Attack Vectors Research Articles

Securing the Chain: Comprehensive Analysis of Vulnerabilities and Defense Mechanisms in Blockchain Systems

Blockchain systems, characterized by decentralization, traceability, and robust security, are increasingly adopted across various domains including finance due to their strong disaster recovery capabilities. This paper focuses on the security mechanisms underpinning Bitcoin and Ethereum, two dominant cryptocurrencies, and examines potential vulnerabilities and attack vectors targeting blockchain technologies. Specifically, it addresses attacks on consensus mechanisms, network structures, and application layers, outlining the cryptographic methods integral to blockchain security. The study delves into the types of attacks that exploit these vulnerabilities, such as forking, stale or orphaned blocks, selfish mining, and majority attacks, which threaten the integrity and consistency of blockchain systems. Additionally, it reviews the current defense mechanisms and proposes new strategies to enhance the security and resilience of these digital platforms. By exploring these security challenges comprehensively, the paper aims to provide deeper insights into the defense strategies that can fortify blockchain against such vulnerabilities, thereby enhancing its reliability and trustworthiness in digital transactions and ensuring its sustainable integration into the digital economy.

Comprehensive Survey of Web Security Threats in 2024

In recent years, the landscape of web security threats has evolved rapidly, driven by advancements in technology and increasingly sophisticated attack vectors. This paper presents a comprehensive survey of prominent web security threats in 2024, examining both traditional vulnerabilities and emerging risks that have intensified in the digital environment. We explore a range of threats, including but not limited to application-layer attacks, network- layer intrusions, and data privacy concerns. The study draws upon a wealth of sources, including industry reports, academic research, and standards from leading organizations. Our findings underscore the critical need for web application developers, security professionals, and organizations to adopt proactive defense mechanisms and stay informed on current threats to effectively protect web-based assets. This survey aims to serve as a reference for security practitioners and researchers, highlighting essential vulnerabilities and encouraging further exploration of effective countermeasures. Keywords Web Security, Threat Detection, Data Privacy, Cross-Site Scripting (XSS), SQL Injection, DDoS Attacks, Zero-Trust Architecture, Quantum-Resistant Encryption, Cloud Security, Machine Learning in Security

F3: Fast and Flexible Network Telemetry with an FPGA coprocessor

Traffic monitoring in the dataplane is vital for reacting to network events such as microbursts, incast, and attacks. However, current solutions are constrained by the limited resources available on modern ASICs and don't provide the flexibility required to identify repeating patterns, such as applications whose flows communicate with a server at regular intervals. While such flexibility can be achieved using a co-processing CPU, it is generally too slow to provide insights quickly enough. In this paper, we show how an FPGA co-processor placed alongside the switching pipeline enables flexible traffic monitoring at data plane rates. While FPGAs have large memory and expressive processing, their throughput is significantly lower than switch ASICs. To bridge the throughput gap, we split query execution between the switch and FPGA and present methods that prevent processing all packets in FPGA. As a result, our system misses up to 5.0x fewer DDoS attack vectors than ACC-Turbo, the state-of-the-art on-switch solution, and up to 24% fewer microburst-contributing flows for the same precision rate.

Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security

The Web of Things (WoT) represents a significant advancement on the Internet of Things (IoT), where web technologies are integrated to enhance device interoperability and accessibility. While this integration offers numerous benefits, it also introduces new and complex security challenges. This paper presents a critical analysis of WoT security, examining the ecosystem’s vulnerabilities and associated threats. Our contributions include an in-depth analysis of existing threat enumeration methodologies, highlighting misconceptions and inefficiencies that may weaken security measures. We further conduct a comprehensive survey of critical threats within the WoT environment, detailing potential attack vectors and misuses linked to these threats. To address these security gaps, we propose a set of defenses tailored to each identified threat, providing a holistic view of the WoT’s security landscape. We also develop abstract architectural models of the WoT using UML, serving as foundational tools for understanding the interactions and risks within WoT systems. Finally, we model a specific attack scenario, demonstrating how attacks unfold in real-world WoT environments and the importance of defense strategies. These findings aim to guide the development of secure WoT systems, ensuring robust defenses against evolving security threats.

