Several state-of-the-art machine and deep learning models in the mode of adversarial training, input transformation, self adaptive training, adversarial purification, zero-shot, one- shot, and few-shot meta learning had been proposed as a possible solution to an out-of-distribution problems by applying them to wide arrays of benchmark dataset across different research domains with varying degrees of performances, but investigating their performance on previously unseen out-of- distribution malware attack remains elusive. Having evaluated the poor performances of these state-of-the-art approaches in (...) our previous research on an out-of-distribution attack. In this research, we dived deeper to understand why they works better for other domain dataset but with poor performance on available benchmark malware dataset like Malimg, Malevis, Sorel, and Avast CTU malware dataset. We explored the both the embedding and vector spaces in datasets and compare them with that from other research domain, and find a surprising wide variation between the embedding and vector spaces in malware datasets. We assert that current state-of-the-art machine and deep learning models does not address the wide variation of embedding and vector spaces which are peculiar to malware dataset, hence their poor performance on out-of-distribution attack classification, and so concluded that addressing this variation in embedding and vector spaces will bring about substantial increase in detection of previously unseen out-of-distribution attack. (shrink)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a (...) shallow neural network with maximum of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a (...) shallow neural network with maximum of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid (...) class='Hi'>malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid (...) class='Hi'>malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data (...) set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware specimens have been collected from the Mailing database for testing purposes. The experimental findings show that the suggested technique for measuring cyber security hazards in IoT has superior classification results to existing methods. Hence to detect the attack prevention in IoT process Whale with Grey wolf optimization (WGWO) and deep convolution network is used. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid (...) class='Hi'>malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)

With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid (...) class='Hi'>malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis (...) capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)

To secure computers and information systems from attackers taking advantage of vulnerabilities in the system to commit cybercrime, several methods have been proposed for real-time detection of vulnerabilities to improve security around information systems. Of all the proposed methods, machine learning had been the most effective method in securing a system with capabilities ranging from early detection of software vulnerabilities to real-time detection of ongoing compromise in a system. As there are different types of cyberattacks, each of the existing state-of-the-art (...) machine learning models depends on different algorithms for training which also impact their suitability for detection of a particular type of cyberattack. In this research, we analyzed each of the current state-of-the art machine learning models for different types of cyberattack detection from the past 10 years with a major emphasis on the most recent works for comparative study to identify the knowledge gap where work is still needed to be done with regard to detection of each category of cyberattack. (shrink)

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their vulnerabilities and (...) future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their cor- responding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model. (shrink)

E-Government refers to the administration of Information and Communication Technologies (ICT) to the procedures and functions of the government with the objective of enhancing the transparency, efficiency and participation of the citizens. E-Government is tough systems that require distribution, protection of privacy and security and collapse of these could result in social and economic costs on a large scale. Many of the available e-government systems like electronic identity system of management (eIDs), websites are established at duplicated databases and servers. An (...) established validation and management system could face a single failure point and the system is prone to Distributed Denial of Service Attacks (DDoS), denial of service attacks (DoS), malware and other cyber attacks. The execution of a privacy preserving and a secure decentralized system is enabled by the block chain technology. Here any third-party organizations do not have any control over the transactions of the Government. With the help of block chain technology, new and existing data are encapsulated within ledger or blocks, which are evenly distributed through the network in an enduring and sustainable way. The privacy and security of information are improved with the help of block chain technology, where distribution and encryption of data are performed through the total network. This analytical paper maps out the analysis of the security in the e-government system, utilizing the block chain technology that provides privacy and security of information and thereby enhancing the trust among the public sector. Qualitative and theoretical analysis is made for the proposed topic and implications of privacy and security of the proposed system is made. (shrink)

heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and (...) HeartDisease. A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached (...) an accuracy of 98.01% on the Heart Disease Dataset. (shrink)

