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Cyberattack Ramifications, The Hidden Cost of a Security Breach

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Journal of Information Security 2024年第2期15卷 87-105页

作者： Meysam Tahmasebi department of cybersecurity EC-Council University Albuquerque USA

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

关键词： Artificial Intelligence (AI) Business Continuity Case Studies Copyright Cost-Benefit Analysis Credit Rating Cyberwarfare cybersecurity Breaches Data Breaches Denial Of Service (DOS) Devaluation Of Trade Name Disaster Recovery Distributed Denial of Service (DDOS) Identity Theft Increased Cost to Raise Debt Insurance Premium Intellectual Property Operational Disruption Patent Post-Breach Customer Protection Recovery Point Objective (RPO) Recovery Time Objective (RTO) Regulatory Compliance Risk Assessment Service Level Agreement Stuxnet Trade Secret

Energy-Efficient Implementation of BCD to Excess-3 Code Converter for Nano-Communication Using QCA Technology

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China Communications 2024年第6期21卷 103-111页

作者： Nuriddin Safoev Angshuman Khan Khudoykulov Zarif Turakulovich Rajeev Arya department of cybersecurity&Criminology Tashkent University of Information TechnologiesTashkent 100084Uzbekistan department of Electronics and Communication Engineering University of Engineering and ManagementJaipurRajasthan 303807India department of Cryptology Tashkent University of Information TechnologiesTashkent 100084Uzbekistan department of Electronics and Communication Engineering National Institute of Technology PatnaBihar 800005India

Code converters are essential in digital nano communication;therefore,a low-complexity optimal QCA layout for a BCD to Excess-3 code converter has been proposed in this paper.A QCA clockphase-based design technique was adopted to investigate integration with other complicated circuits.Using a unique XOR gate,the recommended circuit’s cell complexity has been decreased.The findings produced using the QCADesigner-2.0.3,a reliable simulation tool,prove the effectiveness of the current structure over earlier designs by considering the number of cells deployed,the area occupied,and the latency as design metrics.In addition,the popular tool QCAPro was used to estimate the energy dissipation of the proposed design.The proposed technique reduces the occupied space by∼40%,improves cell complexity by∼20%,and reduces energy dissipation by∼1.8 times(atγ=1.5EK)compared to the current scalable designs.This paper also studied the suggested structure’s energy dissipation and compared it to existing works for a better performance evaluation.

关键词： BCD code converter Excess-3 nano communication QCA circuits

Cloud Datacenter Selection Using Service Broker Policies:A Survey

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 1-41页

作者： Salam Al-E’mari Yousef Sanjalawe Ahmad Al-Daraiseh Mohammad Bany Taha Mohammad Aladaileh department of Information Security Faculty of ITUniversity of PetraAmman11196Jordan department of cybersecurity School of ITAmerican University of Madaba(AUM)Amman11821Jordan department of Computer Science School of ITAmerican University of Madaba(AUM)Amman11821Jordan department of Data Science and Artificial Intelligence School of ITAmerican University of Madaba(AUM)Amman11821Jordan

Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.

关键词： Cloud computing cloud service broker datacenter selection quality-of-service user request

Optimization of the Physical Aperture of the Parabolic Reflector Antenna

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Open Journal of Applied Sciences 2024年第1期14卷 182-192页

作者： Nkordeh Nsikan Akindele Ayoola Ololade Oladoyin Amadi Eberi Ibinabo Bobmanuel department of Business Analytic and Information Management University of Delaware Newark USA department of cybersecurity University of Dallas Dallas USA

In fields like astronomy and radar technology, high-gain antennas are required for long-distance communication. Due to its relatively large gain, the use of parabolic antennas has become very popular over time, because they can easily achieve gains of above 30 dB at microwave and higher frequencies. Today, most systems’ success depends on how well the antennas perform. These antennas are available in different types and sizes. Each antenna’s effective area usually has less than the actual physical area of the antenna surface. This means that the unused area of the antenna is massive, and a waste. The aim of the research is to show that the actual physical aperture of a parabolic antenna can be reduced as much as possible to equal the effective area, as given by the antenna formula, thereby saving manufacturing costs, improve the aesthetics. In other words, the focus of this work is to experimentally show that reflector antenna can be made of smaller sizes but better performance. Measurements were taken from different positions from a parabolic antenna, the signal level measured and compared with signal levels for optimal performance.