Data-Sharing System with Attribute-Based Encryption in Blockchain and Privacy Computing

With the development of the data-sharing system in recent years, financial management systems and their privacy have sparked great interest. Existing financial data-sharing systems store metadata, which include a hash value and database index on the blockchain, and store high-capacity actual data in the center database. However, current data-sharing systems largely depend on centralized systems, which are susceptible to distributed denial-of-service (DDoS) attacks and present a centralized attack vector. Furthermore, storing data in a local center database has a high risk of information disclosure and tampering. In this paper, we propose the ChainMaker Privacy Computing (CPC) system, a new decentralized data-sharing system for secure financial data, to solve this problem. It provides a series of financial data information and a data structure rather than actual data on the blockchain to protect the privacy of data. We utilize a smart contract to establish a trusted platform for the local database to obtain encrypted data. We design a resource catalog to provide a trusted environment of data usage in the privacy computing system that is visible for members on the blockchain. Based on cipher-policy attribute-based encryption (CP-ABE), We design a CPC-CP-ABE algorithm to enable fine-grained access control through attribute based encryption. Finally, We propose an efficient scheme that allows authenticated data-sharing systems to perform Boolean searches on encrypted data information. The results of experiment show that the CPC system can finish trusted data sharing to all organizations on the blockchain.

Electromagnetic side-channel attack risk assessment on a practical quantum-key-distribution receiver based on multi-class classification

While quantum key distribution (QKD) is a theoretically secure way of growing quantum-safe encryption keys, many practical implementations are challenged due to various open attack vectors, resulting in many variations of QKD protocols. Side channels are one such vector that allows a passive or active eavesdropper to obtain QKD information leaked through practical devices. This paper assesses the feasibility and implications of extracting the raw secret key from far-field radiated emissions from the single-photon avalanche diodes used in a BB84 QKD quad-detector receiver. Enhancement of the attack was also demonstrated through the use of deep-learning model to distinguish radiated emissions due to the four polarized encoding states. To evaluate the severity of such side-channel attack, multi-class classification based on raw-data and pre-processed data is implemented and assessed. Results show that classifiers based on both raw-data and pre-processed features can discern variations of the electromagnetic emissions caused by specific orientations of the detectors within the receiver with an accuracy higher than 90%. This research proposes machine learning models as a technique to assess EM information leakage risk of QKD and highlights the feasibility of side-channel attacks in the far-field region, further emphasizing the need to utilise mechanisms to avoid electromagnetic radiation information leaks and measurement-device-independent QKD protocols.

Security Challenges in Energy Flexibility Markets: A Threat Modelling-Based Cyber-Security Analysis

Flexibility markets are crucial for balancing the decentralised and renewable-driven energy landscape. This paper presents a security evaluation of a flexibility market system using a threat modelling approach. A reference architecture for a typical flexibility market system is proposed, and attack graph-driven simulations are performed to analyse potential attack pathways where malicious actors might infiltrate the system and the vulnerabilities they might exploit. Key findings include the identification of high-risk areas, such as the Internet links between market actors. To mitigate these risks, the paper proposes and evaluates multiple protection scenarios in reducing the identified attack vectors. The findings underline the importance of multi-layered security strategies to safeguard flexibility markets from increasingly sophisticated cyber threats.

Remote side-channel analysis of the loop PUF using a TDC-based voltage sensor

FPGAs promise significant performance improvements for several computations in cloud applications. However, their shared use in multi-tenant scenarios makes them susceptible to attacks. Different from classical scenarios, where the attacker has hardware access to the device, in cloud scenarios the attack is possible only from remote. As a consequence remote SCA attacks gained increasing attention in the last years. These attacks exploit that attacker and victim share with the same FPGA also the same PDN so that operation-dependent voltage fluctuations caused by the victim’s IP are observable by a voltage sensor of the attacker. While previous attacks in this domain focused on cryptographic algorithms, this work provides insights in an attack on a PUF primitive, the Loop PUF. This primitive is an interesting target for the attack since it might be used, e.g., for storing or deriving secret keys on a remote FPGA. We introduce and discuss the setup of such a remote SCA using a TDC-based voltage sensor. With this sensor, we compare the performance of classical and remote SCA attacks on the Loop PUF using two different Artix-7 FPGA and demonstrate and discuss findings regarding sampling frequency and placement. This work extends and deepens the analysis from a previously published analysis at the ASHES Workshop 2022. In particular, it provides insights into the influence of repeated measurements, measurement time, and usage of multiple TDCs on the attack performance. It also discusses the applicability of the attack to further RO-based PUF primitives. Overall the results show that remote SCA attacks on PUFs in a multi-tenant FPGA scenario have to be considered as a severe attack vector in the future.