Dalawa sa mga hibla ng Pilosopiyang Filipino ay patungkol sa paggamit ng banyagang pilosopiya at pamimilosopiya sa wikang Filipino. Makatutulong ang dalawang ito tungo sa pagsasalin ng mga banyagang kaisipan sa talastasang bayan. Ang dalawang hiblang ito ang nais na ambagan ng kasalukuyang sanaysay, sa pamamagitan ng pagsasagawa ng rebyu sa isang halimbawa ng anime/manga na Hapon: ang Attack on Titan (AOT) ni Hajime Isayama. Gagamitin sa pagbasa ng AOT ang mga pilosopikal na pananaw ng ilang Aleman/Austrianong pilosoper/sikolohista na (...) sina Sigmund Freud, Erich Fromm, Carl Schmitt, at Friedrich Nietzsche. Sa pagbasang ito sa pamamagitan ng apat na banyagang palaisip, tututukan ang tema ng kalayaan at digmaan. Liban dito, maglalatag din ng pagmumuni-muni ukol sa paksa ng pananangkapan sa kasaysayan bilang sandatang pulitikal, isang temang lantad na lantad rin sa AOT. Ang lahat ng ito ay isasagawa sa wikang Filipino, bilang pagtatangka na makapag-ambag sa pagpapasok ng anime/manga na Hapon at pilosopiyang Aleman/Austriano sa talastasang bayan. (shrink)

Why are personal attacks so powerful? In political debates, speeches, discussions and campaigns, negative character judgments, aggressive charges and charged epithets are used for different purposes. They can block the dialogue, trigger value judgments and influence decisions; they can force the interlocutor to withdraw a viewpoint or undermine his arguments. Personal attacks are not only multifaceted dialogical moves, but also complex argumentative strategies. They can be considered as premises for further arguments based on signs, generalizations or consequences. They involve tactics (...) for arousing emotions such as fear, hate or contempt, or for ridiculing the interlocutor. The twofold level of investigation presented in this paper is aimed at distinguishing the different roles that ad hominem have in a dialogue and bringing to light their hidden dimensions. The reasoning structure of each type of attack will be distinguished from the tactics used to increase its effectiveness and conceal its weaknesses. (shrink)

Liberalism is today under attack. This attack is being fought along two fronts, and so appears to be coming from different directions, but it is actually coming exclusively from the right. One source is Islamic fundamentalism, and the other is American neo-conservatism, which in turn unites elements of Christian fundamentalism with elements of neo-Platonic political philosophy and neo-Aristotelian moral theory. Both Islamic fundamentalism and American neo-conservatism are perfectionist views, and while perfectionist attacks on liberalism are nothing new, there (...) is a special danger in the position that liberalism currently finds itself. Although bitterly opposed to each other and divided by utterly incompatible substantive views, these two sources of attack share many similarities at a more fundamental level of political theory. The purpose of this paper is accordingly to focus our attention on the similarities between these seemingly opposing perfectionist views, and to defend liberalism against one of the most serious accusations that perfectionists from both camps make against it—the claim that liberalism leads to nihilism. (shrink)

McMahan argues that justification defeats liability to defensive attack (which would undermine the thesis of the "moral equality of combatants"). In response, I argue, first, that McMahan’s attempt to burden the contrary claim with counter-intuitive implications fails; second, that McMahan’s own position implies that the innocent civilians do not have a right of self-defense against justified attackers, which neither coheres with his description of the case (the justified bombers infringe the rights of the civilians) nor with his views about (...) rights forfeiture, is unsupported by independent argument, and, in any case, extremely implausible and counter-intuitive; and third, that his interpretation of the insulin case confuses the normative relations between an agent’s justification and non-liability on the one hand and permissible or impermissible interference with the agent’s act on the other. Similar confusions, fourth, affect his discussion of liability to compensation. (shrink)

