, we evaluate the models trained with the synthetic data against attack datasets that simulates malicious commands injections with different levels of sophistication. Our findings show that synthetic data generated with some level of power grid domain knowledge helps train robust machine learning models against different types of attacks.

Fractional repetition (FR) codes are a special family of regenerating codes with the repair-by-transfer property. The constructions of FR codes are naturally related to combinatorial designs, graphs, and hypergraphs. Given the file size of an FR code, it is desirable to determine the minimum number of storage nodes needed. The problem is related to an extremal graph theory problem, which asks for the minimum number of vertices of an α-regular graph such that any subgraph with k vertices has at most δ edges. In this paper, we present a class of regular graphs for this problem to give the bounds for the minimum number of storage nodes for the FR codes.

Browsers are one of the most widely used types of software around the world. This prevalence makes browsers a prime target for cyberattacks. To mitigate these threats, users can practice safe browsing habits and take advantage of the security features available to browsers. These protections, however, could be severely crippled if the browser itself were malicious. Presented in this paper is the concept of the evil-twin browser (ETB), a clone of a legitimate browser that looks and behaves identically to the original browser, but discreetly performs other tasks that harm a user's security. To better understand the concept of the evil-twin browser, a prototype ETB named ChroNe was developed. The creation and installation process of ChroN e is discussed in this paper. This paper also explores the motivation behind creating such a browser, examines existing relevant work, inspects the open-source codebase Chromium that assisted in ChroNe's development, and discusses relevant topics like ways to deliver an ETB, the capabilities of an ETB, and possible ways to defend against ETBs.

A brute force is a Hacking methodology used to decrypt login passwords, keys and credentials. Hacks that exploit vulnerabilities in packages are rare, whereas Brute Force attacks aim to be the simplest, cheapest, and most straightforward approach to access a website. Using Splunk to analyse massive amounts of data could be very beneficial. The application enables to capture, search, and analyse log information in real-time. By analysing logs as well as many different sources of system information, security events can be uncovered. A log file, which details the events that have occurred in the environment of the application and the server on which they run, is a valuable piece of information. Identifying the attacks against these systems is possible by analysing and correlating this information. Massive amounts of ambiguous and amorphous information can be analysed with its superior resolution. The paper includes instructions on setting up a Splunk server and routing information there from multiple sources. Practical search examples and pre-built add-on applications are provided. Splunk is a powerful tool that allows users to explore big data with greater ease. Seizure can be tracked in near real-time and can be searched through logs. A short amount of time can be spent on analysing big data using map-reduce technology. Briefly, it helps to analyse unstructured log data to better understand how the applications operate. With Splunk, client can detect patterns in the data through a powerful query language. It is easy to set up alerts and warnings based on the queries, which will help alert client about an ongoing (suspected) activity and generate a notification in real-time.

This paper dives into the growing world of IoT botnets that have taken the world by storm in the past five years. Though alone an IP camera cannot produce enough traffic to be considered a DDoS. But a botnet that has over 150,000 connected IP cameras can generate as much as 1 Tbps in traffic. Botnets catch many by surprise because their attacks and infections may not be as apparent as a DDoS, some other cases include using these cameras and printers for extracting information or quietly mine cryptocurrency at the IoT device owner's expense. Here we analyze damages on IoT hacking and define botnet architecture. An overview of Mirai botnet and cryptojacking provided to better understand the IoT botnets.

Cloud service uses CAPTCHA to protect itself from malicious programs. With the explosive development of AI technology and the emergency of third-party recognition services, the factors that influence CAPTCHA’s security are going to be more complex. In such a situation, evaluating the security of mainstream CAPTCHAs in cloud services is helpful to guide better CAPTCHA design choices for providers. In this paper, we evaluate and analyze the security of 6 mainstream CAPTCHA image designs in public cloud services. According to the evaluation results, we made some suggestions of CAPTCHA image design choices to cloud service providers. In addition, we particularly discussed the CAPTCHA images adopted by Facebook and Twitter. The evaluations are separated into two stages: (i) using AI techniques alone; (ii) using both AI techniques and third-party services. The former is based on open source models; the latter is conducted under our proposed framework: CAPTCHAMix.

With the advent of massive machine type of communications, security protection becomes more important than ever. Efforts have been made to impose security protection capability to physical-layer signal design, so called physical-layer security (PLS). The purpose of this paper is to evaluate the performance of PLS schemes for a multi-input-multi-output (MIMO) systems with space-time block coding (STBC) under imperfect channel estimation. Three PLS schemes for STBC schemes are modeled and their bit error rate (BER) performances are evaluated under various channel estimation error environments, and their performance characteristics are analyzed.

