Screen-Printed Stretchable Supercapacitors Based on Tin Sulfide-Decorated Face-Mask-Derived Activated Carbon Electrodes with High Areal Energy Density

\ua9 2024 The Authors. Published by American Chemical Society.In this work, tin sulfide nanosheets decorated on face-mask-derived activated carbon have been explored as electrode material for electrochemical supercapacitors. A hydrothermal route was employed to grow tin sulfide on the surface and inside of high-surface-area face-mask-derived activated carbon, activated at 850 \ub0C, to produce a hierarchical interconnected porous composite (ACFM-850/TS) structure. The presence of tin sulfide in the porous carbon framework exposed the surface active sites for rapid adsorption/desorption of electrolyte ions and ensured high utilization of the porous carbon surface. Furthermore, the porous ACFM-850 framework prevented the stacking/agglomeration of tin sulfide sheets, thereby enhancing the charge-transport kinetics in the composite electrodes. Benefiting from the synergistic effect of tin sulfide and ACFM-850, the resulting ACFM-850/TS composite exhibited an attractive specific capacitance of 423 F g-1 at a 0.5 A g-1 current density and superior rate capability (71.3% at a 30 A g-1 current density) in a 1.0 M Na2SO4 electrolyte. In addition, we fabricated a planar symmetric interdigitated supercapacitor on a stretchable Spandex fabric using an ACFM-850/TS composite electrode and carboxymethyl cellulose/NaClO4 as a solid-state gel electrolyte employing a scalable screen-printing process. The as-prepared stretchable supercapacitors displayed an ultrahigh energy density of 9.2 μWh cm-2 at a power density of 0.13 mW cm-2. In addition, they exhibited an excellent cyclic stability of 64% even after 10,000 charge-discharge cycles and 42% after 1000 continuous stretch (at 25% stretching)/release cycles. Such screen-printed interdigitated planar supercapacitors with activated carbon composite electrodes and a solid-state gel electrolyte act as promising low-cost energy-storage devices for wearable and flexible integrated electronic devices

A systematic literature review and meta-analysis on artificial intelligence in penetration testing and vulnerability assessment

Vulnerability assessment (e.g., vulnerability identification and exploitation; also referred to as penetration testing) is a relatively mature industry, although attempting to keep pace with the diversity of computing and digital devices that need to be examined is challenging. Hence, there has been ongoing interest in exploring the potential of artificial intelligence to enhance penetration testing and vulnerability identification of systems, as evidenced by the systematic literature review performed in this paper. In this review, we focus only on empirical papers, and based on the findings, we identify a number of potential research challenges and opportunities, such as scalability and the need for real-time identification of exploitable vulnerabilities

Lico: A Lightweight Access Control Model for Inter-Networking Linkages

© 2013 IEEE. Processes in operating systems are assigned different privileges to access different resources. A process may invoke other processes whose privileges are different; thus, its privileges are expanded (or escalated) due to such improper 'inheritance.' Inter-networking can also occur between processes, either transitively or iteratively. This complicates the monitoring of inappropriate privilege assignment/escalation, which can result in information leakage. Such information leakage occurs due to privilege transitivity and inheritance and can be defined as a general access control problem for inter-networking linkages. This is also a topic that is generally less studied in existing access control models. Specifically, in this paper, we propose a lightweight directed graph-based model, LiCo, which is designed to facilitate the authorization of privileges among inter-networking processes. To the best of our knowledge, this is the first general access control model for inter-invoking processes and general inter-networking linkages

A systematic literature review of blockchain cyber security

Since the publication of Satoshi Nakamoto's white paper on Bitcoin in 2008, blockchain has (slowly) become one of the most frequently discussed methods for securing data storage and transfer through decentralized, trustless, peer-to-peer systems. This research identifies peer-reviewed literature that seeks to utilize blockchain for cyber security purposes and presents a systematic analysis of the most frequently adopted blockchain security applications. Our findings show that the Internet of Things (IoT) lends itself well to novel blockchain applications, as do networks and machine visualization, public key cryptography, web applications, certification schemes and the secure storage of Personally Identifiable Information (PII). This timely systematic review also sheds light on future directions of research, education and practices in the blockchain and cyber security space, such as security of blockchain in IoT, security of blockchain for AI data, and sidechain security,etc

A hierarchical key pre-distribution scheme for fog networks

Security in fog computing is multi-faceted, and one particular challenge is establishing a secure communication channel between fog nodes and end devices. This emphasizes the importance of designing efficient and secret key distribution scheme to facilitate fog nodes and end devices to establish secure communication channels. Existing secure key distribution schemes designed for hierarchical networks may be deployable in fog computing, but they incur high computational and communication overheads and thus consume significant memory. In this paper, we propose a novel hierarchical key pre-distribution scheme based on “Residual Design” for fog networks. The proposed key distribution scheme is designed to minimize storage overhead and memory consumption, while increasing network scalability. The scheme is also designed to be secure against node capture attacks. We demonstrate that in an equal-size network, our scheme achieves around 84% improvement in terms of node storage overhead, and around 96% improvement in terms of network scalability. Our research paves the way for building an efficient key management framework for secure communication within the hierarchical network of fog nodes and end devices. KEYWORDS: Fog Computing, Key distribution, Hierarchical Networks

