Sophisticated Attacks Research Articles

Machine Learning Meets Cybersecurity

In an era where digital transformation is accelerating, the integration of machine learning (ML) into cybersecurity is becoming increasingly crucial. This article delves into the significant role that ML plays in enhancing cybersecurity measures. ML's ability to process and analyze vast amounts of data at unprecedented speeds allows for more efficient threat detection, anomaly identification, and predictive analytics. These capabilities are essential in pre-empting and mitigating cyber threats in real-time. However, the same technologies that bolster defences are also being leveraged by cybercriminals to develop more sophisticated attacks, posing unique challenges. This article explores the dual-edged nature of ML in cybersecurity, highlighting both its benefits and the potential risks. It discusses the challenges of bias in ML algorithms, the vulnerabilities exposed through adversarial attacks, and the ethical and privacy considerations that come with deploying ML solutions. Furthermore, it provides insights into the future trends and developments in ML-driven cybersecurity and underscores the importance of synergizing human expertise with machine intelligence to build more resilient cybersecurity frameworks. Through this exploration, the article aims to provide a comprehensive understanding of the transformative impact of ML on cybersecurity and the path forward in addressing the accompanying challenges.

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.

Machine Learning in Cybersecurity: A Comprehensive Analysis of Intrusion Detection Systems

Cyber threats are getting more complex and larger, and traditional security measures can't keep up with the sophisticated attacks. Machine Learning (ML) has emerged as a powerful tool in enhancing cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). a comprehensive analysis of the role of machine learning in intrusion detection, focusing on its ability to detect and respond to both known and unknown threats in real-time. various machine learning techniques, including supervised, unsupervised, and deep learning models, and evaluates their effectiveness in identifying anomalies and preventing security breaches. Key performance metrics such as accuracy, false positive rates, and scalability are analyzed, with a focus on the advantages and limitations of each approach. Additionally, the paper explores the challenges associated with implementing ML-based IDS, including data quality, adversarial attacks, and computational requirements. Through case studies and real-world examples, this paper highlights how machine learning is revolutionizing intrusion detection and presents insights into future advancements that will further strengthen cybersecurity defenses.

Towards Ensemble Feature Selection for Lightweight Intrusion Detection in Resource-Constrained IoT Devices

The emergence of smart technologies and the wide adoption of the Internet of Things (IoT) have revolutionized various sectors, yet they have also introduced significant security challenges due to the extensive attack surface they present. In recent years, many efforts have been made to minimize the attack surface. However, most IoT devices are resource-constrained with limited processing power, memory storage, and energy sources. Such devices lack the sufficient means for running existing resource-hungry security solutions, which in turn makes it challenging to secure IoT networks from sophisticated attacks. Feature Selection (FS) approaches in Machine Learning enabled Intrusion Detection Systems (IDS) have gained considerable attention in recent years for having the potential to detect sophisticated cyber-attacks while adhering to the resource limitations issues in IoT networks. Apropos of that, several researchers proposed FS-enabled IDS for IoT networks with a focus on lightweight security solutions. This work presents a comprehensive study discussing FS-enabled lightweight IDS tailored for resource-constrained IoT devices, with a special focus on the emerging Ensemble Feature Selection (EFS) techniques, portraying a new direction for the research community to inspect. The research aims to pave the way for the effective design of futuristic FS/EFS-enabled lightweight IDS for IoT networks, addressing the critical need for robust security measures in the face of resource limitations.

