BLOCKCHAIN TECHNOLOGY'S ROLE IN SECURING DATA AND PREVENTING CYBERATTACKS: A DETAILED REVIEW

This systematic review examines the role of blockchain technology in enhancing data security and preventing cyberattacks across various sectors. Blockchain's decentralized and immutable ledger system, secured through cryptographic mechanisms like cryptographic hashes and asymmetric encryption, offers robust protection against unauthorized access and data tampering. The study highlights blockchain's ability to maintain data integrity and immutability, essential for applications requiring high levels of trust, such as financial transactions and healthcare records. The review also emphasizes the significance of smart contracts, which automate and enforce contract terms, thereby reducing human error and fraud. Sector-specific applications in finance, healthcare, supply chain management, and the Internet of Things demonstrate blockchain's versatility in addressing diverse security challenges. However, significant challenges, including scalability issues, high energy consumption, and regulatory and legal hurdles, impede the widespread adoption of blockchain technology. Addressing these challenges is crucial for realizing blockchain's full potential in cybersecurity. This review underscores the need for continued research and development to overcome these obstacles and fully harness blockchain's capabilities in securing data and preventing cyberattacks. &nbsp

CYBERSECURITY SOLUTIONS AND PRACTICES: FIREWALLS, INTRUSION DETECTION/PREVENTION, ENCRYPTION, MULTI-FACTOR AUTHENTICATION

In today's digitally interconnected world, cybersecurity is paramount for protecting sensitive information from sophisticated threats. This literature review examines four key cybersecurity solutions—firewalls, intrusion detection and prevention systems (IDPS), encryption, and multi-factor authentication (MFA)—highlighting their roles, advancements, and challenges based on 105 articles. Firewalls (n=35), including packet-filtering, stateful inspection, proxy, and next-generation firewalls (NGFWs), act as barriers controlling network traffic. NGFWs integrate deep packet inspection and application awareness, enhancing security despite complex maintenance issues. IDPS technologies (n=30) have evolved from anomaly detection to AI-integrated systems, improving threat detection while facing false-positive rates and zero-day exploit challenges. Encryption (n=25) ensures data confidentiality, progressing from basic ciphers to algorithms like AES and post-quantum cryptography, though it grapples with computational and key management complexities. MFA (n=15) enhances security through multiple verification factors, evolving from passwords to biometrics and behavioral analytics, yet faces user inconvenience and potential bypass methods. A comparative analysis reveals that firewalls and IDPS effectively prevent and detect threats but require meticulous management; encryption demands efficient key management; and MFA strengthens authentication but may encounter user resistance. Integrating these solutions within a layered security framework provides comprehensive protection, leveraging their strengths for a resilient security posture. Case studies affirm that multi-layered security approaches reduce breaches, underscoring the effectiveness of integrated cybersecurity practices. Continuous innovation, user education, and adaptive management are vital for addressing dynamic cyber threats, reinforcing the need for a robust, multi-faceted cybersecurity strategy.

A COMPREHENSIVE REVIEW OF MACHINE LEARNING AND DEEP LEARNING APPLICATIONS IN CYBERSECURITY: AN INTERDISCIPLINARY APPROACH

Cybersecurity is increasingly becoming a critical concern as the complexity and frequency of cyber-attacks continue to rise. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to enhance cybersecurity systems, offering dynamic capabilities in real-time threat detection, anomaly detection, and intrusion prevention. This article (45) presents a systematic review of the applications of ML and DL in cybersecurity, adhering to the PRISMA guidelines. The review covers several key domains, including network security, cloud security, and Internet of Things (IoT) security, highlighting how ML/DL models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) outperform traditional rule-based systems. It also addresses challenges such as adversarial attacks, data privacy concerns, and the computational resource demands of DL models. Current solutions like adversarial training, federated learning, and model optimization techniques are examined for their potential to mitigate these issues. The findings suggest that while ML/DL technologies hold great promise, further research and innovation are necessary to overcome the inherent challenges, ensuring that these systems can be deployed effectively and securely in real-world environments

A REVIEW OF MACHINE LEARNING AND FEATURE SELECTION TECHNIQUES FOR CYBERSECURITY ATTACK DETECTION WITH A FOCUS ON DDOS ATTACKS

This study provides a systematic review of machine learning (ML) techniques applied in intrusion detection systems (IDS), with a particular focus on Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Following the PRISMA guidelines, a comprehensive search of relevant databases identified 205 articles, from which 68 were selected for detailed analysis. The findings highlight that RF consistently outperforms other models, achieving accuracy rates as high as 99.72% in detecting Distributed Denial of Service (DDoS) attacks due to its ensemble learning approach. SVM, while effective in specific scenarios with binary classification tasks, struggles with scalability and high-dimensional datasets, though feature selection significantly improves its performance. DT models, known for their simplicity and interpretability, are prone to overfitting, but this issue is mitigated when combined with feature selection techniques. The study further emphasizes the importance of feature selection in enhancing IDS accuracy and efficiency across various models. Additionally, ensemble and hybrid methods, which combine multiple ML techniques, offer promising improvements in detection accuracy and real-time performance. These findings underscore the potential of machine learning, particularly through the use of ensemble and hybrid approaches, to significantly improve cybersecurity measures in modern networks. &nbsp

Impact of Dosimetric Compromises on Early Outcomes of Chordomas and Chondrosarcomas Treated With Image-guided Pencil Beam Scanning Proton Beam Therapy

Purpose: To critically review the clinical factors, dosimetry, and their correlation with early outcomes in patients with chordomas and chondrosarcomas treated with pencil beam scanning (PBS) proton beam therapy (PBT). Methods and Materials: Consecutive 64 patients diagnosed with chordoma or chondrosarcoma treated at our center were studied. Patient, tumor, and treatment-related factors including dosimetry were captured. Early and late toxicities and early outcomes were evaluated and correlated with clinical and dosimetric factors using standard statistical tools. Results: The median age of patients was 39 years (range, 4-74 years), and most common site was skull base (47%), followed by sacrum (31%) and mobile spine (22%). The median prescription dose to the high-risk clinical target volumes for chordoma and chondrosarcoma was 70.4 cobalt gray equivalent (CGE) and 66 CGE at 2.2 CGE per fraction, respectively. At presentation, 55% presented after a recurrence/progression of which 17% had received previous radiation and 32% had a significant neural compression. At the time of PBT, 25% of patients had suboptimal neural separation. Three-fourths of patients had at least an acceptable target coverage. Although 11% had a tier 1 compromise (gross tumor volume [GTV] D98 25 cm3 and a tier 2 compromise were associated with inferior local control (hazard ratio [HR], 0.19; P = .019; HR, 0.061; P = .022, respectively) and progression-free survival (HR, 0.128; P = 0.014; HR, 0.194; P =.025, respectively) on multivariate analysis. Despite multiple surgeries, a majority presented with recurrent disease and previous radiations and grade 3 acute and late toxicities were limited and comparable with others in the literature. Conclusions: Despite multiple surgeries, adequate neural separation was challenging to achieve. Severe dosimetric compromise (GTV D98 < 59 CGE) led to inferior early outcomes. Adequate neural separation is key to avoiding dosimetric compromise and achieving optimal local control