Bitcoin is a famously decentralized cryptocurrency. Bitcoin is excellent because it is a digital currency that provides convenience and security in transactions. Transaction security in Bitcoin uses a consensus involving a distributed system, the security of this system generates a hash sequence with a Proof of Work (PoW) mechanism. However, in its implementation, various attacks appear that are used to generate profits from the existing system. Attackers can use various types of methods to get an unfair portion of the mining income. Such attacks are commonly referred to as Mining attacks. Among which the famous is the Selfish Mining attack. In this study, we simulate the effect of changing decision matrix, attacker region, attacker hash rate on selfish miner attacks by using the opensource NS3 platform. The experiment aims to see the effect of using 1%, 10%, and 20% decision matrices with different attacker regions and different attacker hash rates on Bitcoin selfish mining income. The result of this study shows that regional North America and Europe have the advantage in doing selfish mining attacks. This advantage is also supported by increasing the decision matrix from 1%, 10%, 20%. The highest attacker income, when using decision matrix 20% in North America using 16 nodes on 0.3 hash rate with income 129 BTC. For the hash rate, the best result for a selfish mining attack is between 27% to 30% hash rate.

Dielectric capacitors have attracted attention as energy storage devices that can achieve rapid charge and discharge. But the key to restricting its development is the low energy storage density of dielectric materials. Polyvinylidene fluoride (PVDF), as a polymer with high dielectric properties, is expected to improve the energy storage density of dielectric materials. In this work, the multilayer structure of PVDF ferroelectric polymer is designed, and the influence of the number of layers on the maximum polarization, remanent polarization, applied electric field and energy storage density of the dielectric material is studied. The final obtained double-layer PVDF obtained a discharge energy storage density of 10.6 J/cm3 and an efficiency of 49.1% at an electric field of 410 kV/mm; the three-layer PVDF obtained a discharge energy storage density of 11.0 J/cm3 and an efficiency of 37.2% at an electric field of 440 kV/mm.

Physical layer secret key exploits the random but reciprocal channel features between legitimate users to encrypt their data against fiber-tapping. We propose a novel tapping-based eavesdropper scheme, leveraging its tapped signals from legitimate users to reconstruct their common features and the secret key.

Safety-critical systems require resiliency against both cyberattacks and environmental faults. Researches have shown that microkernels can isolate components and limit the capabilities of would-be attackers by confining the attack in the component that it is initiated in. This limits the propagation of faults to sensitive components in the system. Nonetheless, the isolation mechanism in microkernels is not fully investigated for its resiliency against hardware faults. This paper investigates whether microkernels provide protection against hardware faults and, if so, to what extent quantitatively. This work is part of an effort in establishing an overlap between security and reliability with the goal of maximizing both while minimizing their impact on performance. In this work, transient faults are emulated on the seL4 microkernel and Linux kernel using debugger-induced bit flips across random timestamps in benchmark applications. Results show differences in the frequency and final outcome of fault to error manifestation in the seL4 environment compared to the Linux environment, including a reduction in silent data corruptions.

Recent scientific results show that audio feedback associated with body movements can be fundamental during the development to learn new spatial concepts [1], [2]. Within the weDraw project [3], [4], we have investigated how this link can be useful to learn mathematical concepts. Here we present a study investigating how mathematical skills changes after multisensory training based on human-computer interaction (RobotAngle and BodyFraction activities). We show that embodied angle and fractions exploration associated with audio and visual feedback can be used in typical children to improve cognition of spatial mathematical concepts. We finally present the exploitation of our results: an online, optimized version of one of the tested activity to be used at school. The training result suggests that audio and visual feedback associated with body movements is informative for spatial learning and reinforces the idea that spatial representation development is based on sensory-motor interactions.

User authentication based on muscle tension manifested during password typing seems to be an interesting additional layer of security. It represents another way of verifying a person’s identity, for example in the context of continuous verification. In order to explore the possibilities of such authentication method, it was necessary to create a capturing software that records and stores data from EMG (electromyography) sensors, enabling a subsequent analysis of the recorded data to verify the relevance of the method. The work presented here is devoted to the design, implementation and evaluation of such a solution. The solution consists of a protocol and a software application for collecting multimodal data when typing on a keyboard. Myo armbands on both forearms are used to capture EMG and inertial data while additional modalities are collected from a keyboard and a camera. The user experience evaluation of the solution is presented, too.