Secured Cyber-Internet Security in Intrusion Detection with Machine Learning Techniques

The rapid proliferation of Internet-connected devices has elevated the significance of cybersecurity, making intrusion detection a critical aspect of maintaining network integrity. Traditional security measures often fail to provide adequate protection against sophisticated attacks, necessitating advanced and robust solutions. This paper introduces a comprehensive cyber-internet security framework that leverages machine learning techniques for real-time intrusion detection and prevention. The proposed methodology employs a hybrid approach, integrating supervised and unsupervised learning models to detect anomalies and classify intrusions effectively. Specifically, a combination of Support Vector Machine (SVM), Decision Trees (DT), and K-means clustering is used to enhance detection accuracy and reduce false-positive rates. The experimental results demonstrate that the proposed model achieved a detection accuracy of 97.8%, a precision of 96.5%, and a recall of 95.2% on the NSL-KDD dataset. The implementation also reduced the false-positive rate to 1.2% and the computational overhead by 15% compared to traditional detection systems. Additionally, the proposed system was tested on real-time traffic data, where it successfully identified and mitigated various cyber threats, including Distributed Denial of Service (DDoS) attacks and network infiltrations, with minimal latency and high reliability. In conclusion, the study presents an efficient and secured cyber-internet security framework that significantly enhances intrusion detection capabilities using machine learning techniques. The proposed system provides a scalable and adaptive solution for securing critical infrastructure and networks against evolving cyber threats, making it an ideal candidate for deployment in real-world cybersecurity applications.

Honey password vaults tolerating leakage of both personally identifiable information and passwords

Honey vaults are useful tools for password management. A vault usually contains usernames for each domain, and the corresponding passwords, encrypted with a master password chosen by the owner. By generating decoy vaults for incorrect master password attempts, honey vaults force attackers with the vault’s storage file to engage in online verification to distinguish the real vaults, thus thwarting offline guessing attacks. However, sophisticated attackers can acquire additional information, such as personally identifiable information (PII) and partial passwords contained within the vault from various data breaches. Since many users tend to incorporate PII in their passwords, attackers may utilize PII to distinguish the real vault. Furthermore, if attackers may learn partial passwords included in the real vault, it can exclude numerous decoy vaults without the need for online verification. Indeed, both leakages pose serious threats to the security of the existing honey vault schemes. In this paper, we explore two attack variants of the inspired attack scenario, where the attacker gains access to the vault’s storage file along with acquiring PII and partial passwords contained within the real vault, and design a new honey vault scheme. For security assurance, we prove that our scheme is secure against one of the aforementioned attack variants. Moreover, our experimental findings suggest enhancements in security against the other attack. In particular, to evaluate the security in multiple leakage cases where both the vault’s storage file and PII are leaked, we propose several new practical attacks (called PII-based attacks), building upon the existing practical attacks in the traditional single leakage case where only the vault’s storage file is compromised. Our experimental results demonstrate that certain PII-based attacks achieve a 63–70% accuracy in distinguishing the real vault from decoys in the best-performing honey vault scheme (Cheng et al. in Incrementally updateable honey password vaults, pp 857–874, 2021). Our scheme reduces these metrics to 41–50%, closely approaching the ideal value of 50%.

An adaptive multistage intrusion detection and prevention system in software defined networking environment

The advancements made in Software-Defined Networking (SDN) technology seem quite promising, with potential wide application in managing and controlling the latest network infrastructures. SDN technology decouples the control plane from the data plane, enabling effective and flexible network management. However, this dynamic phenomenon brings new security challenges. With the increasing dynamism and programmable nature of networks, conventional security protocols may not sufficient to protect against advanced and sophisticated attacks. Although Intrusion Detection Systems (IDSs) have been extensively applied for identifying and preventing security threats in traditional network environments, IDS models designed specifically for traditional network requirements may not be adequate for SDN environments. These issues may stem from the static nature of conventional networks, contrasting with the dynamicity of advanced SDN networks, and the traditional IDS’s inability to adapt to the dynamic nature of SDN. To address these challenges, the current research proposes a novel Deep Hybrid IDS model to enhance network security in SDN environments and prevent attacks using Scapy. The proposed model detects signature-based attacks by integrating Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) for real-time simulated datasets, achieving an accuracy of 97.8%, which is comparatively better than existing models.

