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In many everyday decisions, individuals choose between trialling something novel or something they know well. Deciding when to try a new option or stick with an option that is already known to you, known as the "explore/exploit" dilemma, is an important feature of cognition that characterises a range of decision-making contexts encountered by humans. Recent evidence has suggested preferences in explore/exploit biases are associated with psychopathology, although this has typically been examined within individual disorders. The current review examined whether explore/exploit decision-making represents a promising transdiagnostic target for psychosis, depression, and anxiety. A systematic search of academic databases was conducted, yielding a total of 29 studies. Studies examining psychosis were mostly consistent in showing that individuals with psychosis explored more compared with individuals without psychosis. The literature on anxiety and depression was more heterogenous; some studies found that anxiety and depression were associated with more exploration, whereas other studies demonstrated reduced exploration in anxiety and depression. However, examining a subset of studies that employed case-control methods, there was some evidence that both anxiety and depression also were associated with increased exploration. Due to the heterogeneity across the literature, we suggest that there is insufficient evidence to conclude whether explore/exploit decision-making is a transdiagnostic target for psychosis, depression, and anxiety. However, alongside our advisory groups of lived experience advisors, we suggest that this context of decision-making is a promising candidate that merits further investigation using well-powered, longitudinal designs. Such work also should examine whether biases in explore/exploit choices are amenable to intervention. [ABSTRACT FROM AUTHOR]

Née en 1948, Christine Caron a su s’imposer comme une championne au service des projets de la société gaulliste. La télévision et la radio jouent un rôle important dans la genèse de cette figure de championne, mais aussi d’idole des jeunes. Multiple recordwoman de France, puis d’Europe des 100 et 200 m dos, Christine Caron est visible parce qu’elle réalise des exploits sportifs, mais surtout parce qu’elle représente un idéal de jeunesse et répond aux normes de la féminité sportive. Elle est tout de même attendue comme une future championne olympique par le public français. Le 14 octobre 1964, elle termine deuxième. Pour la télévision et la radio, c’est un échec. Basé sur un corpus d’extrait de radio et de télévision public de 1962 à 1964, il s’agit ici de s’interroger sur le rôle des médias dans la construction d’un espoir des Français pour une médaille d’or olympique de Christine Caron lors des Jeux Olympiques de Tokyo en 1964.

One of the most significant barriers to blockchain adoption is the need for a better user experience. Tasks such as account setup, key management, and transaction handling need to be streamlined to propel the next wave of technological adoption across various sectors. A promising solution to these issues lies in account abstraction, which aims to simplify the user experience by masking the underlying complexities of blockchain technology. The most notable effort to implement account abstraction is Ethereum's ERC-4337, a proposal that addresses these pain points by enhancing flexibility and ease of use for developers and end users alike. This paper explores the account abstraction architecture, its functions and how it can redefine blockchain utility, from smart contracts to smart reporting. [ABSTRACT FROM AUTHOR]

Copyright of Materiales para la Historia del Deporte is the property of Polytechnic University of Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the evolving landscape of cybersecurity threats, permanent denial-of-service (PDoS) attacks have emerged as a particularly damaging form of cyber aggression. Unlike the more well-known denial-of-service (DoS) attacks, which disrupt services temporarily, PDoS attacks aim to inflict irreversible damage to systems, often resulting in significant system overhauls and requiring hardware replacement. To enable the development of effective security measures, but also to address the knowledge gaps, this paper presents an in-depth exploration of PDoS attacks, emphasizing their distinguishing characteristics, underlying mechanisms, and potential further development. Through a comprehensive case study, this research highlights diverse tactics and strategies employed by attackers, from targeting IoT devices to manipulating boot processes and exploiting firmware vulnerabilities. A novel classification of PDoS attack vectors is proposed that also explains the ways in which the systems can be compromised. The findings confirm the pressing need for adaptive and robust defense mechanisms to mitigate the threats posed by PDoS attacks in our interconnected digital world.

