Malicious Intent Research Articles

Harm Imbrication and Virtualised Violence: Reconceptualising the Harms of Doxxing

This article develops a framework for analysing the harms of doxxing: the practice of publishing personal identifying information about someone on the internet, usually with malicious intent. Doxxing is not just a breach of privacy, nor are its effects limited to first‑order harms to an individual’s bodily integrity. Rather, doxxing increases the spectre of second-order harms to an individual’s security interests. To better understand these harms—and the relationships between them—we draw together the theories of Bhaskar, Deleuze and Levi to develop two concepts: the virtualisation of violence and harm imbrication. The virtualisation of violence captures how, when concretised into structures, the potential for harm can be virtualised through language, writing and digitisation. We show that doxxed information virtualises violence through constituting harm-generating structures and we analyse how the virtual harm-generating potential of these structures is actualised through first- and second-order harms against a doxxing victim. The concept of harm imbrication, by contrast, helps us to analyse the often-imbricated and supervenient relationship between harms. In doing so, it helps us explain the emergent – and supervenient – relationship between doxxing’s first- and second-order harms.

Rancang Bangun Kunci Pintu Rumah Berbasis Internet of Things (IoT)

One of the problems of theft of goods at home is often caused by our own negligence as homeowners who forget to lock the door of the house when traveling. In addition to the problem of failing to lock the door which makes unknown people enter the house. Forgetting to put the key or worse, we could lose the key to our house, and the key could be found by someone with malicious intent and on us or our family. By utilizing the Internet of Things (IoT), a device is developed that can open and lock the door of the house, so that homeowners no longer have to worry about forgetting to lock the door. Using Nodemcu esp8266 which is already equipped with a wifi module to be able to connect to the internet, and can be controlled via an application on a smartphone via the internet

Achieving data privacy for decision support systems in times of massive data sharing.

The world is suffering from a new pandemic of Covid-19 that is affecting human lives. The collection of records for Covid-19 patients is necessary to tackle that situation. The decision support systems (DSS) are used to gather that records. The researchers access the patient’s data through DSS and perform predictions on the severity and effect of the Covid-19 disease; in contrast, unauthorized users can also access the data for malicious purposes. For that reason, it is a challenging task to protect Covid-19 patient data. In this paper, we proposed a new technique for protecting Covid-19 patients’ data. The proposed model consists of two folds. Firstly, Blowfish encryption uses to encrypt the identity attributes. Secondly, it uses Pseudonymization to mask identity and quasi-attributes, then all the data links with one another, such as the encrypted, masked, sensitive, and non-sensitive attributes. In this way, the data becomes more secure from unauthorized access.

