Statistical Detection of Adversarial Examples in Blockchain-Based Federated Forest In-Vehicle Network Intrusion Detection Systems

Aliyu, Ibrahim; Engelenburg, Sélinde van; Mu’azu, Muhammed Bashir; Kim, Jinsul; Lim, Chang Gyoon

doi:10.1109/access.2022.3212412

Cited by 11 publications

(13 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mark indicate the successful detection of attack, and X mark indicating the detection is not possible. The earlier works (3) , (4) , (6) , (8) achieved detectability at epsilon value greater than 2 but failed in detecting the attack samples with perturbation values < 2.0 and when encounter with new attack types. Our ML-Filter achieved the credibility for detection of those attacks samples of unknown attacks and known attacks and with the attack samples with minimal perturbations, thus overcoming the lack of generalized detection by the earlier works.…”

Section: Statistical Significance Testmentioning

confidence: 90%

See 1 more Smart Citation

Mitigating Gradient-Based Data Poisoning Attacks on Machine Learning Models: A Statistical Detection Method

Sanapala,

Gondi

2024

IJST

View full text Add to dashboard Cite

Objectives: This research paper aims to develop a novel method for identifying gradient-based data poisoning attacks on industrial applications like autonomous vehicles and intelligent healthcare systems relying on machine learning and deep learning techniques. These algorithms performs well only if they are trained on good quality dataset. However, the ML models are prone to data poisoning attacks, targeting the training dataset, manipulate its input samples such that the machine learning algorithm gets confused and produces wrong predictions. The current detection techniques are effective to detect known attacks and lack generalized detection to unknown attacks. To address this issue, this paper aims to integrate security elements within the machine learning framework, guaranteeing effective identification and mitigation of known and unknown threats and achieve generalized detection. Methods: ML Filter, a unique attack detection approach integrates ML-Filter Detection Algorithm and the Statistical Perturbation Bounds Identification Algorithm to determine the given dataset is poisoned or not. DBSCAN algorithm is used to divide the dataset into several smaller subsets and perform algorithmic analysis for detection. The performance of the proposed method is evaluated in terms of True positive rate and significance test accuracy. Findings: The probability distribution differences between original and poisoned datasets vary with change in perturbation size rather than the datasets and ML models use for application. This finding lead to determine the perturbation bounds using statistical pairwise distance metrics and corresponding significance tests computed on the results. ML Filter demonstrates a high detection rate of 99.63% for known attacks and achieves a generalized detection accuracy of 98% for unknown attacks. Novelty: A secured ML architecture and a unique statistical detection approach ML-Filter, effectively detect data poisoning attacks, demonstrating significant advancements in detecting both known and unknown threats in industrial applications utilizing machine learning and deep learning algorithms. Keywords: Privacy and security, Adversarial machine learning, Secured ML Architecture, ML-Filter, Statistical Perturbation Bounds Identification Algorithm

show abstract

Section: Statistical Significance Testmentioning

confidence: 90%

“…Then, the ML model trains these attack samples to learn their patterns and identify them as malicious ones. This entire procedure is called adversarial training (AT) (8) . Although these techniques perform well on known attacks, they are still susceptible to unknown attacks and lack generality.…”

Section: Introductionmentioning

confidence: 99%

Mitigating Gradient-Based Data Poisoning Attacks on Machine Learning Models: A Statistical Detection Method

Sanapala,

Gondi

2024

IJST

View full text Add to dashboard Cite

show abstract

“…The following describes the datasets utilized in the provided papers to assess the efficacy of various IDS solutions. Three of the papers, namely [52,57,63], employed the CAN-intrusion dataset (OTIDS), which was sourced from the Hacking and Countermeasure Research Lab at Korea University. This dataset provides a comprehensive representation of intrusion scenarios within in-vehicle networks, making it suitable for assessing IDSs specifically designed for vehicular contexts.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…• Attacks detected: Within the domain of FL-based IDSs for IoV, numerous research papers have put forth methodologies to identify a diverse range of cyber threats. DoS attacks [47,52,[57][58][59][60]63] and constant attacks [53,55,61] are the most frequently discussed types of attacks in the literature. In addition, some authors emphasized specific attacks, such as the Sybil assault [56] and the black hole attack [54].…”

Section: Analysis and Discussionmentioning

confidence: 99%

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

Alsamiri,

Alsubhi

2023

Future Internet

View full text Add to dashboard Cite

In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL’s inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature.

show abstract

“…Although the idea of decentralization is currently very common in the case of decentralization being applied to a number of fields. However, the application to software development is still relatively new [9]. At present, because of the characteristics of blockchain technology itself.…”

Section: Definition Of Decentralized Applications (Development History)mentioning

confidence: 99%

Design, development, and deployment of decentralized applications

Chen

2024

ACE

View full text Add to dashboard Cite

Beginning with a comprehensive definition of Decentralized Applications (DApps) and their developmental trajectory, this treatise delves into their inception around 2010. It is intriguing to note that by 2020, DApps had already found preliminary applications in diverse sectors, ranging from finance to archaeology. Yet, there remains vast untapped potential awaiting exploration and refinement within the realm of DApps. The discourse then navigates the intricate web of DApps' system architecture, illuminating the cardinal aspects of their design, evolution, and eventual deployment. Herein, the essence of systematic planning during the design phase is underscored, underpinning its pivotal role in shaping the efficacy of the application. Further shedding light on DApps' expansive utility, the paper underscores their transformative influence in areas such as authentication systems and real-time operational control. However, the journey of DApps is not without its challenges. The document elucidates the complexities associated with crafting robust smart contracts, mitigating scalability concerns, and nurturing user acceptance and integration. In light of these hurdles, a clarion call is made for persistent research and avant-garde innovation, propelling DApps to their true potential in the evolving digital landscape.

show abstract

Statistical Detection of Adversarial Examples in Blockchain-Based Federated Forest In-Vehicle Network Intrusion Detection Systems

Cited by 11 publications

References 50 publications

Mitigating Gradient-Based Data Poisoning Attacks on Machine Learning Models: A Statistical Detection Method

Mitigating Gradient-Based Data Poisoning Attacks on Machine Learning Models: A Statistical Detection Method

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

Design, development, and deployment of decentralized applications

Contact Info

Product

Resources

About