The quality of our public discourse – think of the climate change debate for instance – is never very high. A day spent observing it reveals a litany of misrepresentation and error, argumentative fallacy, and a general lack of good will. In this paper I focus on a microcosmic aspect of these practices: the use of two types of argument – the argumentum ad hominem and appeal to authority – and a way in which they are related. Public debate is (...) so contaminated by the misuse of the ad hominem tactic that it is important to have an understanding of one of the ways in which its use may be legitimate. So readers who might have expected an analysis of the ad hominem fallacy will, on the contrary, be presented with an account of one of the places for its potential success. In ad hominem, arguers are viewed as legitimate constitutive targets for their own argument; in authority arguments, arguers provide a legitimate constitutive warrant for an argument they deploy. These types of argument are connected in so far as the identities of persons deploying them carry the cognitive weight of the argument. If authority arguments sometimes deserve to succeed, ad hominem attacks on them are potentially legitimate. An important motivation for this analysis is that in identifying the success conditions for one of the ad hominem types we attain some clarity in the evaluation of their use. (shrink)

Science has been under attack ever since William Blake and Romantic movement. In our time, criticisms of modern science have led to Alan Sokal's spoof, and the so-called science wars. Both sides missed the point. Science deserves to be criticized for seriously misrepresenting its highly problematic aims, which have metaphysical, value and political assumptions associated with them. Instead of repressing these problematic aims, science ought rather to make them explicit, so that they can be critically assessed and, we may (...) hope, improved. (shrink)

We present an interdisciplinary approach to study systematic relations between logical form and attacks between claims in an argumentative framework. We propose to generalize qualitative attack principles by quantitative ones. Specifically, we use coherent conditional probabilities to evaluate the rationality of principles which govern the strength of argumentative attacks. Finally, we present an experiment which explores the psychological plausibility of selected attack principles.

This paper provides a Kantian interpretation of core issues involved in the trial following the terrorist attacks that struck Norway on July 22nd 2011. After a sketch of the controversies surrounding the trial itself, a Kantian theory of why the wrongdoer’s mind struck us as so endlessly disturbed is presented. This Kantian theory, I proceed by arguing, also helps us understand why it was so important to respond to the violence through the legal system and to treat the perpetrator, Anders (...) Behring Breivik, so respectfully before, during, and after the trial. I close by addressing the controversial issue now facing Norway: how capable is the Norwegian legal system to deal with cases involving extreme violence, including as committed by psychologically impaired mass murderers? (shrink)

Text-guided image generation models can be prompted to generate images using nonce words adversarially designed to robustly evoke specific visual concepts. Two approaches for such generation are introduced: macaronic prompting, which involves designing cryptic hybrid words by concatenating subword units from different languages; and evocative prompting, which involves designing nonce words whose broad morphological features are similar enough to that of existing words to trigger robust visual associations. The two methods can also be combined to generate images associated with more (...) specific visual concepts. The implications of these techniques for the circumvention of existing approaches to content moderation, and particularly the generation of offensive or harmful images, are discussed. (shrink)

The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. By leveraging (...) advanced optimization techniques such as Grid Search and Particle Swarm Optimization (PSO), the proposed SVM-based IDS achieves superior classification results. (shrink)

The attachment for download here merely references my 5,500-word final and extended article, criticising those who seek to justify Israeli attacks on Gaza. The article is published online by Daily Philosophy, 5th January 2024, link shown below. -/- After a background of facts (probably well-known by readers concerned about the matters), the article examines typical arguments much used in the media as attempts to justify Israel’s determined destruction of Gaza, involving well over twenty thousand Palestinians killed, hundreds of thousands suffering (...) appalling injuries and two million having their lives and livihoods shattered. -/- I expose inconsistencies, poor reasoning and immoralities of those who support Israeli actions. There is a final touch of Kant and Nietzsche - and use of the saying attributed to Jesus: For what shall it profit a man, if he shall gain the whole world and lose his own soul? (shrink)