In the evolving landscape of cybersecurity threats, permanent denial-of-service (PDoS) attacks have emerged as a particularly damaging form of cyber aggression. Unlike the more well-known denial-of-service (DoS) attacks, which disrupt services temporarily, PDoS attacks aim to inflict irreversible damage to systems, often resulting in significant system overhauls and requiring hardware replacement. To enable the development of effective security measures, but also to address the knowledge gaps, this paper presents an in-depth exploration of PDoS attacks, emphasizing their distinguishing characteristics, underlying mechanisms, and potential further development. Through a comprehensive case study, this research highlights diverse tactics and strategies employed by attackers, from targeting IoT devices to manipulating boot processes and exploiting firmware vulnerabilities. A novel classification of PDoS attack vectors is proposed that also explains the ways in which the systems can be compromised. The findings confirm the pressing need for adaptive and robust defense mechanisms to mitigate the threats posed by PDoS attacks in our interconnected digital world. [ABSTRACT FROM AUTHOR]

Blockchain technology has captured the attention of various industries due to its potential to revolutionize traditional systems through decentralization, transparency and immutability. This paper examines the emerging trend of integrating blockchain-based decentralized applications (DApps) into the education sector. With blockchain's distributed ledger system, educational institutions can achieve safe, secure and transparent management of student achievements, certifications and credentials. The paper presents the architecture of a system that enables students to build verifiable digital portfolios of educational achievements stored and shared securely using smart contracts and digital tokens. By harnessing the benefits and addressing the challenges of blockchain DAppps in education, we can pave the way for a truly transformative era in the learning experience of tomorrow.

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information. This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain. [ABSTRACT FROM AUTHOR]

Researchers and experts on information protection develop antivirus programs and applications to improve the security of operating systems and security policies. Threats will be relevant to organizations that do not consider security policies and regular software updates. This paper discusses applications for scanning and analyzing network traffic, such as Netdiscover, Wireshark, and Nmap. The model network is based on a virtual machine. This research aims to determine methods for scanning and analyzing network traffic and detecting network vulnerabilities. This study conducted a penetration test for Windows 10 using the Kali Purple operating system and identified the vulnerability of the operating system. The calculation of network traffic is performed with (1) the determination of the arithmetic means of network traffic, (2) the calculation of the variance, and (3) the determination of the magnitude of fluctuations relative to the average M; the range of maximum and minimum values of D; and the Hurst coefficient. As a result of the conducted research on students enrolled in the educational program 6B06301 -- Information Security Systems at Farabi University, the proficiency in MS Excel and C# skills amounted to 77.11%. The research results can be used in the field of information security systems. [ABSTRACT FROM AUTHOR]

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information.This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain.

From adolescence to adulthood, there is a decrease in exploration choices in foraging decision-making problems. In youth, a disposition to explore when faced with explore/exploit choices can support the individual to learn the structure of their environment, enabling them to effectively exploit rewards in maturity. Such age-related differences are likely driven by neurocognitive changes to reward processing, learning, and cognitive control across development. Individual differences in foraging strategies can be indicative of cognitive biases implicated in mental health outcomes. Patch foraging is a near-ubiquitous behaviour across the animal kingdom and characterises many decision-making domains encountered by humans. We review how a disposition to explore in adolescence may reflect the evolutionary conditions under which hunter-gatherers foraged for resources. We propose that neurocomputational mechanisms responsible for reward processing, learning, and cognitive control facilitate the transition from exploratory strategies in adolescence to exploitative strategies in adulthood – where individuals capitalise on known resources. This developmental transition may be disrupted by psychopathology, as there is emerging evidence of biases in explore/exploit choices in mental health problems. Explore/exploit choices may be an informative marker for mental health across development and future research should consider this feature of decision-making as a target for clinical intervention. [ABSTRACT FROM AUTHOR]

Currently, enhancing the efficiency of vulnerability detection and assessment remains relevant. We investigate a new approach for the detection of vulnerabilities that can be used in cyber attacks and assess their severity for further effective responses based on an analysis of exploit source codes and real-time detection of features of their implementation. The key element of this approach is an exploit source code model. In this paper, to specify the model, we systematically analyze existing source code models, approaches to source code analysis in general, and exploits in particular in order to examine their advantages, applications, and challenges. Finally, we provide an initial specification of the proposed source code model. [ABSTRACT FROM AUTHOR]

For a better awareness of the tactics employed by the malicious entities in Internet of Things, a system called Honeypot tricks the attackers into exploiting its "sweet" fake resources. While implementing nine types of devices only six of those were attacked. Despite this, the honeypot managed to capture attacks destined for three devices that were not implemented. Overall, several real-world attacks were captured and analyzed providing different indicators of compromise. No new threats were identified but the server only ran for a short period of time and with limited resources. This approach looks like a promising avenue for creating attacker profiles and collecting data on botnet behavior. [ABSTRACT FROM AUTHOR]