Tolkien, Thompson, English Modernity, and the Left

Tolkien, Thompson, English Modernity, and the Left Ewan Cameron (bio) Yet such is oft the course of deeds that move the wheels of the world: small hands do them because they must, while the eyes of the great are elsewhere. —J.R.R. Tolkien The working class did not rise like the sun at an appointed time. It was present at its own making. —E. P. Thompson, The Making of the English Working Class There has been something of a desire by those of all ideologies to claim Tolkien for their own cause, and while this essay may follow similar lines, the attempt is not to say that Tolkien was left wing: such a statement would be anachronistic and false. Rather, this is an argument for Tolkien as a resource for those on the left, especially those of an anti-authoritarian stance. In particular, I look at the work of the British historian and socialist humanist E. P. Thompson as a figure whose projects and outlooks have significant intersections with Tolkien's work. Tolkien: Reactionary? To draw links between Tolkien and the left may appear strange to some. After all, Tolkien's biographer, Humphrey Carpenter, wrote that, in being a monarchist who was not exactly enamoured with democracy, Tolkien was right wing, albeit "in modern jargon" (Bio 128). There is also a charge that a reactionary mindset pervades Tolkien's work in both his depiction of political structures such as monarchy and also the undeniable racial element to his work. Perhaps the worst example of this is his description of the orcs, which ventures into the language of racial othering (Fimi, "Was Tolkien Really Racist?"). Born into Victorian society, Tolkien would almost inevitably inherit a racialized worldview that bled into his writings, yet his understanding [End Page 159] of race is perhaps best considered more as a mix of "confusion and vagueness" than any malicious intent (Fimi, Race and Cultural History 157). As he grew older, especially during the rise of Nazism in the 1930s, Tolkien was outspoken against racial chauvinism as it manifested in the world (Letters 37–38). He began to take pains to distance his own work from the appropriation of racists, noting that words such as "Nordic" had become associated with racist theories which he wanted nothing to do with (Letters 55–56). Despite his personal repudiation, the works of Tolkien continue to be claimed by far-right white supremacist groups (Martinez). Tolkien's treatment of race in his work is certainly problematic and it is not something that can or should be ignored. The racially coded language is regrettable and the cultural eurocentrism feels outdated. Nevertheless, our acknowledgement of Tolkien's imperfection, that he did not always live up to the high standards of some of his characters and themes, does not preclude us from finding meaningful value in the majority of his work and themes (Sisto and Marchese). Is it fair to say that Tolkien was a conservative of a reactionary nature, one whose creations appeared at the intersection of nostalgia and nationalism? For Raymond Williams, Tolkien belonged to a class of writers whose rural fantasies "scribbled over" the "real land and its people" (Williams, Country and the City 258). Fred Inglis would go even further, drawing fascist equivalences: for instance, "instead of Nuremberg, Frodo's farewell" (Inglis 40). Societies have claimed and reworked idioms of the past into the national myths of the present to justify a thoroughly modern nationalism (Hobsbawm 6), and Tolkien's conservatism is most prominent in his idealization of certain political formations, especially that of bloodline kingship. In The Lord of the Rings, this is taken to a somewhat absurd extent, with Aragorn taking his "rightful" place as king (RK, V, viii, 136) after a thousand years of his family's absence, somewhat equivalent to the descendants of Harold Godwinson showing up in the twenty-first century United Kingdom and attempting to eject the Windsors from Buckingham Palace. Tolkien also mostly favors aristocratic characters in his narratives, with the majority of speaking characters, whether they are human, dwarf, elf, or hobbit, being renowned for their elite lineage. Sam is a notable exception to this, of course, and...

Utilization Strategy of User Engagements in Korean Fake News Detection

Fake News (disinformation with malicious intent) has emerged as a major social problem. To address this issue, previous studies mainly utilized single information, the news content, to detect fake news. However, using only news content in training is insufficient. Moreover, most studies did not consider the propagation aspect of fake news as a training feature. Thus, in an attempt to incorporate the ability to learn representation based on textual information and social context, this study proposed a fake news detection algorithm that thoroughly utilizes user graph in Korean fake news and dataset construction methods. In addition, a training strategy was proposed for utilizing user graph in Korean fake news detection through comparative and ablation studies. The experimental results showed that K-FANG outperformed the baseline in detecting fake news. Moreover, user engagements were found to be useful for detecting fake news even if the data contained hate speech. Finally, the validity of using stance information by expanding its class and controlling the class imbalance issues was also verified. This study provided useful implications for utilizing user information in fake news detection.

The concept of female crime and its specific features

The article discusses the concept of female crime, the differences between female and male crime and its specific features. In the structure of female crime, personal qualities and crimes committed with malicious intent are distinguished, as well as demographic, psychological and ideological aspects. Criminological aspects of female crime have been studied. The opinions of leading national and foreign scholars were also analyzed.

Network event-based model using finite state machine to detect and predict insider intrusion on enterprise networks

Network infrastructure being the backbone of organisations, has become the preferred target for attackers. Hackers pose threat to the network architecture and target the data in storage and in transit mode. In addition to this, insiders with malicious intentions also try to exploit access, therefore organisations need to detect and deal with such attempts. In this paper, we propose a finite state machine-based attack probability detection model to detect insider attacks on enterprise network. The model consists of a knowledge-based finite state machine and a probability-based IPAM algorithm that gives high detection rates and less false positives. The approach uses a novel composition wherein the event sequences are generated for each pair of connected hosts using the state machine and then fed to probability component for further analysis and ascertaining the network safety level. The proposed approach defends the denial of service attacks and scanning probes. The model works in the lower layers of the network protocol stack and hence the detection characteristics can be easily extended to protect against new attacks.

