Secure design for cloud control system against distributed denial of service attack

Ali, Yasir; Xia, Yuanqing; Liang, Ma; Ahmad, Hammad

doi:10.1007/s11768-018-8002-8

Cited by 35 publications

(19 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, the authors of [ 63 , 98 ] discussed threshold-based attack detection mechanisms, and the analysis of transmission failure patterns to distinguish strategic attacks and non-malicious failures was explored by Cetinkaya et al [ 26 ]. In addition, Ali et al [ 99 ] recently considered methods from information technologies for detecting and mitigating distributed DoS attacks against networked control systems.…”

Section: An Overview Of Attack-resilient Control and Communicationmentioning

confidence: 99%

An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses

Cetinkaya

Ishii

Hayakawa

2019

Entropy

125

View full text Add to dashboard Cite

In this paper, we provide an overview of recent research efforts on networked control systems under denial-of-service attacks. Our goal is to discuss the utility of different attack modeling and analysis techniques proposed in the literature for addressing feedback control, state estimation, and multi-agent consensus problems in the face of jamming attacks in wireless channels and malicious packet drops in multi-hop networks. We discuss several modeling approaches that are employed for capturing the uncertainty in denial-of-service attack strategies. We give an outlook on deterministic constraint-based modeling ideas, game-theoretic and optimization-based techniques and probabilistic modeling approaches. A special emphasis is placed on tail-probability based failure models, which have been recently used for describing jamming attacks that affect signal to interference-plus-noise ratios of wireless channels as well as transmission failures on multi-hop networks due to packet-dropping attacks and non-malicious issues. We explain the use of attack models in the security analysis of networked systems. In addition to the modeling and analysis problems, a discussion is provided also on the recent developments concerning the design of attack-resilient control and communication protocols.

show abstract

Section: An Overview Of Attack-resilient Control and Communicationmentioning

confidence: 99%

An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses

Cetinkaya

Ishii

Hayakawa

2019

Entropy

125

View full text Add to dashboard Cite

show abstract

“…For most of the web applications (e.g. online auctions and online retail sales), security of the network is a major factor in the internet services [1]. Intrusion detection system (IDS) was noted as an effective method to detect DDoS attack and ensures the functional cloud services [6].…”

Section: Introductionmentioning

confidence: 99%

“…Intrusion detection system (IDS) was noted as an effective method to detect DDoS attack and ensures the functional cloud services [6]. IDS detects the computer attacks by investigating the records, which are collected from the internet [1].…”

Section: Introductionmentioning

confidence: 99%

“…Anomaly detection specifies the deviations that are obtained from the regular patterns whereas the signature detection utilized the patterns, which are related to the attacks to detect the intrusions [1].…”

Section: Introductionmentioning

confidence: 99%

“…Intrusion detection (ID) in CC is considered as an NP-hard problem. Metaheuristic algorithms give the best solution to the NP-hard problems [1] The rule learning approach based on the fuzzy classifier where the genetic algorithm (GA) in the adaptive genetic fuzzy system (AGFS) is the standard form. Moreover, the genetic algorithm has no guarantee for optimal solution and its one of state-of-the-art algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms

Velliangiri¹,

Pandey

2020

Future Generation Computer Systems

View full text Add to dashboard Cite

Cloud computing environment support resource sharing as cloud service over the internet. It enables the users to outsource data into the cloud server that can be accessed remotely from various devices distributed geographically. Accessing resources from the cloud causes various security issues as the attackers try to illegally access the data. The distributed denial of service (DDoS) attack is one of the security concern in the cloud server. DDoS is a kind of cyber attack which disrupt normal traffic of targeted cloud server (or any other servers). In this paper, we propose an effective fuzzy and taylor-elephant herd optimization (FT-EHO) inspired by deep belief network (DBN) classifier for detecting the DDoS attack. FT-EHO uses taylor series and elephant heard optimization algorithm along with a fuzzy classifier for rules learning. The performance of the proposed FT-EHO is evaluated through rigorous computer simulations. Three standard benckmark databases, namely, KDD cup, database1 and database2 are used during simulations. Four quality measures such as accuracy, detection accurarcy, precision and recall are considered as a performance metrics. FT-EHO's performance is compared against the state-ofthe-art methods considering the evaluation metrics. Results reveals that the proposed FT-EHO showed significantly higher value of evaluation metrics (accuracy (93.811%), detection rate (97.200%), precision (94.981%) and recall (93.833%)) as compared to other methods.

show abstract

Robust $${H_\infty }$$ Filtering for Semi-Markov Jump Systems Encountering Denial-of-Service Jamming Attacks

Wang

Zhu

2021

Circuits Syst Signal Process

View full text Add to dashboard Cite

Secure design for cloud control system against distributed denial of service attack

Cited by 35 publications

References 14 publications

An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses

An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses

Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms

Robust $${H_\infty }$$ Filtering for Semi-Markov Jump Systems Encountering Denial-of-Service Jamming Attacks

Contact Info

Product

Resources

About