Malicious actors often utilize publicly available software vulnerabilities and exploit codes to attack vulnerable targets. Exploit codes are shared across several platforms, including exploit databases, hacker communities, and social media platforms. Public exploit code information is a type of cyber threat intelligence. It can help security experts to analyze which vulnerabilities are available for malicious actors and need to be prioritized for patching. In this paper, We propose a intelligent framework to automatically extract public exploit code information from social media. Social media sites are capable of aggregating numerous cybersecurity-related information due to their timeliness and volume. Firstly, we present a convolutional neural network classifier to identity disclose exploit codes in their content or corresponding web pages linked in tweets, which achieved 0.989 AUC and 0.939 F1-score. The model shows better prediction accuracy than the baseline approaches. Secondly, we present a Bert-BiLSTM-CRF entity recognition method to figure out the target entity which may be influenced by the exploit code. As a result, the Bert-BiLSTM-CRF model reached an F1-score of 0.959, which performed better than the 0.927 and 0.922 obtained by the same neural network using Word2vec and GloVe word embeddings respectively. Finally, the experiment results show the proposed method provide enriched supplementary information and earlier intelligence for the appearances of open exploit codes on the Internet by contrasting to the exploit database. [ABSTRACT FROM AUTHOR]

As the most crucial link in the network kill chain, exploiting a vulnerability is viewed as one of the most popular attack vectors to get the control authority of the system, which is dangerous for legal users. Therefore, an effective exploit traffic detection method is urgent. However, current methods are almost based on pattern matching, invalid for encrypted traffic. To address this problem, we propose a reverse shell-based exploit traffic detection method, ETDetector. Our key insight is that the reverse shell attack often coexists with vulnerability exploitation as one of the most popular exploit behaviors. So, we first extract the fusion information feature from original features, such as the packet delay sequence, as input of a decision tree model to identify reverse shell traffic in the shellcode execution stage. Then, we trace suspicious traffic in the shellcode delivery stage by reconstructing the session relationship of the two stages above. Compared with Blatta, using a cyclic neural network to detect early exploit traffic, the detection rate of ETDetector is increased by 50% and valid for encrypted exploit traffic. In addition, we propose a traffic stratification method based on a bisecting K-means algorithm, which can intuitively show the traffic communication behavior and improve the interpretability of ETDetector. [ABSTRACT FROM AUTHOR]

Copyright of Al-Adab / Al-ādāb is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the present world, information and data are the greatest assets one can possess. If one cannot secure their information from cyber-attacks, they would lose everything in the blink of an eye. Penetration testing can help reduce this cyber-risk exposure of clients' data and protect them. Penetration testing (also called "pen testing") is a part of ethical hacking that exposes the weak areas, vulnerabilities, or loopholes in the core of a PC, its networks, and its applications with the purpose of securing the system. The main idea of pen testing is to find vulnerabilities in systems and fix them before attackers can take advantage of them. These vulnerabilities are identified, exploited, and analyzed in five phases: information gathering, scanning, gaining access, maintaining access, and covering tracks. Penetration testing is done regularly in order to maintain high-security standards. As it pertains to any organization's secrecy and privacy, this testing is also constrained by a number of legal agreements. The work provided by many researchers in the field of penetration (PEN) testing is reviewed and analyzed in this paper. This report gives a detailed description of the process and tools used to conduct penetration testing. [ABSTRACT FROM AUTHOR]

Zero-day attacks are among the most dangerous security incidents affecting both home users and corporate environments. Since 2021 broke the record for zero-day attacks, this study will present the state-of-the-art by clarifying the concept and giving a detailed analysis of the field. Unknown threats such as zero day or unknown malware software usually avoid traditional antivirus or antimalware protection solutions. This type of cyberattack disrupts the activity of companies, causing loss of time and money or compromising confidential data. By their nature, antivirus signatures cannot stop unknown threats. New and old security vendors claim that their "next generation" solutions use signature-based detection based on revolutionary technologies such as machine learning to identify zero-day attacks. Despite captivating stories and tempting words, the effectiveness of these solutions is unscientific, and it is rarely supported by reliable sources. The results of this paper will present the development of different well-known attacks by analyzing their evolution.