This research studies the effect of a countdown timer and a count-up timer on the keystroke pattern of the student and finds out whether changing the timer type changes the keystroke pattern. It also points out which timer affects more students in a timer environment during exams. We used two hypothesis testing statistical Algorithms, namely, the Two-Sample T-Test and One-way ANOVA Test, for analysis to identify the effect of different times our whether significant differences were found in the keystroke pattern or not when different timers were used. The supporting results have been found with determines that timer change can change the keystroke pattern of the student and from the study of hypothesis testing, different students result from different types of stress when they are under different timer environments.

In recent years, the use of the Internet of Things (IoT) has increased rapidly in different areas. Due to many IoT applications, many limitations have emerged such as power consumption and limited resources. The security of connected devices is becoming more and more a primary need for the reliability of systems. Among other things, power consumption remains an essential constraint with a major impact on the quality of the encryption system. For these, several lightweight cryptography algorithms were proposed and developed. The PRESENT algorithm is one of the lightweight block cipher algorithms that has been proposed for a highly restrictive application. In this paper, we have proposed an efficient hardware serial architecture that uses 16 bits for data path encryption. It uses fewer FPGA resources and achieves higher throughput compared to other existing hardware applications.

Lightweight cryptography is a novel diversion from conventional cryptography that targets internet-of-things (IoT) platform due to resource constraints. In comparison, it offers smaller cryptographic primitives such as shorter key sizes, block sizes and lesser energy drainage. The main focus can be seen in algorithm developments in this emerging subject. Thus, verification is carried out based upon theoretical (mathematical) proofs mostly. Among the few available side-channel analysis studies found in literature, the highest percentage is taken by power attacks. PRESENT is a promising lightweight block cipher to be included in IoT devices in the near future. Thus, the emphasis of this paper is on lightweight cryptology, and our investigation shows unavailability of a correlation electromagnetic analysis (CEMA) of it. Hence, in an effort to fill in this research gap, we opted to investigate the capabilities of CEMA against the PRESENT algorithm. This work aims to determine the probability of secret key leakage with a minimum number of electromagnetic (EM) waveforms possible. The process initially started from a simple EM analysis (SEMA) and gradually enhanced up to a CEMA. This paper presents our methodology in attack modelling, current results that indicate a probability of leaking seven bytes of the key and upcoming plans for optimisation. In addition, introductions to lightweight cryptanalysis and theories of EMA are also included.

In the context of the Internet of Things (IoT), lightweight block ciphers are of vital importance. Due to the nature of the devices involved, traditional security solutions can add overhead and perhaps inhibit the application's objective due to resource limits. Lightweight cryptography is a novel suite of ciphers that aims to provide hardware-constrained devices with a high level of security while maintaining a low physical cost and high performance. In this paper, we are going to evaluate the performance of some of the recently proposed lightweight block ciphers (GIFT-COFB, Romulus, and TinyJAMBU) on the Arduino Due. We analyze data on each algorithm's performance using four metrics: average encryption and decryption execution time; throughput; power consumption; and memory utilization. Among our chosen ciphers, we find that TinyJAMBU and GIFT-COFB are excellent choices for resource-constrained IoT devices.

As information and communication technologies evolve every day, so does the use of technology in our daily lives. Along with our increasing dependence on digital information assets, security vulnerabilities are becoming more and more apparent. Passwords are a critical component of secure access to digital systems and applications. They not only prevent unauthorized access to these systems, but also distinguish the users of such systems. Research on password predictability often relies on surveys or leaked data. Therefore, there is a gap in the literature for studies that consider real data in this regard. This study investigates the password security awareness of 161 computer engineering students enrolled in a Linux-based undergraduate course at Ataturk University. The study is conducted in two phases, and in the first phase, 12 dictionaries containing also real student data are formed. In the second phase of the study, a dictionary-based brute-force attack is utilized by means of a serial and parallel version of a Bash script to crack the students’ passwords. In this respect, the /etc/shadow file of the Linux system is used as a basis to compare the hashed versions of the guessed passwords. As a result, the passwords of 23 students, accounting for 14% of the entire student group, were cracked. We believe that this is an unacceptably high prediction rate for such a group with high digital literacy. Therefore, due to this important finding of the study, we took immediate action and shared the results of the study with the instructor responsible for administering the information security course that is included in our curriculum and offered in one of the following semesters.

