Biblio

In the information era, knowledge is becoming increasingly significant for all industries, especially criminal investigation that deeply relies on intelligence and strategies. Therefore, there is an urgent need for effective management and utilization of criminal investigation knowledge. As an important branch of knowledge engineering, the expert system can simulate the thinking pattern of an expert, proposing strategies and solutions based on the knowledge stored in the knowledge base. A crucial step in building the expert system is to construct the knowledge base, which determines the function and capability of the expert system. This paper establishes a practical knowledge base for criminal investigation, combining the technologies of cloud computing with traditional method of manual entry to acquire and process knowledge. The knowledge base covers data information and expert knowledge with detailed classification of rules and cases, providing answers through comparison and reasoning. The knowledge becomes more accurate and reliable after repeated inspection and verification by human experts.

Highly secure devices are often isolated from the Internet or other public networks due to the confidential information they process. This level of isolation is referred to as an ’air-gap .’In this paper, we present a new technique named ETHERLED, allowing attackers to leak data from air-gapped networked devices such as PCs, printers, network cameras, embedded controllers, and servers. Networked devices have an integrated network interface controller (NIC) that includes status and activity indicator LEDs. We show that malware installed on the device can control the status LEDs by blinking and alternating colors, using documented methods or undocumented firmware commands. Information can be encoded via simple encoding such as Morse code and modulated over these optical signals. An attacker can intercept and decode these signals from tens to hundreds of meters away. We show an evaluation and discuss defensive and preventive countermeasures for this exfiltration attack.

Obfuscation refers to changing the structure of code in a way that original semantics can be hidden. These techniques are often used by application developers for code hardening but it has been found that obfuscation techniques are widely used by malware developers in order to hide the work flow and semantics of malicious code. Class Encryption, Code Re-Ordering, Junk Code insertion and Control Flow modifications are Code Obfuscation techniques. In these techniques, code of the application is changed. These techniques change the signature of the application and also affect the systems that use sequence of instructions in order to detect maliciousness of an application. In this paper an ’Opcode sequence’ based detection system is designed and tested against obfuscated samples. It has been found that the system works efficiently for the detection of non obfuscated samples but the performance is effected significantly against obfuscated samples. The study tests different code obfuscation schemes and reports the effect of each on sequential opcode based analytic system.

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).

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.

The range of text analysis methods in the field of natural language processing (NLP) has become more and more extensive thanks to the increasing computational resources of the 21st century. As a result, many deep learning-based solutions have been proposed for the purpose of authorship attribution, as they offer more flexibility and automated feature extraction compared to traditional statistical methods. A number of solutions have appeared for the attribution of English texts, however, the number of methods designed for Hungarian language is extremely small. Hungarian is a morphologically rich language, sentence formation is flexible and the alphabet is different from other languages. Furthermore, a language specific POS tagger, pretrained word embeddings, dependency parser, etc. are required. As a result, methods designed for other languages cannot be directly applied on Hungarian texts. In this paper, we review deep learning-based authorship attribution methods for English texts and offer techniques for the adaptation of these solutions to Hungarian language. As a part of the paper, we collected a new dataset consisting of Hungarian literary works of 15 authors. In addition, we extensively evaluate the implemented methods on the new dataset.

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 digital certificate is by far the most widely used artifact to establish secure electronic communication over the Internet. It certifies to its user that the public key encapsulated in it is associated with the subject of the certificate. A Public Key Infrastructure (PKI) is responsible to create, store, distribute, and revoke digital certificates. To establish a secure communication channel two unfamiliar entities rely on a common certificate issuer (a part of PKI) that vouches for both entities' certificates - thus authenticating each other via public keys listed in each other's certificates. Therefore, PKIs act as a trusted third party for two previously unfamiliar entities. Certificates are static data structures, their revocation status must be checked before usage; this step inadvertently involves a PKI for every secure channel establishment - leading to privacy violations of relying parties. As PKIs act as trust anchors for their subjects, any inadvertent event or malfeasance in PKI setup breaches the trust relationship leading to identity theft. Alternative PKI trust models, like PGP and SPKI, have been proposed but with limited deployment. With several retrofitting amendments to the prevalent X.509 standard, the standard has been serving its core objective of entity authentication but with modern requirements of contextual authentication, it is falling short to accommodate the evolving requirements. With the advent of blockchain as a trust management protocol, the time has come to rethink flexible alternatives to PKI core functionality; keeping in mind the modern-day requirements of contextual authentication-cum-authorization, weighted trust anchors, privacy-preservation, usability, and cost-efficient key management. In this paper, we assess this technology's complementary role in modern-day evolving security requirements. We discuss the feasibility of re-engineering PKIs with the help of blockchains, and identity networks.