The security architecture and procedure for 5G systems (TS 33.501) is based on the 3rd Generation Partner Project (3GPP) security specification draft that is released in 2018. Since its debut, the security violations in the 5G security protocol have been intensively studied and discussed. Based on the 5G security protocol, this paper illustrates a new tracking-attack scenario that feasibly makes subscribers suffer in a breakdown of personal privacy. Specifically, it is shown in this paper that patterns of personal behavior are leaked without any awareness during the synchronization procedures in the 5G protocol. An in-depth analysis of the privacy violations is presented in this paper and potential countermeasures for protecting the sensitive information of genuine subscribers are given. A lemma model based on the TAMARIN Prover is illustrated to analyze the privacy vulnerabilities in the depicted attack scenario. Furthermore, a practical experiment based on the srsLTE framework is setup to demonstrate how the privacy information of genuine subscribers are violated based on the scenario that is reported in this paper.

Background: Microsoft Windows Security is a recently implemented safeguard for the Windows operating systems, including the latest versions of Windows10 and 11. However, there is a major shortcoming in this system to stop Advanced Persistent Threat (APT). These are government-financed groups that are funded to attack other government entities. Following the initial security breach, the hacked Windows device is used to access the rest of the network devices in order to transfer data to external storage (Exfiltration). Methods: In this work, we have tested the Microsoft Windows Security system using MITRE CALDERA and ATT&CK frameworks and explain how APT groups are able to bypass Windows Security. Results: In this study we used '54ndc47' agent through GoLang feature in MITRE CALDERA platform to test and bypass Microsoft Windows Security systems (MS Windows 10). Through it, we were able to bypass the Windows Security system and display entire files in the victim's device. Conclusions: In this paper, we have provided recommendations to Microsoft to improve their Windows Security tool through the use of Artificial intelligence (AI).

Currently, enhancing the efficiency of vulnerability detection and assessment remains relevant. We investigate a new approach for the detection of vulnerabilities that can be used in cyber attacks and assess their severity for further effective responses based on an analysis of exploit source codes and real-time detection of features of their implementation. The key element of this approach is an exploit source code model. In this paper, to specify the model, we systematically analyze existing source code models, approaches to source code analysis in general, and exploits in particular in order to examine their advantages, applications, and challenges. Finally, we provide an initial specification of the proposed source code model.

The development of information and communication technology that is increasingly widespread in today's world, has led to a new phenomenon called cybercrime, it can even be done instead of communication through love scams or scammer love. In this study, we would like to conclude several factors that can lead to crime using love scam communication techniques to provide a solution. The phenomenon of love scam or scammer love. This action is an act of fraud under the guise of romance. Usually the perpetrator uses tricks or instead of trust that involves the emotions or feelings of the victim and then takes advantage of those feelings or good intentions to commit fraud. indeed it can not be seen directly. That's what causes hackers to be interested in using the Internet as a medium of communication and at the same time forming a community, because the Internet is the fastest way for hackers to act and interact with victims without having to show their true identity. [ABSTRACT FROM AUTHOR]

This survey is Chinese Advanced Persistent Threat (APT) real attack groups and scenarios. This survey provides a taxonomy of Chinese APT groups/attacks in conjunction with the use of Threat Intelligence (TI) to detect and prevent the attacks. This paper will provide the current knowledge and emerging APT groups that target governments and private enterprises. In addition, this paper presents, contributions, performance comparison and methods of criticism of detection in the current solutions. The study covers many attack groups funded by different Chinese governments to attack other governments around the world, taking into account that each group is specialized to attack specific sectors, some of them attack the military, police and intelligence departments, and some attack the banking, commercial and agricultural sectors, and some attack the information technology, health, arts, and nanotechnology sectors, etc. In this paper, we propose solutions at the first potential victim, and at the network, level to stop APT attacks. We recommend that there must be multi-layer protection over the first machine and infrastructure to detect and prevent APT attacks. This Paper will use adversarial tactics techniques and common knowledge (ATT&CK) as a knowledge base. We recommend researchers focus on ATT&CK, and TI to develop a solution against APT attacks. [ABSTRACT FROM AUTHOR]