Intelligent and Secure Cloud–Edge Collaborative Industrial Information Encryption Strategy Based on Credibility Assessment

As industries develop and informatization accelerates, enterprise collaboration is increasing. However, current architectures face malicious attacks, data tampering, privacy issues, and security and efficiency problems in information exchange and enterprise credibility. Additionally, the complexity of cyber threats requires integrating intelligent security measures to proactively defend against sophisticated attacks. To address these challenges, this paper introduces an intelligent and secure cloud–edge collaborative industrial information encryption strategy based on credibility assessment. The proposed strategy incorporates adaptive encryption specifically designed for cloud–edge and edge–edge architectures and utilizes attribute encryption to control access to user-downloaded data, ensuring secure information exchange. A mechanism for assessing enterprise credibility over a defined period helps maintain a trusted collaborative environment, crucial for identifying and mitigating risks from potentially malicious or unreliable entities. Furthermore, integrating intelligent threat detection and response systems enhances overall security by continuously monitoring and analyzing network traffic for anomalies. Experimental analysis evaluates the security of communication paths and examines how enterprise integrity influences collaboration outcomes. Simulation results show that this approach enhances enterprise integrity, reduces losses caused by harmful actors, and promotes efficient collaboration without compromising security. This intelligent and secure strategy not only safeguards sensitive data but also ensures the resilience and trustworthiness of the collaborative network.

Privacy Protection in Mobile Big Data: Challenges and Solutions

The pervasive use of mobile big data has profoundly altered daily life, providing unprecedented convenience and efficiency. However, with the proliferation of mobile devices and the explosive growth of data volume, the issue of user privacy protection has become increasingly severe. Location information and semantic information, as the two core components of mobile big data, can directly reflect users’ behavioral trajectories and thought dynamics, underscoring the importance of privacy protection. Although existing technologies can protect user data to a certain extent, traditional methods struggle to address increasingly sophisticated attack techniques in the face of evolving privacy threats. A comprehensive privacy protection scheme for mobile big data was proposed in this study, with a focus on two main areas: the privacy protection of location-based and semantic-based mobile big data. For location information protection, an uncertain graph model was employed to effectively resist combined attacks by jointly protecting the user layer and the location layer. For semantic information protection, a hypergraph clustering method was used to structurally protect the user layer and the semantic layer, enhancing the privacy security of semantic information. This study not only addresses existing gaps but also provides new solutions for mobile big data privacy protection, offering significant theoretical and practical value.

Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things

The Internet of Underwater Things (IoUT) represents an emerging and innovative field with the potential to revolutionize underwater exploration and monitoring. Despite its promise, IoUT faces significant challenges related to reliability and security, which hinder its development and deployment. A particularly critical issue is the establishment of trustworthy communication networks, necessitating the adaptation and enhancement of existing models from terrestrial and marine systems to address the specific requirements of IoUT. This work explores the problem of dishonest recommendations within trust modelling systems, a critical issue that undermines the integrity of trust modelling in IoUT networks. The unique environmental and operational constraints of IoUT exacerbate the severity of this issue, making current detection methods insufficient. To address this issue, a recommendation evaluation method that leverages both filtering and weighting strategies is proposed to enhance the detection of dishonest recommendations. The model introduces a filtering technique that combines outlier detection with deviation analysis to make initial decisions based on both majority outcomes and personal experiences. Additionally, a belief function is developed to weight received recommendations based on multiple criteria, including freshness, similarity, trustworthiness, and the decay of trust over time. This multifaceted weighting strategy ensures that recommendations are evaluated from different perspectives to capture deceptive acts that exploit the complex nature of IoUT to the advantage of dishonest recommenders. To validate the proposed model, extensive comparative analyses with existing trust evaluation methods are conducted. Through a series of simulations, the efficacy of the model in capturing dishonest recommendation attacks and improving the accuracy rate of detecting more sophisticated attack scenarios is demonstrated. These results highlight the potential of the model to significantly enhance the trustworthiness of IoUT establishments.