Взрыв 1 августа 1918 г. в Омске

The aim of the work is to analyze the causes and consequences of a tragic incident - an explosion of ammunition in Omsk at the cargo yard of the city railway station on August 1, 1918. The incident is first studied in historiography. The following objectives are reached in the study. Firstly, the complex of sources that give an idea of the event were studied. Secondly, the factors and actions of specific individuals and forces that led to the tragedy and influenced the course of the investigation of its causes were identified. Thirdly, the research aspects were determined that emerged due to the anthropological study of the event regarding the situation of the Civil War. For the preparation of the work, mainly unpublished sources, stored in the Historical Archive of Omsk Oblast, were used. These are the protocols of interrogation of witnesses and materials of office work, which are part of the investigation file in the fund of the Omsk District Court, and the records of the victims of the explosion, made in the registers of the Orthodox churches of Omsk, available in the fund of the Omsk Spiritual Consistory. Memoirs, periodicals, and photographic documents were also used. The connection between the general historiography of the topic and the issue under study is described. The anthropological approach combined with the comparative historical and problematic chronological methods formed the theoretical basis of the work. Particular attention in the publication is paid to an attempt to a “politicized use” of the tragedy by representatives of the right-wing conservative public, who saw the cause of the incident, like all Russia's failures, as a “German-Bolshevik trace”. In conclusion, the author states the inconsistency of the initial version of the investigation about the malicious intent of certain political forces. The inquest recognized the cause of the tragedy as criminal negligence, initiating the closure of the case. This was also facilitated by the changed political conjuncture of the anti-Bolshevik Omsk, which perceived the incident as a local one. In the case under consideration, the anthropological vision of the past orients us towards comprehending the circumstances of the military and political everyday life of the Civil War, the psychology of the tragedy's contemporaries who lived in the militarized society, the influence of the phenomena of captivity and the human factor. The work is intended for a wide range of readers, including specialists who study military history, military anthropology, the Civil War in Russia and Siberian everyday life in this period.

Secure Inter-Domain Routing for Resisting Unknown Attacker in Internet-of-Things

With an increasing adoption of Internet-of-Things (IoT) over massively connected device, there is a raising security concern. Review of existing security schemes in IoT shows that there is a significant trade-off due to non-adoption of inter-domain routing scheme over larger domain of heterogeneous nodes in an Internet of Things (IoT) via gateway nodes. Hence, the purpose of the proposed study is to bridge this trade-off by adopting a new security scheme that works over an inter-domain routing without any apriori information of an attacker. The goal of the proposed framework is to identify the malicious intention of attacker by evaluating their increasing attention over different types of hop links information. Upon identification, the framework also aims for resisting attacker node to participate in IoT environment by advertising the counterfeited route information with a target of misleading the attackers promoting autonomous self-isolation. The study outcome shows proposed scheme is secure compared to existing scheme.

Les monuments dans l’espace public camerounais : pour embellir, distraire ou restituer l’histoire vraie ?

Focusing on monuments, their purpose, and their relationship with the populations, this text highlights some aspects of Cameroon’s history that were factors in the process of creation of monuments. The author provides a classification of the Cameroonian monuments by categorizing them into “monuments of commemoration” and “monuments of celebration” of great figures of the country’s history. However, these monuments seem to honor the colonial memory to the detriment of the national memory and silence the names of Cameroonians who have particularly contributed to the process of evolution of their country. It seems that the state authorities are allergic to questions of memory and history, with a malicious intention to manipulate and distract the conscience: the monuments are dilapidated, damaged, with dirty surroundings, invaded by mentally deficient people who have taken up residence there. The relationship between these buildings and the population is not a tender one: while some perceive them as mere decorative objects, symbolizing nothing to them, others such as political activists are determined to dismantle them because they consider them fake. All of this seems to demonstrate that Cameroonian monuments do little to celebrate significant parts of the country’s history, hence the numerous controversies and revolts against them.

Formal modelling and verification of high interactive honeypot using coloured Petri nets

Honeypot is an active defence mechanism intended to mimic a computer system concealing its identity to misguide attackers. The mechanism traps an attacker and collects intrusion information as they trespass a network environment and cause a menace for their interest. The paper proposes a honeypot tool with deadlock and livelock states to strengthen the defence mechanism and engage the attacker for a longer period. The proposed work aims to present the formal analysis of a honeypot using coloured petri nets tool. The three core components of the honeypot, such as data capture, control and collection, are included in the formal modelling to study the behavioural properties of a honeypot in a deadlock and livelock state. The main objective is to emphasise the working of high interaction honeypot in deadlock or livelock states under an attack surface. The honeypot's formal model verification using a state-space tool in coloured petri net determines that an attacker wedged in a deadlock or livelock state fails to navigate further to fulfil malicious intent, thereby deceiving an attacker for a longer period.