This study examines the xenophobic attacks on Nigerians in South Africa, its ethical implications and responses of the Nigerian government. The study was guided by two objectives while it adopted the normative theory by Plato and Aristotle. The study looked at conceptual clarification like the concept of xenophobia. The study adopted ex-post research design while data was sourced through secondary source such as textbooks, journal articles, newspapers, magazines and internet while the data generated was analyzed through content analysis. The findings (...) of the study showed that there are Nigerians that are employees of labor in South Africa as well as Nigerians that are lecturers in South Africa universities that have contributed to their educational sector and there are also Nigerian students in their schools who pay fees as foreign students and these have contributed positively to South Africa economy and thus the reasons for these xenophobic attacks are not in accordance to the universalization of the norms of any society. The study also found out that the Nigerian government has responded in several ways which include, summoning of the South Africa ambassador to express her displeasure over the attacks on Nigerians, in the same vain Nigerians have used social media to call on the Nigerian government to boycott South African companies such as MTN, DSTV and Shoprite and also the immediate evacuation or repatriation of Nigerians in South Africa. Based on the findings the study recommended among others that the Nigerian government should let the South African government to know that the actions that are carried out by South Africans that has led to deaths of Nigerians no matter the actions that must had led to that does not fall within the moral principles of any society and the government of Nigeria should establish a bilateral commission with South Africa that will be meeting regularly to ensure protection of the two countries’ national interests. (shrink)

Nowak is (or was) a respected Harvard professor of mathematical biology with numerous well regarded publications. Sadly, he has chosen to launch an arrogant attack on science motivated by religious fervor. His recent actions show the evil consequences when universities accept money from religious groups, science journals are so awed by big names that they avoid proper peer review, and egos are permitted to get out of control. Most of this book is good, but it repeats the utterly misconceived (...) attacks on inclusive fitness in favor of group selection which he and his much more famous Harvard colleague E.O.Wilson have been making the last 5 years, so it is on that chapter of the book that I will concentrate. This takes us into the nature of evolution, the basics of scientific methodology, how math relates to science, what constitutes a law or a theory, and what attitudes to religion and generosity are appropriate as we inexorably approach the collapse of industrial civilization. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 3rd ed (2019), The Logical Structure of Human Behavior (2019), and Suicidal Utopian Delusions in the 21st Century 4th ed (2019). (shrink)

By drawing attention to these facts and to the relationship between Cantor’s and Husserl's ideas, I have tried to contribute to putting Frege's attack on Husserl "in the proper light" by providing some insight into some of the issues underling criticisms which Frege himself suggested were not purely aimed at Husserl's book. I have tried to undermine the popular idea that Frege's review of the Philosophy of Arithmetic is a straightforward, objective assessment of Husserl’s book, and to give some (...) specific reasons for thinking that the uncritical reading of Frege's review has unfairly distorted philosophers' perception of a work they do not know very well. (shrink)

This book has many arguments doing an excellent job of dismantling the positions of those who would have the state do considerably more than defend the national realm. Thus far, it is hard for me to fault it—which is more difficult when one is already in agreement: the ideologically opposed can often provide more useful criticisms. But, as the book‟s title indicates, it does not go all the way to anarcho-liberalism (in fact, it does not even fully embody certain basic (...) tenets of classical liberalism). And as its criticisms of anarcho-liberalism—or private-property anarchy—are those to which I am ideologically opposed, I shall mainly concentrate on those. (shrink)

In a recent article, Mario Attie-Picker maintains that a number of experimental studies provide evidence against Sextus Empiricus’s empirical claims about both the connection between belief and anxiety and the connection between suspension of judgement and undisturbedness. In this article, I argue that Sextus escapes unharmed from the challenge raised by the studies in question for the simple reason that he does not make the claims ascribed to him. In other words, I argue that Attie-Picker is attacking a straw man.