A Mathematical Modelling for Three-Factor Quantum Biometric Authentication Using Machine Learning, Double-Layer Encryption, Cryptographic Algorithms

Traditional authentication mechanisms are vulnerable to sophisticated attacks in the ever-changing cybersecurity paradigms. In this paper, a new three-factor quantum biometric system is proposed that were analysed to increase security level and reduce threats. This model employs multiple strategies, like quantum key distribution, a biometric identification system and machine learning which encrypts data based on their unique properties in an innovative way to provide not only one but two layers of encryption from unauthorized access. The system in its essence combines three critical elements: quantum generated cryptographic keys, biometric data (fingerprint or retinal scans) and dynamic behaviour-based identification using machine learning algorithms. By adding QKD to the hardware, an encryption key that should be practically impossible for anyone else (except sender and receiver) to intercept is securely generated and distributed through quantum mechanics principles of detection preventing possible eavesdropper. Secondly, the biometric data means that an imposter would have to not only steal your smartphone but also be able to authenticate themselves using unique physiology which takes even more of the likelihood out. Machine learning models study user behaviour patterns and work to adapt against evolving threats, gradually improving the accuracy of authentication with time. For added security, the system uses a double-layer encryption routine. The newly proposed system has a two-level encryption, and in the first layer, quantum keys encrypted bio-metric data while classical algorithms are applied to encrypt overall communication as well storage process. Besides ensuring the safety of private biometric information through dual encryption, this added redundancy also allows them to function as extra fail safe so that if one layer is breached, the entire system will still stand uncompromised. We evaluate the implementation of this new authentication scheme to a series-simulations and real-world test, showing the performance in different scenarios including high threat level environments. The results show major security enhancement compared to the traditional implementation of authentication methods with an observed amount decrease in data breaches and unsanctioned access attempts.

Secure impulsive tracking of multi-agent systems with directed hypergraph topologies against hybrid deception attacks

This research delves into the challenges of achieving secure consensus tracking within multi-agent systems characterized by directed hypergraph topologies, in the face of hybrid deception attacks. The hybrid discrete and continuous deception attacks are targeted at the controller communication channels and the hyperedges, respectively. To overcome these threats, an impulsive control mechanism based on hypergraph theory are introduced, and sufficient conditions are established, under which consensus can be maintained in a mean-square bounded sense, supported by rigorous mathematical proofs. Furthermore, the investigation quantifies the relationship between the mean-square bounded consensus of the multi-agent system and the intensity of the deception attacks, delineating a specific range for this error metric. The robustness and effectiveness of the proposed control method are verified through comprehensive simulation experiments, demonstrating its applicability in varied scenarios influenced by these sophisticated attacks. This study underscores the potential of hypergraph-based strategies in enhancing system resilience against complex hybrid attacks.

Proactive cyber threat mitigation: Integrating data-driven insights with user-centric security protocols

In the rapidly evolving cybersecurity landscape, proactive threat mitigation has become essential for protecting sensitive data and systems from increasingly sophisticated attacks. The paper explores a holistic approach to enhancing cybersecurity by merging data-driven insights with user-centric security practices. This study highlights the limitations of traditional reactive security measures and advocates for a shift towards a proactive strategy that anticipates and addresses potential threats before they materialize. The research emphasizes the importance of leveraging data analytics to gain actionable insights into potential vulnerabilities and emerging threats. By analyzing large datasets from network traffic, user behavior, and historical incidents, organizations can identify patterns and anomalies that signal potential risks. These data-driven insights enable the development of more effective threat detection systems and predictive models that enhance an organization’s ability to respond swiftly to evolving cyber threats. In addition to data analytics, the study underscores the significance of integrating user-centric security protocols. Traditional security measures often overlook the human element, which can be a critical factor in both the efficacy of security measures and the overall risk profile. User-centric protocols focus on enhancing user awareness, implementing tailored security training, and promoting best practices in cybersecurity. This approach ensures that security measures are not only technologically sound but also aligned with user behavior and needs, reducing the risk of human error and improving overall security posture. The research also discusses practical implementation strategies for combining data-driven insights with user-centric protocols, including the use of advanced analytics tools, continuous user education, and adaptive security frameworks. Case studies demonstrate the benefits of this integrated approach, such as improved threat detection rates, reduced incident response times, and enhanced overall security resilience. In conclusion, this study presents a comprehensive framework for proactive cyber threat mitigation, emphasizing the synergy between data-driven analytics and user-centric security practices as a robust strategy for safeguarding against modern cyber threats. Keywords: Cyber Threat, Proactive, Data-Driven, User-Centric, Security Protocols