Random numbers are essential for communications security, as they are widely employed as secret keys and other critical parameters of cryptographic algorithms. The Linux random number generator (LRNG) is the most popular open-source software-based random number generator (RNG). The security of LRNG is influenced by the overall design, especially the quality of entropy sources. Therefore, it is necessary to assess and quantify the quality of the entropy sources which contribute the main randomness to RNGs. In this paper, we perform an empirical study on the quality of entropy sources in LRNG with Linux kernel 5.6, and provide the following two findings. We first analyze two important entropy sources: jiffies and cycles, and propose a method to predict jiffies by cycles with high accuracy. The results indicate that, the jiffies can be correctly predicted thus contain almost no entropy in the condition of knowing cycles. The other important finding is the failure of interrupt cycles during system boot. The lower bits of cycles caused by interrupts contain little entropy, which is contrary to our traditional cognition that lower bits have more entropy. We believe these findings are of great significance to improve the efficiency and security of the RNG design on software platforms.

Military networks consist of heterogeneous devices that provide soldiers with real-time terrain and mission intel-ligence. The development of next-generation Software Defined Networks (SDN)-enabled devices is enabling the modernization of traditional military networks. Commonly, traditional military networks take the trustworthiness of devices for granted. How-ever, the recent modernization of military networks introduces cyber attacks such as data and identity spoofing attacks. Hence, it is crucial to ensure the trustworthiness of network traffic to ensure the mission's outcome. This work proposes a Continuous Behavior-based Authentication (CBA) protocol that integrates network traffic analysis techniques to provide robust and efficient network management flow by separating data and control planes in SDN-enabled military networks. The evaluation of the CBA protocol aimed to measure the efficiency of the proposed protocol in realistic military networks. Furthermore, we analyze the overall network overhead of the CBA protocol and its accuracy to detect rogue network traffic data from field devices.

The use of software daily has become inevitable nowadays. Almost all everyday tools and the most different areas (e.g., medicine or telecommunications) are dependent on software. The C programming language is one of the most used languages for software development, such as operating systems, drivers, embedded systems, and industrial products. Even with the appearance of new languages, it remains one of the most used [1] . At the same time, C lacks verification mechanisms, like array boundaries, leaving the entire responsibility to the developer for the correct management of memory and resources. These weaknesses are at the root of buffer overflows (BO) vulnerabilities, which range the first place in the CWE’s top 25 of the most dangerous weaknesses [2] . The exploitation of BO when existing in critical safety systems, such as railways and autonomous cars, can have catastrophic effects for manufacturers or endanger human lives.

This paper examines audio-based social networking platforms and how their environments can affect the persistence of fake news and mis/disinformation in the whole information ecosystem. This is performed through an exploration of their features and how they compare to that of general-purpose multimodal platforms. A case study on Spotify and its recent issue on free speech and misinformation is the application area of this paper. As a supplementary, a demographic analysis of the current statistics of podcast streamers is outlined to give an overview of the target audience of possible deception attacks in the future. As for the conclusion, this paper confers a recommendation to policymakers and experts in preparing for future mis-affordance of the features in social environments that may unintentionally give the agents of mis/disinformation prowess to create and sow discord and deception.

Software-Defined Networking (SDN) can be a good option to support Industry 4.0 (4IR) and 5G wireless networks. SDN can also be a secure networking solution that improves the security, capability, and programmability in the networks. In this paper, we present and analyze an SDN-based security architecture for 4IR with 5G. SDN is used for increasing the level of security and reliability of the network by suitably dividing the whole network into data, control, and applications planes. The SDN control layer plays a beneficial role in 4IR with 5G scenarios by managing the data flow properly. We also evaluate the performance of the proposed architecture in terms of key parameters such as data transmission rate and response time.

Software-Defined Networking (SDN) technique is presented in this paper to manage the Naval Supervisory Control and Data Acquisition (SCADA) network for equipping the network with the function of reconfiguration and scalability. The programmable nature of SDN enables a programmable Modular Topology Generator (MTG), which provides an extensive control over the network’s internal connectivity and traffic control. Specifically, two functions of MTG are developed and examined in this paper, namely linkHosts and linkSwitches. These functions are able to place the network into three different states, i.e., fully connected, fully disconnected, and partially connected. Therefore, it provides extensive security benefits and allows network administrators to dynamically reconfigure the network and adjust settings according to the network’s needs. Extensive tests on Mininet have demonstrated the effectiveness of SDN for enabling the reconfigurable and scalable Naval SCADA network. Therefore, it provides a potent tool to enhance the resiliency/survivability, scalability/compatibility, and security of naval SCADA networks.

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.