Since the early eighties, computationalism in the study of the mind has been “under attack” by several critics of the so-called “classic” or “symbolic” approaches in AI and cognitive science. Computationalism was generically identified with such approaches. For example, it was identified with both Allen Newell and Herbert Simon’s Physical Symbol System Hypothesis and Jerry Fodor’s theory of Language of Thought, usually without taking into account the fact ,that such approaches are very different as to their methods and aims. (...) Zenon Pylyshyn, in his influential book Computation and Cognition, claimed that both Newell and Fodor deeply influenced his ideas on cognition as computation. This probably added to the confusion, as many people still consider Pylyshyn’s book as paradigmatic of the computational approach in the study of the mind. Since then, cognitive scientists, AI researchers and also philosophers of the mind have been asked to take sides on different “paradigms” that have from time to time been proposed as opponents of (classic or symbolic) computationalism. Examples of such oppositions are: -/- computationalism vs. connectionism, computationalism vs. dynamical systems, computationalism vs. situated and embodied cognition, computationalism vs. behavioural and evolutionary robotics. -/- Our preliminary claim in section 1 is that computationalism should not be identified with what we would call the “paradigm (based on the metaphor) of the computer” (in the following, PoC). PoC is the (rather vague) statement that the mind functions “as a digital computer”. Actually, PoC is a restrictive version of computationalism, and nobody ever seriously upheld it, except in some rough versions of the computational approach and in some popular discussions about it. Usually, PoC is used as a straw man in many arguments against computationalism. In section 1 we look in some detail at PoC’s claims and argue that computationalism cannot be identified with PoC. In section 2 we point out that certain anticomputationalist arguments are based on this misleading identification. In section 3 we suggest that the view of the levels of explanation proposed by David Marr could clarify certain points of the debate on computationalism. In section 4 we touch on a controversial issue, namely the possibility of developing a notion of analog computation, similar to the notion of digital computation. A short conclusion follows in section 5. (shrink)

The coordinated attack scenario and the electronic mail game are two paradoxes of common knowledge. In simple mathematical models of these scenarios, the agents represented by the models can coordinate only if they have common knowledge that they will. As a result, the models predict that the agents will not coordinate in situations where it would be rational to coordinate. I argue that we should resolve this conflict between the models and facts about what it would be rational to (...) do by rejecting common knowledge assumptions implicit in the models. I focus on the assumption that the agents have common knowledge that they are rational, and provide models to show that denying this assumption suffices for a resolution of the paradoxes. I describe how my resolution of the paradoxes fits into a general story about the relationship between rationality in situations involving a single agent and rationality in situations involving many agents. (shrink)

The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. DDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD Cup 99) (...) dataset has been used to train and validate. For validation, comparison for 2 dataset (CICIDS2017 and KDD Cup 99) is done. Then, to implement the Deep learning algorithms is proposed. Method Classification using SVM algorithm Model predict is done. (shrink)

This article describes a case of a ’spiritual attack’ in the context of Amazonian ayahuasca ceremonies. These attacks are often assumed to be imaginary, and there is relatively little information available about them. Specifically, there appears to be no documentation about possible mechanisms of action for these attacks. Subjectively, they typically appear as context-dependent visions or somatic sensations that represent disease-inducing or lethal interventions from an external hostile party. -/- Such ’spiritual attacks’ could tentatively be conceptualized as subjective mental (...) representations of fundamental disagreements between two parties. ’Defenses’ against these attacks might then consist of methods for maintaining stability and resolving the associated negative affects. This discussion could facilitate an improved understanding of this rarely documented, difficult-to-conceptualize phenomenon. -/- doi: 10.13140/RG.2.2.11914.34247. (shrink)

The social and political development among countries has affected relationships and that has ushered a new era wherein others nations feel that they are better off than others. The mentality of betterment has resulted in hatred among nations in the same country and from different nations. This has culminated into civil war from within and clashes from without. These conflicts have divided countries especially in Africa which is propagating Ubuntu and communalism. From the premise, the paper will investigate the historical (...) background of xenophobia, followed by its impact on Africanism and communalism to an extent of hampering development. The paper will then propose a social developmental model that could assist in addressing xenophobia that is a threat to peace, stability, and development in South Africa. (shrink)

The categorical imperative inherent in Kant’s ethics has had indubitable historical influence on societies worldwide whether in the form of laws, democracy or public deliberation. The Toronto Van Attack of 2018 and its subsequent legal trial is a case example that shows how the categorical imperative can be applied to assist in understanding the reasoning for the case’s guilty verdict. This paper will convey the applicability of the categorical imperative for examining criminal case studies by closing the gap between (...) ethical theory and practice. Such closure will be shown to assist in understanding why a perpetrator of a crime can be found guilty of following a hypothetical imperative to base their actions. The rationale for the verdict in this case will be shown to be based on the perpetrator’s responsibility in the form of maintaining autonomy despite having an autism diagnosis. The perpetrator will be shown to have acted on an irrational will and yet was treated in the legal sense as a rational individual. Such rationality was maintained despite complications with their autonomy in the form of relativism and ethical solipsism. (shrink)