The paper considers the important problem of information protection in complex energy systems. The expert assessment of information protection in complex energy systems method has been developed. Based on the conducted research and data processing, a method of forming the analytical basis for decision-making aimed at ensuring the competitiveness of complex information protection systems has been developed.

Insider threats are steadily increasing, and the damage is also enormous. To prevent insider threats, security solutions, such as DLP, SIEM, etc., are being steadily developed. However, they have limitations due to the high rate of false positives. In this paper, we propose a data analysis method and methodology for responding to a technology leak incident. The future study may be performed based on the proposed methodology.

With novel 3D imaging technology based on computed tomography (CT) set to replace the current 2D X-ray systems, airports face the challenge of adequately preparing airport security officers (screeners) through knowledge building. Virtual reality (VR) bears the potential to greatly facilitate this process by allowing learners to experience and engage in immersive virtual scenarios as if they were real. However, while general aspects of immersion have been explored frequently, less is known about the benefits of immersive technology for instructional purposes in practical settings such as airport security.In the present study, we evaluated how different display technologies (2D vs VR) and segmentation (system-paced vs learner-paced) affected screeners' objective and subjective knowledge gain, cognitive load, as well as aspects of motivation and technology acceptance. By employing a 2 x 2 between-subjects design, four experimental groups experienced uniform learning material featuring information about 3D CT technology and its application in airport security: 2D system-paced, 2D learner-paced, VR system-paced, and VR learner-paced. The instructional material was presented as an 11 min multimedia lesson featuring words (i.e., narration, onscreen text) and pictures in dynamic form (i.e., video, animation). Participants of the learner-paced groups were prompted to initialize the next section of the multimedia lesson by pressing a virtual button after short segments of information. Additionally, a control group experiencing no instructional content was included to evaluate the effectiveness of the instructional material. The data was collected at an international airport with screeners having no prior 3D CT experience (n=162).The results show main effects on segmentation for objective learning outcomes (favoring system-paced), germane cognitive load on display technology (supporting 2D). These results contradict the expected benefits of VR and segmentation, respectively. Overall, the present study offers valuable insight on how to implement instructional material for a practical setting.

Facial is the most dynamic part of the human body that conveys information about emotions. The level of diversity in facial geometry and facial look makes it possible to detect various human expressions. To be able to differentiate among numerous facial expressions of emotion, it is crucial to identify the classes of facial expressions. The methodology used in this article is based on convolutional neural networks (CNN). In this paper Deep Learning CNN is used to examine Alex net architectures. Improvements were achieved by applying the transfer learning approach and modifying the fully connected layer with the Support Vector Machine(SVM) classifier. The system succeeded by achieving satisfactory results on icv-the MEFED dataset. Improved models achieved around 64.29 %of recognition rates for the classification of the selected expressions. The results obtained are acceptable and comparable to the relevant systems in the literature provide ideas a background for further improvements.

Big Data is a concept used in various sectors today, including the government sector in the Smart Government initiative. With a large amount of structured and unstructured data being managed, information assurance becomes important in adopting Big Data. However, so far, no research has focused on information assurance for Big Data. This paper identified information assurance factors for Big Data. This research used the systematic snapshot mapping approach to examine factors relating to information assurance from the literature related to Big Data from 2011 through 2021. The data extraction process in gathering 15 relevant papers. The findings revealed ten factors influencing the information assurance implementation for Big Data, with the security factor becoming the most concentrated factor with 18 sub-factors. The findings are expected to serve as a foundation for adopting information assurance for Big Data to develop an information assurance framework for Smart Government.