Advanced Threat Recognition: Harnessing Machine Learning for Intrusion Detection

Conventional intrusion detection systems (IDS) are frequently inadequate to combat complex cyber threats in the changing cybersecurity landscape. To create a reliable intrusion detection system, this work investigates the use of machine learning, more especially the RandomForestClassifier method. The study highlights that in order to maximize model training, careful data preprocessing—including feature selection, label encoding, and scaling—is essential. It also uses Optuna for hyperparameter adjustment to improve the classifier's performance. As a scalable and dependable response to contemporary cybersecurity issues, the findings show how effective machine learning is in identifying sophisticated attacks. The outcomes show a high detection accuracy and resilience to complex intrusions, confirming machine learning's potential as a scalable and reliable answer to today's cybersecurity problems

Exploring the nature of generative artificial intelligence in evolving cyber threats

The rapid advancement of Generative Artificial Intelligence (GenAI) has significantly impacted cybersecurity, presenting both opportunities and challenges. This study explores the evolving nature of cyber threats facilitated by GenAI, focusing on its dual role in enhancing security measures and creating sophisticated attack vectors. Through a comprehensive literature review, analyses were made on previous research focused on the applications of GenAI in cybersecurity, examining its potential to detect, prevent, and respond to threats and its vulnerabilities to exploitation by malicious actors. Utilizing qualitative research methodology, this study gathers insight from peer-reviewed articles, case studies, and expert interviews to reveal the implications of GenAI in the cybersecurity landscape. The findings reveal the complex interplay between GenAI's protective and adversarial capabilities, highlighting the need for continuous innovation and robust strategies to mitigate associated risks. The study concludes by positioning these insights within the broader context of cybersecurity and proposing directions for future research to address emerging challenges.

The Role of PASTA in Addressing Future Trends in Regulatory Compliance: Emerging Cyber Threats

The evolving landscape of cyber threats necessitates an adaptive approach to threat modeling and regulatory compliance. This paper explores the integration of the PASTA (Process for Attack Simulation and Threat Analysis) framework with emerging regulatory trends to address future cyber threats. The study examines how PASTA can be aligned with regulatory frameworks such as PCI DSS, HIPAA, GDPR, and CCPA, to enhance cybersecurity resilience and compliance. By analyzing the application of PASTA to Advanced Persistent Threats (APTs) and ransomware, the paper demonstrates how this structured methodology can help organizations effectively model, simulate, and mitigate sophisticated attacks. The integration of PASTA with regulatory requirements provides a comprehensive approach to managing cyber risks, ensuring robust protection against both current and emerging threats.

Anomaly detection technique for securing microgrid against false data attacks

The development of microgrid automation depends on information and communication technologies, which are vulnerable to cyber-attacks. Recent advancements in MGs enhance power systems' efficacy and reliability, but cybersecurity remains a significant concern, especially with false data injection attacks (FDIAs) posing serious threats. FDIAs can compromise measurement devices and tamper with State Estimation (SE), risking the seamless operation of MGs. To address this, this paper proposes an efficient Iterative Free Detection of False Data (IFDFD) scheme for detecting FDIAs in microgrid state estimation. The IFDFD scheme uses complex Micro Phasor Measurement Unit (μPMU) measurements and computes nodal power injections to detect FDIAs. Furthermore, the proposed scheme integrates an S-Estimator to eliminate noise errors caused by environmental factors and the component lifespan, making IFDFD robust against sophisticated attackers. The proposed IFDFD scheme has been tested and validated on the modified IEEE 14 bus test system, integrating Distributed Generations (DGs). False data was injected into the measurements to test the scheme's effectiveness. The efficacy of proposed IFDFD scheme has been validated by comparing it to existing method of FDIAs. The obtained result clearly validates the efficacy of the proposed IFDFD scheme.