How Practical Are Fault Injection Attacks, Really?

Fault injection attacks (FIA) are a class of active physical attacks, mostly used for malicious purposes such as extraction of cryptographic keys, privilege escalation, attacks on neural network implementations. There are many techniques that can be used to cause the faults in integrated circuits, many of them coming from the area of failure analysis. In this paper we tackle the topic of practicality of FIA. We analyze the most commonly used techniques that can be found in the literature, such as voltage/clock glitching, electromagnetic pulses, lasers, and Rowhammer attacks. To summarize, FIA can be mounted on most commonly used architectures from ARM, Intel, AMD, by utilizing injection devices that are often below the thousand dollar mark. Therefore, we believe these attacks can be considered practical in many scenarios, especially when the attacker can physically access the target device.

Source Printer Identification with Microscopic Printing using Deep Learning

Due to the advent of digital technologies, it has become much easier to falsify printed documents for malicious purposes. Therefore, it is essential to research and develop efficient algorithms to distinguish authentic printed documents from fakes. A source printer identification technique is one of the methods to trace the source of the documents, thereby protecting the reliability and integrity of printed documents. This paper proposes a method to identify the source printers using Deep Learning approach. Particularly, we utilise a Convolutional Neural Network for features extraction from printed microscopic patterns of different printing sources. These extracted features are fed into a multi-class Support Vector Machine and Random Forest classification for the source identification. The actual printed dot patterns from common printing technologies such as conventional offset, waterless offset, electrophotographic are used to evaluate the effectiveness of our proposed approach. This study highlights the fact that printed patterns in gray images and features based on Convolutional Neural Network extraction help to reserve much more distinguishable characteristics of printing sources. The hybrid classification algorithms outperform the previous studies using hand-extracted features, with more than 99 % of samples correctly classified in the test set.

Ensemble dynamic behavior detection method for adversarial malware

Behavior-based malware detection approaches combined with deep learning techniques are effective against unknown malware and malware variants. However, such approaches are vulnerable to adversarial attacks. Adversarial malware is carefully optimized to evade detection through embedding numerous anti-detection techniques, e.g., inserting irrelevant API calls or using API calls in loops during the program execution to mask the real malicious intentions. To address this problem, we propose a novel ensemble adversarial dynamic behavior detection method aiming at three features of malicious API sequence, namely Immediacy, Locality, and Adversary. The individual classifiers of the ensemble method follow the “excellent and diverse” principle. We conduct extensive experiments over large real benign and malicious instances and demonstrate a generic, query-efficient gray-box adversarial attack to evaluate our model. The experimental results indicate that, compared with the individual classifiers, the detection accuracy is improved by up to 2.55%∼11.34% (without anti-attack), 8.64%∼21.33% (random perturbation), and 10.07%∼21.34% (benign perturbation) respectively. To sum up, our method provides better effectiveness, generality, and resiliency in the absence of a constant re-training of the detector needed to cope with the evolution of adversarial malware.

ПРАВОВАЯ ГРАМОТНОСТЬ И ОТНОШЕНИЕ НАСЕЛЕНИЯ К ПОСМЕРТНОМУ ДОНОРСТВУ И ТРАНСПЛАНТАЦИИ ОРГАНОВ

The successful development of transplantation is not determined by the legal model, but by the positive public opinion regarding organ donation. Purpose: to analyze the attitude of the Belarusian population towards posthumous donation and organ transplantation and to identify the factors that affect it most. Material and methods: data collection was carried out through a questionnaire survey using a Google form; the sample size was 286 respondents. Data processing was carried out using the SPSS Statistics program by CHAID analysis. Results. 94.4% of the respondents have a positive attitude towards organ donation and transplantation, but only 50% of the respondents agree to the removal of their organs after death. People who have a positive attitude towards donation, as well as those who have minimal awareness and study the scientifi literature on this issue, are most inclined to do this. The main motive of people who agree to organ harvesting is the desire to save someone's life, the main motive for unwillingness to give such consent is fear of a medical error or malicious intent on the part of health workers. Conclusions. For the development of transplantation, it is necessary to create a positive public opinion regarding organ donation and transplantation and to increase the legal literacy of the population

EMERGING THREATS IN CYBER SECURITY

The growth of the Internet in the connections has led to a significant growth of cyber attack cases often with heavy outcome towards the globe. Malware is the primary choice of weapon to carry out malicious intent in the cyberspace, either by exploitation in to existing vulnerabilities or utilization of unique characteristics of emerging techniques. The development of more innovative and effective malware defense mechanisms has been regarded as an urgent requirement in the cyber security community. To assist in achieving this goal, we first present an over view of the most exploited vulnerabilities in existing hardware, software, and network layers.