THE FIFTH WAR GENERATION AND ITS GEOSTRATEGIC IMPLICATIONS ON THE BLACK SEA SECURITY DIMENSION: NEW TRENDS, NEW TENDENCIES

Abstract: paper considers and reflects new dimensions of New Cold War geostrategic balance implications at pan-regional (European) and regional (Black Sea) levels. Currently adopted new challenges in the military doctrine of the Russian Federation envisages engagement into two military conflicts simultaneously and constructed its strategic operational framework founded on incorporation of three combat elements: military, non-military and information warfare measures and proper concepts. Hence, it means that Russia can begin war-game scenarios at the same time against two neighboring nations as it occurs at time being against Ukraine and Georgia. A transformation of war strategy and geostrategic culture (it is a new jargon affiliated with cardinal shifting of strategic operational planning in wargame scenarios at global and regional levels). Hence, the process of geostrategic culture transformation occurring currently whilst introducing Fifth War Generation concept replacing the previous one due to the high technical achievements and technological introduction into the neo-modernist war dynamics itself. Generally to say, a fifth generation warfare is an attempt to accomplish strategic objectives through the use of propaganda and information attack vectors. It's carried out by unknown actors for unknown reasons. Even if the core enemy is identified, the victim nation will not be able to understand the purpose or end goal. The goal of the paper is to realize how the geostrategic cultural transformation takes place at regional level and reflection on what type of influence high-level technologies effect on war process itself.

Pattern Analysis of Cyber Crime Incidents to Predict Occurrence and Selection of The Best Technology to Prevent IT

Cybercrime has become a significant global threat, affecting individuals, organizations, and governments alike. This research paper focuses on the pattern analysis of cyber crime incidents and aims to develop a predictive model to forecast its occurrence while selecting the most effective technology for prevention. Through an extensive literature review, we analyze existing research on cybercrime patterns, predictive modeling, and technologies for prevention. The study identifies the gaps in the current literature, emphasizing the need for a comprehensive approach that combines data-driven analysis with advanced cybersecurity technologies. The research methodology encompasses data collection from diverse sources, followed by rigorous data preprocessing and cleaning to ensure data quality. Machine learning algorithms and statistical methods are utilized for developing the predictive model. Evaluation metrics are employed to measure the model's performance and its ability to predict cybercrime incidents accurately. The pattern analysis of cybercrime incidents reveals various attack vectors, target sectors, and geographical distributions, providing crucial insights into the modus operandi of cybercriminals. This analysis forms the basis for building the predictive model, which can assist law enforcement agencies and cybersecurity professionals in anticipating and preventing cybercrime effectively. The findings of this research contribute significantly to the field of cybercrime prevention. The developed predictive model enhances early warning capabilities, enabling proactive measures against cyber threats. Additionally, the technology selection framework assists organizations in making informed decisions regarding cybersecurity investments.

A Survey of AI-Powered Proactive Threat-Hunting Techniques: Challenges and Future Directions

The study narrates the dimensions of AI-powered threat hunting techniques that keep changing and draws on their importance in cybersecurity measures while dealing with advanced cyber threats. While the cyber adversary is continuously adapting and innovating, the integration of advanced AI algorithms, including reinforcement learning and hybrid models, has become one of the key strategies for real-time threat detection and response. The research investigates the transformative potential of these algorithms to facilitate adaptive systems that can quickly recognize and act on new attack vectors. It also discusses the integration of AI with emergent technologies like blockchain, quantum computing, and IoT in the dimension of their potential to leverage threat intelligence and response mechanisms. This would naturally lead to increased efficiency in threat hunting by leveraging the strengths brought into this endeavor by both the AI systems and human analysts. The study also advocates for the development of strong policy and governance frameworks to drive home some ethical concerns in deploying AI in cybersecurity. Such a framework is very important for addressing challenges on data privacy, accountability, and transparency. The findings bring into sharp relief that, in this continuing evolution of the cybersecurity landscape, what will work best is a proactive stance-a proactive stance that incorporates innovative technologies with human ingenuity and ethics. This white paper broadly documents the considerable promise of AI in threat hunting and strategic advances needed to harden organizational defenses against a dynamic threat environment.