Comprehensive review of machine learning models for sql injection detection in e-commerce

With the steady expansion of online commerce, e-commerce sites have become increasingly attractive targets for hackers. These sites serve millions of customers and often hold valuable, confidential, and sometimes financial information in their databases. One particularly dangerous type of attack is SQL injection, which exploits vulnerabilities in web applications to influence backend databases, posing significant threats to such platforms. Traditional defenses like desktop firewalls, input validation, and parameterized queries provide some level of protection but are often insufficient against newer injection variations and sophisticated attackers. The utilization of machine learning to enhance cybersecurity against more advanced threats has been demonstrated as a promising approach. This systematic review examines how various machine learning algorithms are applied to detect SQL injection attacks that could potentially harm e-commerce systems. By identifying and analyzing the relevant literature, this review highlights the effectiveness of different algorithms and their practical applications in enhancing the security of online commerce platforms. More specifically, five techniques were assessed on both real and synthetic datasets: Logistic Regression, Naive Bayes, Random Forest, Artificial Neural Network, and two combined models (Logistic Regression & Naive Bayes, and Artificial Neural Network & Random Forest). The findings indicate that Random Forest performed better than other algorithms in the decision tree family, attributed to its ability to balance precision and recall effectively. However, limitations such as using a single dataset and the computational complexity of some models were noted. This review provides insights for practitioners on selecting appropriate detection models and outlines approaches to address current limitations through future work. Addressing these limitations could involve using more diverse datasets, optimizing computational efficiency, and exploring advanced ensemble methods and neural network architectures.

Cybersecurity in the Age of AI: Protecting Our Data and Privacy in a Digital World

In the digital age, Artificial Intelligence (AI) is pivotal in enhancing cybersecurity, offering advanced capabilities to detect and mitigate cyber threats efficiently. This article delves into AI's role in strengthening cyber security, emphasizing its ability to proactively identify vulnerabilities, forecast attacks, and automate incident responses. It also addresses the challenges and ethical concerns associated with AI in cyber security, such as the potential for misuse by cybercriminals to conduct sophisticated attacks and issues related to data privacy and algorithmic bias. The piece highlights the necessity of a balanced approach to leveraging AI, advocating for collaboration among stakeholders to navigate the ethical and regulatory complexities. Ultimately, it underscores AI's indispensable role in developing resilient cyber security frameworks and fostering a secure digital environment amidst an increasingly complex threat landscape.

AIS-NIDS: An intelligent and self-sustaining network intrusion detection system

The ever-evolving landscape of network security is continually molded by the dynamic evolution of attack vectors and the relentless emergence of new, highly sophisticated attacks. Attackers consistently employ increasingly advanced techniques, rendering their actions elusive and formidable. In response to this ever-growing threat, the demand for intelligent and autonomous security systems has reached paramount importance. In this paper, we introduce AIS-NIDS (An Intelligent and Self-Sustaining Network Intrusion Detection System), an innovative network intrusion detection system (NIDS) that delves into the realm of packet-level analysis. By doing so, AIS-NIDS is capable of identifying threats with intricate payload-level details, a level of granularity that traditional NIDS relying solely on flow-level data may overlook. The defining feature of AIS-NIDS is its dual functionality, driven by autonomous and intelligent learning. It not only autonomously distinguishes between benign and unknown attacks using machine learning models but also conducts incremental learning, adapting to new attack classes. In essence, AIS-NIDS bridges the gap between traditional NIDS and the next generation of intelligent systems, endowing the system with the capacity for independent decision-making and real-time adaptability in the face of evolving threats. Our extensive experiments stand as a testament to AIS-NIDS’ ability to efficiently manage and identify new attack classes, thus establishing it as a valuable asset in the reinforcement of network infrastructures. Through our experimentation, we have demonstrated the practical efficacy of the proposed approach by simulating a real-world scenario in which certain attack classes are unknown. AIS-NIDS not only effectively identified these unknown threats but also autonomously learned to recognize them as it encountered them, enhancing the system’s capabilities for future encounters with these threats.