Ethical bias in machine learning models has become a matter of concern in the software engineering community. Most of the prior software engineering works concentrated on finding ethical bias in models rather than fixing it. After finding bias, the next step is mitigation. Prior researchers mainly tried to use supervised approaches to achieve fairness. However, in the real world, getting data with trustworthy ground truth is challenging and also ground truth can contain human bias. Semi-supervised learning is a technique where, incrementally, labeled data is used to generate pseudo-labels for the rest of data (and then all that data is used for model training). In this work, we apply four popular semi-supervised techniques as pseudo-labelers to create fair classification models. Our framework, Fair-SSL, takes a very small amount (10%) of labeled data as input and generates pseudo-labels for the unlabeled data. We then synthetically generate new data points to balance the training data based on class and protected attribute as proposed by Chakraborty et al. in FSE 2021. Finally, classification model is trained on the balanced pseudo-labeled data and validated on test data. After experimenting on ten datasets and three learners, we find that Fair-SSL achieves similar performance as three state-of-the-art bias mitigation algorithms. That said, the clear advantage of Fair-SSL is that it requires only 10% of the labeled training data. To the best of our knowledge, this is the first SE work where semi-supervised techniques are used to fight against ethical bias in SE ML models. To facilitate open science and replication, all our source code and datasets are publicly available at https://github.com/joymallyac/FairSSL. CCS CONCEPTS • Software and its engineering → Software creation and management; • Computing methodologies → Machine learning. ACM Reference Format: Joymallya Chakraborty, Suvodeep Majumder, and Huy Tu. 2022. Fair-SSL: Building fair ML Software with less data. In International Workshop on Equitable Data and Technology (FairWare ‘22), May 9, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3524491.3527305

The architecture and functioning of the electricity markets are rapidly evolving in favour of solutions based on real-time data sharing and decentralised, distributed, renewable energy generation. Peer-to-peer (P2P) energy markets allow two individuals to transact with one another without the need of intermediaries, reducing the load on the power grid during peak hours. However, such a P2P energy market is prone to various cyber attacks. Blockchain technology has been proposed to implement P2P energy trading to support this change. One of the most crucial components of blockchain technology in energy trading is the consensus mechanism. It determines the effectiveness and security of the blockchain for energy trading. However, most of the consensus used in energy trading today are traditional consensus such as Proof-of-Work (PoW) and Practical Byzantine Fault Tolerance (PBFT). These traditional mechanisms cannot be directly adopted in P2P energy trading due to their huge computational power, low throughput, and high latency. Therefore, we propose the Block Alliance Consensus (BAC) mechanism based on Hashgraph. In a massive P2P energy trading network, BAC can keep Hashgraph's throughput while resisting Sybil attacks and supporting the addition and deletion of energy participants. The high efficiency and security of BAC and the blockchain-based energy trading platform are verified through experiments: our improved BAC has an average throughput that is 2.56 times more than regular BFT, 5 times greater than PoW, and 30% greater than the original BAC. The improved BAC has an average latency that is 41% less than BAC and 81% less than original BFT. Our energy trading blockchain (ETB)'s READ performance can achieve the most outstanding throughput of 1192 tps at a workload of 1200 tps, while WRITE can achieve 682 tps at a workload of 800 tps with a success rate of 95% and 0.18 seconds of latency.

Nowadays, data is essential in several fields, such as science, finance, medicine, and transportation, which means its value continues to rise. Relational databases are vulnerable to copyright threats when transmitted and shared as a carrier of data. The watermarking technique is seen as a partial solution to the problem of securing copyright ownership. However, most of them are currently restricted to numerical attributes in relational databases, limiting their versatility. Furthermore, they modify the source data to a large extent, failing to keep the characteristics of the original database, and they are susceptible to solid malicious attacks. This paper proposes a new robust reversible watermarking technique, Fields Based Inserting Position Tuples algorithm (FBIPT), for relational databases. FBIPT does not modify the original database directly; instead, it inserts some position tuples based on three Fields―Group Field, Feature Field, and Control Field. Field information can be calculated by numeric attributes and any attribute that can be transformed into binary bits. FBIPT technique retains all the characteristics of the source database, and experimental results prove the effectiveness of FBIPT and show its highly robust performance compared to state-of-the-art watermarking schemes.

