Cyber-Attack on P2P Energy Transaction Between Connected Electric Vehicles: A False Data Injection Detection Based Machine Learning Model

Said, Dhaou; Elloumi, Mayssa; Khoukhi, Lyes

doi:10.1109/access.2022.3182689

Cited by 32 publications

(7 citation statements)

References 22 publications

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“…This framework facilitates the creation of interconnected virtual networks for the purpose of generating and gathering flow data via the NetFlow protocol. [11] Flow termination occurs either after 15 seconds of inactivity or after 1800 seconds (30 minutes) of activity. The first dataset, denoted as D1, was utilized for training detection models, while the second dataset, denoted as D2, served for testing purposes.…”

Section: Data Gatheringmentioning

confidence: 99%

Enhanced Network Security against SQL Injection Attack Using Machine Learning

Joshi,

Oza

2024

Preprint

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SQL injection attacks pose a significant risk to the security of computer networks. These attacks have the potential to gain unauthorized access to sensitive data, modify or remove data, or even render entire websites and databases inoperable. Conventional techniques for identifying and stopping SQL injection attacks are frequently resource-intensive, rendering them unfeasible for devices that manage substantial amounts of traffic. The objective of the proposed research is to improve the identification and prevention of structured query language injection attacks (SQLIAs) in web applications, with a specific focus on attaining a high rate of detection while minimizing false alarms. We utilized flow data obtained from several SQL injection attack scenarios that specifically targeted widely-used database engines. We obtained a high level of accuracy in identifying and mitigating these assaults by utilizing machine learning methods, specifically logistic regression. The method we used showed a detection rate over 98% and a false alarm rate below 0.029%. The results demonstrate substantial enhancements in the prevention of unauthorized access and the protection of sensitive information within databases. The results of our research align with or surpass current techniques, offering enhanced security for web applications. The model’s exceptional accuracy and minimal false alarm rate provide a groundbreaking method for detecting SQL injection attacks.

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Section: Data Gatheringmentioning

confidence: 99%

Enhanced Network Security against SQL Injection Attack Using Machine Learning

Joshi,

Oza

2024

Preprint

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“…Because of the abovementioned relationship, in these cases, the system move towards the balance state at the lower speed than Eqs. ( 14) and (15) for satisfying the abovementioned conditions (Eq. ( 19)).…”

Section: Algorithmic Steps Of Tea-fs Techniquementioning

confidence: 99%

“…The significant features of BC involving anonymity, decentralization, and security can apply secure data transactions and overcome centralized server dependence to support security in 5G-based IoT. Additionally, the inimitable property of dispersed data storage, smart contracts, and asset tracking make BC technology required for 5G-based IoT [15].…”

Section: Introductionmentioning

confidence: 99%

Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme

Alohali¹,

Elsadig²,

Al‐Wesabi³

et al. 2023

Computer Systems Science and Engineering

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With recent advancements in information and communication technology, a huge volume of corporate and sensitive user data was shared consistently across the network, making it vulnerable to an attack that may be brought some factors under risk: data availability, confidentiality, and integrity. Intrusion Detection Systems (IDS) were mostly exploited in various networks to help promptly recognize intrusions. Nowadays, blockchain (BC) technology has received much more interest as a means to share data without needing a trusted third person. Therefore, this study designs a new Blockchain Assisted Optimal Machine Learning based Cyberattack Detection and Classification (BAOML-CADC) technique. In the BAOML-CADC technique, the major focus lies in identifying cyberattacks. To do so, the presented BAOML-CADC technique applies a thermal equilibrium algorithm-based feature selection (TEA-FS) method for the optimal choice of features. The BAOML-CADC technique uses an extreme learning machine (ELM) model for cyberattack recognition. In addition, a BC-based integrity verification technique is developed to defend against the misrouting attack, showing the innovation of the work. The experimental validation of BAOML-CADC algorithm is tested on a benchmark cyberattack dataset. The obtained values implied the improved performance of the BAOML-CADC algorithm over other techniques.

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“…Additionally, machine learning techniques can be utilized for anomaly detection in vehicle behavior [251,252]. By establishing the baselines of normal vehicle operation, machine learning models can identify deviations that may indicate unauthorized access or malicious control attempts [253,254]. This can help protect vehicles from cyber-physical attacks, such as remote hijacking or manipulation of critical systems [255,256].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Formal Methods and Validation Techniques for Ensuring Automotive Systems Security

Krichen

2023

Information

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The increasing complexity and connectivity of automotive systems have raised concerns about their vulnerability to security breaches. As a result, the integration of formal methods and validation techniques has become crucial in ensuring the security of automotive systems. This survey research paper aims to provide a comprehensive overview of the current state-of-the-art formal methods and validation techniques employed in the automotive industry for system security. The paper begins by discussing the challenges associated with automotive system security and the potential consequences of security breaches. Then, it explores various formal methods, such as model checking, theorem proving, and abstract interpretation, which have been widely used to analyze and verify the security properties of automotive systems. Additionally, the survey highlights the validation techniques employed to ensure the effectiveness of security measures, including penetration testing, fault injection, and fuzz testing. Furthermore, the paper examines the integration of formal methods and validation techniques within the automotive development lifecycle, including requirements engineering, design, implementation, and testing phases. It discusses the benefits and limitations of these approaches, considering factors such as scalability, efficiency, and applicability to real-world automotive systems. Through an extensive review of relevant literature and case studies, this survey provides insights into the current research trends, challenges, and open research questions in the field of formal methods and validation techniques for automotive system security. The findings of this survey can serve as a valuable resource for researchers, practitioners, and policymakers involved in the design, development, and evaluation of secure automotive systems.

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Cyber-Attack on P2P Energy Transaction Between Connected Electric Vehicles: A False Data Injection Detection Based Machine Learning Model

Cited by 32 publications

References 22 publications

Enhanced Network Security against SQL Injection Attack Using Machine Learning

Enhanced Network Security against SQL Injection Attack Using Machine Learning

Blockchain Assisted Optimal Machine Learning Based Cyberattack Detection and Classification Scheme

Formal Methods and Validation Techniques for Ensuring Automotive Systems Security

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