Anomaly detection system for ADS-B data: Attack vectors and machine learning models

Anomaly detection system for ADS-B data: Attack vectors and machine learning models

SolarWinds Attack: Stages, Implications, and Mitigation Strategies in the Cyber Age

SolarWinds is a software company based in the United States that provides IT monitoring and management tools. Founded in 1999, SolarWinds offers a variety of products that help organizations manage networks, systems, IT infrastructure, applications and cloud-based services. SolarWinds products are used for performance monitoring, log management, IT security, and data analysis. The company became widely known after a major cybersecurity incident came to light in late 2020, in which their network management software, Orion, was used as a vector for attacks by a state-backed hacking group. These attacks affected many organizations, including government agencies and private companies, and led to an increased focus on software supply chain security. This paper has reviewed stages, Implications, and mitigation strategies of SolarWinds.

Optimizing Machine Learning-Based Intrusion Detection Systems for Enhanced Security in Cloud Computing Environments.

In today’s cloud computing environments, robust network security is crucial due to the inherently distributed and dynamic nature of cloud systems. Traditional Network Intrusion Detection Systems (NIDS) often face challenges in handling large-scale, rapidly evolving data and sophisticated attack vectors unique to cloud settings. This paper presents an optimized Machine Learning (ML)-based NIDS framework designed specifically for cloud infrastructures, leveraging feature selection, dimensionality reduction, and algorithmic tuning techniques to enhance detection accuracy and reduce false positives. Integrating supervised and unsupervised learning methods, this NIDS system can detect both known and novel attack types efficiently. By utilizing cloud-specific datasets and real-time traffic monitoring, it adapts well to the scalability and elasticity requirements of cloud environments. Additionally, the system incorporates automated optimization techniques, such as hyperparameter tuning and ensemble learning, to further enhance performance. Experimental results show improvements in detection rates, reduced computational overhead, and better scalability, making it a promising solution for modern cloud-based infrastructures.

Explainable AI-based innovative hybrid ensemble model for intrusion detection

AbstractCybersecurity threats have become more worldly, demanding advanced detection mechanisms with the exponential growth in digital data and network services. Intrusion Detection Systems (IDSs) are crucial in identifying illegitimate access or anomalous behaviour within computer network systems, consequently opposing sensitive information. Traditional IDS approaches often struggle with high false positive rates and the ability to adapt embryonic attack patterns. This work asserts a novel Hybrid Adaptive Ensemble for Intrusion Detection (HAEnID), an innovative and powerful method to enhance intrusion detection, different from the conventional techniques. HAEnID is composed of a string of multi-layered ensemble, which consists of a Stacking Ensemble (SEM), a Bayesian Model Averaging (BMA), and a Conditional Ensemble method (CEM). HAEnID combines the best of these three ensemble techniques for ultimate success in detection with a considerable cut in false alarms. A key feature of HAEnID is an adaptive mechanism that allows ensemble components to change over time as network traffic patterns vary and new threats appear. This way, HAEnID would provide adequate protection as attack vectors change. Furthermore, the model would become more interpretable and explainable using Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). The proposed Ensemble model for intrusion detection on CIC-IDS 2017 achieves excellent accuracy (97-98%), demonstrating effectiveness and consistency across various configurations. Feature selection further enhances performance, with BMA-M (20) reaching 98.79% accuracy. These results highlight the potential of the ensemble model for accurate and reliable intrusion detection and, hence, is a state-of-the-art choice for accuracy and explainability.

Adaptive Cybersecurity Neural Networks: An Evolutionary Approach for Enhanced Attack Detection and Classification

The increasing sophistication and frequency of cyber threats necessitate the development of advanced techniques for detecting and mitigating attacks. This paper introduces a novel cybersecurity-focused Multi-Layer Perceptron (MLP) trainer that utilizes evolutionary computation methods, specifically tailored to improve the training process of neural networks in the cybersecurity domain. The proposed trainer dynamically optimizes the MLP’s weights and biases, enhancing its accuracy and robustness in defending against various attack vectors. To evaluate its effectiveness, the trainer was tested on five widely recognized security-related datasets: NSL-KDD, CICIDS2017, UNSW-NB15, Bot-IoT, and CSE-CIC-IDS2018. Its performance was compared with several state-of-the-art optimization algorithms, including Cybersecurity Chimp, CPO, ROA, WOA, MFO, WSO, SHIO, ZOA, DOA, and HHO. The results demonstrated that the proposed trainer consistently outperformed the other algorithms, achieving the lowest Mean Square Error (MSE) and highest classification accuracy across all datasets. Notably, the trainer reached a classification rate of 99.5% on the Bot-IoT dataset and 98.8% on the CSE-CIC-IDS2018 dataset, underscoring its effectiveness in detecting and classifying diverse cyber threats.