Homomorphic encryption (HE) facilitates computing over encrypted data without using the secret keys. It is currently inefficient for practical implementation on the Internet of Things (IoT). However, the performance of these HE schemes may increase with optimized libraries and hardware capabilities. Thus, implementing and analyzing HE schemes and protocols on resource-constrained devices is essential to deriving optimized and secure schemes. This paper develops an energy profiling framework for homomorphic encryption on IoT devices. In particular, we analyze energy consumption and performance such as CPU and Memory utilization and execution time of numerous HE schemes using SEAL and HElib libraries on the Raspberry Pi 4 hardware platform and study energy-performance-security trade-offs. Our analysis reveals that HE schemes can incur a maximum of 70.07% in terms of energy consumption among the libraries. Finally, we provide guidelines for optimization of Homomorphic Encryption by leveraging multi-threading and edge computing capabilities for IoT applications. The insights obtained from this study can be used to develop secure and resource-constrained implementation of Homomorphic encryption depending on the needs of IoT applications.

In recent years, the security of AI systems has drawn increasing research attention, especially in the medical imaging realm. To develop a secure medical image analysis (MIA) system, it is a must to study possible backdoor attacks (BAs), which can embed hidden malicious behaviors into the system. However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e.g., X-Ray, CT, and MRI) and analysis tasks (e.g., classification, detection, and segmentation). Most existing BA methods are designed to attack natural image classification models, which apply spatial triggers to training images and inevitably corrupt the semantics of poisoned pixels, leading to the failures of attacking dense prediction models. To address this issue, we propose a novel Frequency-Injection based Backdoor Attack method (FIBA) that is capable of delivering attacks in various MIA tasks. Specifically, FIBA leverages a trigger function in the frequency domain that can inject the low-frequency information of a trigger image into the poisoned image by linearly combining the spectral amplitude of both images. Since it preserves the semantics of the poisoned image pixels, FIBA can perform attacks on both classification and dense prediction models. Experiments on three benchmarks in MIA (i.e., ISIC-2019 [4] for skin lesion classification, KiTS-19 [17] for kidney tumor segmentation, and EAD-2019 [1] for endoscopic artifact detection), validate the effectiveness of FIBA and its superiority over stateof-the-art methods in attacking MIA models and bypassing backdoor defense. Source code will be available at code.

Financial technology (Fintech) is an amalgamation of financial management using a technology system. Fintech has become a public concern because this service provides many service features to make it easier from the financial side, such as being used in cooperative financial institutions, banking and insurance. This paper will analyze the opportunities and challenges of Fintech sharia in Indonesia. By exploring the existing literature, this article will try to answer that question. This research is carried out using a literature review approach and comparative qualitative method which will determined the results of the SWOT analysis of sharia financial technology in indonesia. It is needed to mitigate risk of funding in a peer to peer method in overcoming the security of funds and data from investors, firstly companies can perform transparency on the clarity of investor funds. This is done as one of the facilities provided to investors in the Fintech application. In the future, it is hoped that in facing competition, sharia-based fintech companies must be able to provide targeted services through the socialization of sharia fintech to the public, both online and offline. Investors are expected to be more careful before investing in choosing Fintech Peer to Peer (P2P) Lending services by checking the list of Fintech lending and lending companies registered and found by the Financial Services Authority (OJK).

The rapid complexity growth of electronic systems nowadays increases their vulnerability to hacking, such as fault injection, including insertion of glitches into the system clock to corrupt internal state through timing errors. As a countermeasure, a frequency locked loop (FLL) based clock glitch detector is proposed in this paper. Regulated from an external supply voltage, this FLL locks at 16-36X of the system clock, creating four phases to measure the system clock by oversampling at 64-144X. The samples are then used to sense the frequency and close the frequency locked loop, as well as to detect glitches through pattern matching. Implemented in a 5nm FINFET process, it can detect the glitches or pulse width variations down to 3.125% of the input 40MHz clock cycle with the supply varying from 0.5 to 1.0V.