Detecting attacks on IoT devices with probabilistic Bayesian neural networks and hunger games search optimization approaches

AbstractTechnologies that enable physical objects to communicate with each other are attracting a lot of interest day by day. Internet of Things (IoT) is a communication network that is used to connect with technological devices and systems. The growth of IoT networks has led to malicious targeting of these systems by third parties. To prevent such malicious purposes, network security should be at the forefront. Recently, artificial intelligence‐based approaches have been used in security domains. Two accessible datasets were used in this study. The datasets consist of simulated bot IoT network traffic data containing distributed denial‐of‐service (DDoS) attack types. In the proposed approach, probabilistic Bayesian neural networks and normal Bayesian neural networks are used as the deep learning models. All features of the dataset were trained using two models. Then, the datasets were optimized using hunger games search (HGS) method and the most efficient parameters of the datasets were selected and retrained using deep learning models. In the analysis performed using the proposed approach, an overall accuracy of 100% was obtained for the dataset used in the first experiment and 99.99% for the dataset used in the second experiment. It was also observed that metaheuristic optimization helps the proposed approach to save time in detecting DDoS attacks.

Detecting model misconducts in decentralized healthcare federated learning

BackgroundTo accelerate healthcare/genomic medicine research and facilitate quality improvement, researchers have started cross-institutional collaborations to use artificial intelligence on clinical/genomic data. However, there are real-world risks of incorrect models being submitted to the learning process, due to either unforeseen accidents or malicious intent. This may reduce the incentives for institutions to participate in the federated modeling consortium. Existing methods to deal with this “model misconduct” issue mainly focus on modifying the learning methods, and therefore are more specifically tied with the algorithm. Basic ProceduresIn this paper, we aim at solving the problem in an algorithm-agnostic way by (1) designing a simulator to generate various types of model misconduct, (2) developing a framework to detect the model misconducts, and (3) providing a generalizable approach to identify model misconducts for federated learning. We considered the following three categories: Plagiarism, Fabrication, and Falsification, and then developed a detection framework with three components: Auditing, Coefficient, and Performance detectors, with greedy parameter tuning. Main FindingsWe generated 10 types of misconducts from models learned on three datasets to evaluate our detection method. Our experiments showed high recall with low added computational cost. Our proposed detection method can best identify the misconduct on specific sites from any learning iteration, whereas it is more challenging to precisely detect misconducts for a specific site and at a specific iteration. Principal ConclusionsWe anticipate our study can support the enhancement of the integrity and reliability of federated machine learning on genomic/healthcare data.

The Nature of the Future: Agriculture, Science, and Capitalism in the Antebellum North

The Nature of the Future: Agriculture, Science, and Capitalism in the Antebellum North

MagNet: Detecting Digital Presentation Attacks on Face Recognition

Presentation attacks on face recognition systems are classified into two categories: physical and digital. While much research has focused on physical attacks such as photo, replay, and mask attacks, digital attacks such as morphing have received limited attention. With the advancements in deep learning and computer vision algorithms, several easy-to-use applications are available where with few taps/clicks, an image can be easily and seamlessly altered. Moreover, generation of synthetic images or modifying images/videos (e.g. creating deepfakes) is relatively easy and highly effective due to the tremendous improvement in generative machine learning models. Many of these techniques can be used to attack the face recognition systems. To address this potential security risk, in this research, we present a novel algorithm for digital presentation attack detection, termed as MagNet, using a “Weighted Local Magnitude Pattern” (WLMP) feature descriptor. We also present a database, termed as ID Age nder, which consists of three different subsets of swapping/morphing and neural face transformation. In contrast to existing research, which utilizes sophisticated machine learning networks for attack generation, the databases in this research are prepared using social media platforms that are readily available to everyone with and without any malicious intent. Experiments on the proposed database, FaceForensic database, GAN generated images, and real-world images/videos show the stimulating performance of the proposed algorithm. Through the extensive experiments, it is observed that the proposed algorithm not only yields lower error rates, but also provides computational efficiency.