Background: The ongoing coronavirus (COVID-19) pandemic has had a profound global impact. Although it has unexpectedly placed considerable strain on healthcare sectors, the effects of the pandemic have been far more extensive--presenting significant challenges to infrastructures and industries including the economy, transportation systems, and even telecommunications operations. These persistent issues have created a pathway, providing cyber criminals with an opportunity to employ advanced persistent threats (APT) to target private and government sectors. These prolonged attacks have sought to target enterprise organizations within the public sector, allowing attackers to covertly gain access to their infrastructures and information systems, often remaining undetectable for long periods of time. Technically speaking, APTs are considered one of the most dangerous types of cyber-attacks, in part due to the myriad of techniques and tactics which can be undertaken. This paper will illustrate conditions for these attacks to triumph. Methods: This paper used MITRE ATT&CK to purpose Multi Layers Protection Approach (MLPA), beginning with the implementing CPU utilization method based of using mimikatz malicious application in credential dumping technique on all internal devices, passing through the application of the approach to the entire infrastructure Results: MLPA provided in this paper was able to detect APT attacks based on central processing unit utilization, protection rule with accuracy detection 99.7% and false-positive 0.3%. Meanwhile got entire information of APT attack #20373e4d4d11ba0e839278737ee9fc49cb164bbd# Conclusions: In this paper we have proposed APT groups (Kimsuky, APT36, Patchwork, TA505, TA542, Ocean Lotus and Gamaredon) and how to mitigate their attacks Through using MLPA and CPU method. [ABSTRACT FROM AUTHOR]

Background: Microsoft Windows Security is a recently implemented safeguard for the Windows operating systems, including the latest versions of Windows10 and 11. However, there is a major shortcoming in this system to stop Advanced Persistent Threat (APT). These are government-financed groups that are funded to attack other government entities. Following the initial security breach, the hacked Windows device is used to access the rest of the network devices in order to transfer data to external storage (Exfiltration). Methods: In this work, we have tested the Microsoft Windows Security system using MITRE CALDERA and ATT&CK frameworks and explain how APT groups are able to bypass Windows Security. Results: In this study we used '54ndc47' agent through GoLang feature in MITRE CALDERA platform to test and bypass Microsoft Windows Security systems (MS Windows 10). Through it, we were able to bypass the Windows Security system and display entire files in the victim's device. Conclusions: In this paper, we have provided recommendations to Microsoft to improve their Windows Security tool through the use of Artificial intelligence (AI).

Background: Microsoft Windows Security is a recently implemented safeguard for the Windows operating systems, including the latest versions of Windows10 and 11. However, there is a major shortcoming in this system to stop Advanced Persistent Threat (APT). These are government-financed groups that are funded to attack other government entities. Following the initial security breach, the hacked Windows device is used to access the rest of the network devices in order to transfer data to external storage (Exfiltration). Methods: In this work, we have tested the Microsoft Windows Security system using MITRE CALDERA and ATT&CK frameworks and explain how APT groups are able to bypass Windows Security. Results: In this study we used '54ndc47' agent through GoLang feature in MITRE CALDERA platform to test and bypass Microsoft Windows Security systems (MS Windows 10). Through it, we were able to bypass the Windows Security system and display entire files in the victim's device. Conclusions: In this paper, we have provided recommendations to Microsoft to improve their Windows Security tool through the use of Artificial intelligence (AI).

As the most crucial link in the network kill chain, exploiting a vulnerability is viewed as one of the most popular attack vectors to get the control authority of the system, which is dangerous for legal users. Therefore, an effective exploit traffic detection method is urgent. However, current methods are almost based on pattern matching, invalid for encrypted traffic. To address this problem, we propose a reverse shell-based exploit traffic detection method, ETDetector. Our key insight is that the reverse shell attack often coexists with vulnerability exploitation as one of the most popular exploit behaviors. So, we first extract the fusion information feature from original features, such as the packet delay sequence, as input of a decision tree model to identify reverse shell traffic in the shellcode execution stage. Then, we trace suspicious traffic in the shellcode delivery stage by reconstructing the session relationship of the two stages above. Compared with Blatta, using a cyclic neural network to detect early exploit traffic, the detection rate of ETDetector is increased by 50% and valid for encrypted exploit traffic. In addition, we propose a traffic stratification method based on a bisecting K-means algorithm, which can intuitively show the traffic communication behavior and improve the interpretability of ETDetector.