The popularity of portable web browsers is increasing due to its convenient and compact nature along with the benefit of the data being stored and transferred easily using a USB drive. As technology gets updated frequently, developers are working on web browsers that can be portable in nature with additional security features like private mode browsing, built in ad blockers etc. The increased probability of using portable web browsers for carrying out nefarious activities is a result of cybercriminals with the thought that if they use portable web browsers in private mode it won't leave a digital footprint. Hence, the research paper aims at performing a comparative study of four portable web browsers namely Brave, TOR, Vivaldi, and Maxthon along with various memory acquisition tools to understand the quantity and quality of the data that can be recovered from the memory dump in two different conditions that is when the browser tabs were open and when the browser tabs were closed in a system to aid the forensic investigators.

Modern connected vehicles are equipped with a large number of sensors, which enable a wide range of services that can improve overall traffic safety and efficiency. However, remote access to connected vehicles also introduces new security issues affecting both inter and intra-vehicle communications. In fact, existing intra-vehicle communication systems, such as Controller Area Network (CAN), lack security features, such as encryption and secure authentication for Electronic Control Units (ECUs). Instead, Original Equipment Manufacturers (OEMs) seek security through obscurity by keeping secret the proprietary format with which they encode the information. Recently, it has been shown that the reuse of CAN frame IDs can be exploited to perform CAN bus reverse engineering without physical access to the vehicle, thus raising further security concerns in a connected environment. This work investigates whether anonymizing the frames of each newly released vehicle is sufficient to prevent CAN bus reverse engineering based on frame ID matching. The results show that, by adopting Machine Learning techniques, anonymized CAN frames can still be fingerprinted and identified in an unknown vehicle with an accuracy of up to 80 %.

The problem of privacy protection of trajectory data has received increasing attention in recent years with the significant grow in the volume of users that contribute trajectory data with rich user information. This creates serious privacy concerns as exposing an individual's privacy information may result in attacks threatening the user's safety. In this demonstration we present TP$^\textrm3$ a novel practical framework for supporting trajectory privacy preservation in Mobile Cloud Environments (MCEs). In TP$^\textrm3$, non-expert users submit their trajectories and the system is responsible to determine their privacy exposure before sharing them to data analysts in return for various benefits, e.g. better recommendations. TP$^\textrm3$ makes a number of contributions: (a) It evaluates the privacy exposure of the users utilizing various privacy operations, (b) it is latency-efficient as it implements the privacy operations as serverless functions which can scale automatically to serve an increasing number of users with low latency, and (c) it is practical and cost-efficient as it exploits the serverless model to adapt to the demands of the users with low operational costs for the service provider. Finally, TP$^\textrm3$'s Web-UI provides insights to the service provider regarding the performance and the respective revenue from the service usage, while enabling the user to submit the trajectories with recommended preferences of privacy.

A malicious insider threat is more vulnerable to an organization. It is necessary to detect the malicious insider because of its huge impact to an organization. The occurrence of a malicious insider threat is less but quite destructive. So, the major focus of this paper is to detect the malicious insider threat in an organization. The traditional insider threat detection algorithm is not suitable for real time insider threat detection. A supervised learning-based anomaly detection technique is used to classify, predict and detect the malicious and non-malicious activity based on highest level of anomaly score. In this paper, a framework is proposed to detect the malicious insider threat using supervised learning-based anomaly detection. It is used to detect the malicious insider threat activity using One-Class Support Vector Machine (OCSVM). The experimental results shows that the proposed framework using OCSVM performs well and detects the malicious insider who obtain huge anomaly score than a normal user.

As an important branch of computer forensics, network forensics technology, whether abroad or at home, is in its infancy. It mainly focuses on the research on the framework of some forensics systems or some local problems, and has not formed a systematic theory, method and system. In order to improve the network forensics sys-tem, have a relatively stable and correct model for refer-ence, ensure the authenticity and credibility of network fo-rensics from the forensics steps, provide professional and non professional personnel with a standard to measure the availability of computer network crime investigation, guide the current network forensics process, and promote the gradual maturity of network forensics theories and methods, This paper presents a fuzzy decision tree reason-ing method for network forensics analysis.