关键词： Parabolic Antenna Reception Manufacturing Cost

The Role of AI in Cyber Security: Safeguarding Digital Identity

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Journal of Information Security 2024年第2期15卷 245-278页

作者： Mohammad Binhammad Shaikha Alqaydi Azzam Othman Laila Hatim Abuljadayel department of Computer Science British University Dubai Dubai The United Arab Emirates department of cybersecurity Dar Al Hekma University Jeddah Saudi Arabia

This article signals the use of Artificial Intelligence (AI) in information security where its merits, downsides as well as unanticipated negative outcomes are noted. It considers AI based models that can strengthen or undermine infrastructural functions and organize the networks. In addition, the essay delves into AI’s role in Cyber security software development and the need for AI-resilient strategies that could anticipate and thwart AI-created vulnerabilities. The document also touched on the socioeconomic ramifications of the emergence of AI in Cyber security as well. Looking into AI and security literature, the report outlines benefits including made threat detection precision, extended security ops efficiency, and preventive security tasks. At the same time, it emphasizes the positive side of AI, but it also shows potential limitations such as data bias, lack of interpretability, ethical concerns, and security flaws. The work similarly focuses on the characterized of misuse and sophisticated cyberattacks. The research suggests ways to diminish AI-generating maleficence which comprise ethical AI development, robust safety measures and constant audits and updates. With regard to the AI application in Cyber security, there are both pros and cons in terms of socio-economic issues, for example, job displacement, economic growth and the change in the required workforce skills.

关键词： Artificial Intelligence Cyber Attack Cyber Security Real-Time Mitigation Social Media Security AI-Driven Threat Intelligence

Optimal monitoring and attack detection of networks modeled by Bayesian attack graphs

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cybersecurity 2024年第1期7卷 1-15页

作者： Armita Kazeminajafabadi Mahdi Imani department of Electrical and Computer Engineering Northeastern UniversityBostonMAUSA

Early attack detection is essential to ensure the security of complex networks,especially those in critical infrastructures.This is particularly crucial in networks with multi-stage attacks,where multiple nodes are connected to external sources,through which attacks could enter and quickly spread to other network elements.Bayesian attack graphs(BAGs)are powerful models for security risk assessment and mitigation in complex networks,which provide the probabilistic model of attackers’behavior and attack progression in the network.Most attack detection techniques developed for BAGs rely on the assumption that network compromises will be detected through routine monitoring,which is unrealistic given the ever-growing complexity of threats.This paper derives the optimal minimum mean square error(MMSE)attack detection and monitoring policy for the most general form of BAGs.By exploiting the structure of BAGs and their partial and imperfect monitoring capacity,the proposed detection policy achieves the MMSE optimality possible only for linear-Gaussian state space models using Kalman filtering.An adaptive resource monitoring policy is also introduced for monitoring nodes if the expected predictive error exceeds a user-defined value.Exact and efficient matrix-form computations of the proposed policies are provided,and their high performance is demonstrated in terms of the accuracy of attack detection and the most efficient use of available resources using synthetic Bayesian attack graphs with different topologies.

关键词： Multi-stage attacks Bayesian attack graph Attack detection Optimal monitoring

An efficient permutation approach for SbPN-based symmetric block ciphers

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cybersecurity 2024年第2期7卷 113-130页

作者： Mir Nazish M.Tariq Banday Insha Syed Sheena Banday department of Electronics and Instrumentation Technology University ofKashmirSrinagar 190006India

It is challenging to devise lightweight cryptographic primitives efficient in both hardware and software that can provide an optimum level of security to diverse Internet of Things applications running on low-end constrained devices.Therefore,an effcient hardware design approach that requires some specific hardware resource may not be effcient if implemented in software.Substitution bit Permutation Network based ciphers such as PRESENT and GIFT are effcient,lightweight cryptographic hardware design approaches.These ciphers introduce confusion and diffu-sion by employing a 4×4 static substitution box and bit permutations.The bit-wise permutation is realised by sim-ple rerouting,which is most cost-effective to implement in hardware,resulting in negligible power consumption.However,this method is highly resource-consuming in software,particularly for large block-sized ciphers,with each single-bit permutation requiring multiple sub-operations.This paper proposes a novel software-based design approach for permutation operation in Substitution bit Permutation Network based ciphers using a bit-banding fea-ture.The conventional permutation using bit rotation and the proposed approach have been implemented,analysed and compared for GIFT and PRESENT ciphers on ARM Cortex-M3-based LPC1768 development platform with KEIL MDK used as an Integrated Development Environment.The real-time performance comparison between conven-tional and the proposed approaches in terms of memory(RAM/ROM)footprint,power,energy and execution time has been carried out using ULINKpro and ULINKplus debug adapters for various code and speed optimisation sce-narios.The proposed approach substantially reduces execution time,energy and power consumption for both PRE-SENT and GIFT ciphers,thus demonstrating the effciency of the proposed method for Substitution bit Permutation Network based symmetric block ciphers.

关键词： Lightweight cryptography PRESENT GIFT SbPN Cortex-M LPC1768 Bit-band memory

A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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Computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali department of Computer Applications Karpagam College of EngineeringCoimbatore641032India department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

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Computers, Materials & Continua 2024年第4期79卷 1419-1439页

作者： Khaled Soliman Mohamed Sobh Ayman M.Bahaa-Eldin department of Computer and Systems Engineering Ain Shams UniversityCairo11517Egypt department of Computer Engineering Technology ElSewedy University of TechnologyCairo44629Egypt

The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.

关键词： Portable executable malware intrusion detection cybersecurity zero-day threats Host IntrusionDetection System(HIDS) machine learning Anomaly-based Intrusion Detection System(AIDS) deep learning