Hardware Security Breaches: Alarming Exploits Beyond Tampering

Side Channel Attacks (SCAs) and Fault Injection Attacks have emerged as significant threats to the security of electronic devices. This paper explores these hardware attack vectors in the context of recent incidents [11] involving pagers and walkie-talkies. We shall deep dive into the possible mechanisms of these attacks, how they can be combined for increased efficacy, and their implications for device security. This paper discusses only the possible ways [1] the adversaries could have exploited and may include other attack vectors such as supply chain attack and few others. The aim of this paper as we discuss the potential attack mechanisms is only to focus on mitigations and awareness of the reader to perform advanced security testing.

Dynamic optimization algorithms for enhancing blockchain network resilience against distributed attacks

This research introduces a dynamic optimization algorithm designed to enhance blockchain network resilience against distributed attacks such as Distributed Denial of Service (DDoS), Sybil, and eclipse attacks. The primary objective is to develop a real-time, adaptive control strategy that minimizes network performance degradation while dynamically responding to evolving threats. The research design integrates multi-objective optimization, game theory, and reinforcement learning to formulate a defense strategy that adapts to adversarial conditions. The methodology is based on a modified state-space model, where the blockchain's performance is represented by a system of dynamic equations influenced by both control actions (defensive measures) and attack vectors. The optimization problem is formulated to minimize a cost function that balances network resilience and resource usage. A numerical example is presented to validate the model, demonstrating the algorithm’s effectiveness in maintaining network performance under attack by adjusting defense mechanisms in real-time. The main results indicate that the proposed method significantly reduces the impact of distributed attacks while ensuring efficient resource allocation. In conclusion, this research offers a novel framework for enhancing blockchain security, with implications for real-world applications in decentralized systems, financial services, and critical infrastructure. Future work will address the scalability of the algorithm and explore more advanced reinforcement learning techniques to handle more complex and unpredictable attack patterns.

Mitigating Complex Cyber Threats: An Integrated Multimodal Deep Learning Framework for Enhanced Security

Abstract: The rapidly evolving landscape of cyber threats poses significant challenges to traditional cybersecurity methods, particularly in detecting and mitigating complex attacks that combine multiple data modalities. This article introduces a novel approach utilizing Multimodal Deep Learning (MDL) to enhance cybersecurity detection and prevention capabilities. By fusing Natural Language Processing (NLP) and computer vision techniques, our proposed MDL framework demonstrates superior performance in analyzing both textual and visual data associated with potential threats. Through a series of experiments and case studies, we show that this integrated approach significantly improves detection accuracy, reduces false positives, and exhibits remarkable adaptability to emerging attack vectors. Our results indicate a 37% increase in threat detection efficiency and a 42% reduction in false positives compared to traditional unimodal methods. Furthermore, we explore the potential applications of this technology in email security, social media monitoring, and advanced malware detection. This article not only contributes to the growing body of knowledge in AI-driven cybersecurity but also provides a roadmap for future developments, including the integration of audio analysis and the application of Explainable AI (XAI) to enhance trust and transparency in MDL-based security systems.

Security Considerations for Large Language Model Use: Implementation Research in Securing LLM-Integrated Applications

Large Language Models (LLMs) are rapidly being adopted in various applications due to their natural language capabilities that enable user interaction using human language. As system designers, developers, and users embrace generative artificial intelligence and large language models in various applications, they need to understand the significant security risks associated with them. The paper describes a typical LLM-integrated application architecture and identifies multiple security risks to address while building these applications. In addition, the paper provides guidance on potential mitigations to consider in this rapidly evolving space to help protect systems and users from potential attack vectors. This paper presents the common real-world application patterns of LLMs trending today. It also provides a background on generative artificial intelligence and related fields.