Tyler Burge is famous for defending primitivist naturalism about mental representations, according to which mental representations are primitive natural states. Primitivist naturalism contrasts with semantic reductionism, according to which mental representations are reducible to more fundamental natural states. Burge developed the most compelling and influential attack on semantic reductionism from a primitivist naturalist point of view. My goal in this paper is to defend semantic reduc- tionism from Burge’s attack. I assess and refute his objection to the motivations (...) for semantic reductionism and his objection to the semantic reductionist theory that he takes to be the most promising one, namely, teleosemantics. I argue that in spite of the ingenuity of Burge’s objections, they fail to show that there is no motivation for taking representational states as reducible to more fundamental natural states or that the core thesis of teleosemantics is flawed. (shrink)

The thought presented in this short piece is purely hypothetical, or a guess, if you like to put it that way. However, the nature of the mindsponge process will lead to the possibility of bringing our BMF analytics's power into further investigation later on.

How can we make rational decisions that involve transformative experiences, that is, experiences that can radically change our core preferences? L. A. Paul (2014) has argued that many decisions involving transformative experiences cannot be rational. However, Paul acknowledges that some traumatic events can be transformative experiences, but are nevertheless not an obstacle to rational decision-making. For instance, being attacked by hungry sharks would be a transformative experience, and yet, deciding not to swim with hungry sharks is rational. Paul has tried (...) to explain why decisions involving ‘sharky’ outcomes are an exception to the rule. However, her putative explanation has been criticized by Campbell and Mosquera (2020). In this paper, I offer a different solution to this problem. Roughly, I argue that transformative experiences give rise a problem for rational decision-making only if the decision can lead to satisfying some of our (new) core preferences, but can also frustrate other (new) core preferences. I also argue that agents can partially project what traumatic transformative experiences are like. (shrink)

Nowadays, intrusions have become a major problem faced by users. To stop these cyber attacks from happening, the development of a reliable and effective Intrusion Detection System (IDS) for cyber security has become an urgent issue to be solved. The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train and validate (...) the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. DDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD Cup 99) dataset has been used to train and validate. For validation, comparison for 2 dataset (CICIDS2017 and KDD Cup 99) is done. Then, to implement the Deep learning algorithms is proposed. Method Classification using SVM algorithm Model predict is done. Testing dataset for anomaly detection model classified as attack or normal. Finally, the experimental results shows that the performance metrics such as accuracy, precision, recall and confusion matrix. (shrink)

In adversarial machine learning, attackers add carefully crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. In this paper, we did comprehensive review of some of the most recent research, advancement and discoveries on adversarial attack, adversarial sampling generation, the potency or effectiveness of each of the existing attack methods, we also did comprehensive review on some of the most recent research, advancement and discoveries on adversarial defense (...) strategies, the effectiveness of each defense methods, and finally we did comparison on effectiveness and potency of different adversarial attack and defense methods. We came to conclusion that adversarial attack will mainly be blackbox for the foreseeable future since attacker has limited or no knowledge of gradient use for NN model, we also concluded that as dataset becomes more complex, so will be increase in demand for scalable adversarial defense strategy to mitigate or combat attack, and we strongly recommended that any neural network model with or without defense strategy should regularly be revisited, with the source code continuously updated at regular interval to check for any vulnerability against newer attack. (shrink)

The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. By leveraging (...) advanced optimization techniques such as Grid Search and Particle Swarm Optimization (PSO), the proposed SVM-based IDS achieves superior classification results. (shrink)