Scalable slot-die coated flexible supercapacitors from upcycled PET face shields

\ua9 2024 The Royal Society of Chemistry.Upcycling Covid19 plastic waste into valuable carbonaceous materials for energy storage applications is a sustainable and green approach to minimize the burden of waste plastic on the environment. Herein, we developed a facile single step activation technique for producing activated carbon consisting of spherical flower like carbon nanosheets and amorphous porous flakes from used PET [poly(ethylene terephthalate)] face shields for supercapacitor applications. The as-obtained activated carbon exhibited a high specific surface area of 1571 m2 g−1 and pore volume of 1.64 cm3 g−1. The specific capacitance of these carbon nanostructure-coated stainless steel electrodes reached 228.2 F g−1 at 1 A g−1 current density with excellent charge transport features and good rate capability in 1 M Na2SO4 aqueous electrolyte. We explored the slot-die coating technique for large-area coatings of flexible high-performance activated carbon electrodes with special emphasis on optimizing binder concentration. Significant improvement in electrochemical performance was achieved for the electrodes with 15 wt% Nafion concentration. The flexible supercapacitors fabricated using these electrodes showed high energy and power density of 21.8 W h kg−1 and 20 600 W kg−1 respectively, and retained 96.2% of the initial capacitance after 10 000 cycles at 2 A g−1 current density. The present study provides a promising sustainable approach for upcycling PET plastic waste for large area printable supercapacitors

Deep dive into ransomware threat hunting and intelligence at fog layer

Ransomware, a malware designed to encrypt data for ransom payments, is a potential threat to fog layer nodes as such nodes typically contain considerably amount of sensitive data. The capability to efficiently hunt abnormalities relating to ransomware activities is crucial in the timely detection of ransomware. In this paper, we present our Deep Ransomware Threat Hunting and Intelligence System (DRTHIS) to distinguish ransomware from goodware and identify their families. Specifically, DRTHIS utilizes Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), two deep learning techniques, for classification using the softmax algorithm. We then use 220 Locky, 220 Cerber and 220 TeslaCrypt ransomware samples, and 219 goodware samples, to train DRTHIS. In our evaluations, DRTHIS achieves an F-measure of 99.6% with a true positive rate of 97.2% in the classification of ransomware instances. Additionally, we demonstrate that DRTHIS is capable of detecting previously unseen ransomware samples from new ransomware families in a timely and accurate manner using ransomware from the CryptoWall, TorrentLocker and Sage families. The findings show that 99% of CryptoWall samples, 75% of TorrentLocker samples and 92% of Sage samples are correctly classified

Forensic investigation of cross platform massively multiplayer online games: Minecraft as a case study

Minecraft, a Massively Multiplayer Online Game (MMOG), has reportedly millions of players from different age groups worldwide. With Minecraft being so popular, particularly with younger audiences, it is no surprise that the interactive nature of Minecraft has facilitated the commission of criminal activities such as denial of service attacks against gamers, cyberbullying, swatting, sexual communication, and online child grooming. In this research, there is a simulated scenario of a typical Minecraft setting, using a Linux Ubuntu 16.04.3 machine (acting as the MMOG server) and Windows client devices running Minecraft. Server and client devices are then examined to reveal the type and extent of evidential artefacts that can be extracted

A Mixing Scheme Using a Decentralized Signature Protocol for Privacy Protection in Bitcoin Blockchain

IEEE Bitcoin transactions are not truly anonymous as an attacker can attempt to reveal a user's private information by tracing related transactions. Existing approaches to protect privacy (e.g. mixcoin, shuffle, and blinded token) suffer from a number of limitations. For example, some approaches assume the existence of a trusted third party, rely on exchanges among various currencies, or broadcast sensitive details before mixing. Therefore, there is a real risk of privacy breach or losing tokens. Thus in this paper, we design a mixing scheme with one decentralized signature protocol, which does not rely on a third party or require a transaction fee. Specifically, our scheme uses a negotiation process to guarantee transaction details, which is monitored by the participants. Furthermore, the scheme includes a signature protocol based on the ElGamal signature protocol and secret sharing. The proposed scheme is then proven secure

AKR7A3 suppresses tumorigenicity and chemoresistance in hepatocellular carcinoma through attenuation of ERK, c-Jun and NF-κB signaling pathways

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