G.A. Cohen is famous for his critique of John Rawls’s view that principles of justice are restricted in scope to institutional structures. In recent work, however, Cohen has suggested that Rawlsians get more than just the scope of justice wrong: they get the concept wrong too. He claims that justice is a fundamental value, i.e. a moral input in our deliberations about the content of action-guiding regulatory principles, rather than the output. I argue here that Cohen’s arguments for extending the (...) scope of justice equivocate across his distinction between fundamental principles of justice, i.e. principles that tell us what justice is; and regulatory principles of justice, i.e. principles that tell us what is required of us, all things – including justice – considered. Though Cohen initially had the regulatory sense of the word ‘justice’ in mind when critiquing the basic structure restriction, his replies to the problem of demandingness presuppose his own, fundamental sense of the word ‘justice’. The upshot is that he escapes demandingness at the cost of sacrificing regulatory justice’s capacity to provide clear guidance. I conclude by considering Peter Singer’s efforts to deal with demandingness in his own work on global poverty. Since Singer manages to deal with demandingness without giving up clarity, his work is a good a place to start in the search for regulatory principles that are suitable for the context of personal choice. (shrink)

The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an (...) adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection. (shrink)

ABSTRACT. Associationist psychologists of the late 19th-century premised their research on a fundamentally Humean picture of the mind. So the very idea of mental science was called into question when T. H. Green, a founder of British idealism, wrote an influential attack on Hume’s Treatise. I first analyze Green’s interpretation and criticism of Hume, situating his reading with respect to more recent Hume scholarship. I focus on Green’s argument that Hume cannot consistently admit real ideas of spatial relations. I (...) then argue that William James’s early work on spatial perception attempted to vindicate the new science of mind by showing how to avoid the problems Green had exposed in Hume’s empiricism. James’s solution involved rejecting a basic Humean assumption—that perceptual experience is fundamentally composed of so-called minima sensibilia, or psychological atoms. The claim that there are no psychological atoms is interesting because James supported it with experimental data rather than (as commentators typically suppose) with introspective description or a priori argument. James claimed to be the real descendant of British empiricism on grounds that his anti-atomistic model of perception fortified what Green had perhaps most wanted to demolish—the prospect of using empirical, scientific methods in the study of mind. (shrink)

Primarily using philosophy, but also some social science, Escape from Leviathan (EfL) explains and defends what it calls an extreme version of the implicit ‘classical liberal compatibility thesis’: liberty, welfare, and anarchy are overwhelmingly complementary in normal practice (rationality is added for its intimate theoretical connections to these categories). This is done using theories, not definitions, of each concept. This important thesis is entirely positive. Therefore, somewhat unusually, all normative issues are avoided as irrelevant distractions in this context. In addition, (...) the epistemology used is also unorthodox. A justificationist, or foundationalist, approach would be literally illogical: the self-refuting trilemma faced by ‘supporting justifications’ (as regards arguments and evidence) is of entailing infinite regresses, dogmatic starting points, or circularities. Consequently, critical rationalism is assumed: conjecture and criticism are all that is possible. Thus all aspects of the conjectured thesis are explained and defended from criticism as thoroughly as the book can encompass. It is concluded that the thesis has not been refuted and so may be critically preferred. (However, this could all still be viewed as a ‘justification’ by those thus inclined.) All of this is made clear enough in the short introductory chapter and repeated where relevant throughout. Yet some reviews do not have a firm grasp of the specific project and so reject it with unsound criticisms. We now turn to one such example. (shrink)

I take the debate between the particularists and the principlists to be centered on the issue of whether there are true moral principles. One argument the principlists often appeal to in support of their claim that there are true moral principles is the argument from supervenience. Roughly, the argument is made up of the following three statements: (P1) If the thesis of moral supervenience holds, then there are true moral principles. (P2) The thesis of moral supervenience holds. (C) There are (...) true moral principles, and hence particularism is false. In this paper, I argue that the above argument is not sound by attacking (P1). I hold that no general supervenient/resultance base has a robust enough configuration of contextual features as to ground the existence of true moral principles. If I am right about this, I think it would be indicative of a reason to be less confident about the truth of principlism and more confident about the truth of particularism. (shrink)