A Novel DoS and DDoS Attacks Detection Algorithm Using ARIMA Time Series Model and Chaotic System in Computer Networks

Nezhad, Seyyed Meysam Tabatabaie; Nazari, Mahboubeh; Gharavol, Ebrahim A.

doi:10.1109/lcomm.2016.2517622

Cited by 107 publications

(16 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A summary of the related work is listed in Table 1. There are reviews addressing the chal- Scientific Work Reviews [19,27] Graph-based methods [2,12,13,36,37,41,42] Graph-based and time-sensitive methods [1,45] Machine learning-based [6,14,32] Statistical processes [33,44,48,50] Wavelet analysis [25,31,35] Industrial Intrusion Detection [3,15,18,20,23,28,34,38,39,46] lenge of anomaly detection for intrusion detection. García-Teodoro et al address the challenges of this field of work while presenting techniques and systems [19].…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, an anomaly is detected if the observed behaviour does not match the modeled values. A common approach is using Auto-Regressive Integrated Moving Average (ARIMA) to model time series behaviour [33,48], Tabatabaie et al include a chaotic behaviour prediction into their ARIMA model [44]. Yu et al present an anomaly detection scheme based on ARIMA for Wireless Sensor Networkss (WSNs) [50].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Using Temporal and Topological Features for Intrusion Detection in Operational Networks

Antón¹,

Fraunholz²,

Schotten³

2019

Proceedings of the 14th International Conference on Availability, Reliability and Security

View full text Add to dashboard Cite

Until two decades ago, industrial networks were deemed secure due to physical separation from public networks. An abundance of successful attacks proved that assumption wrong. Intrusion detection solutions for industrial application need to meet certain requirements that differ from home-and office-environments, such as working without feedback to the process and compatibility with legacy systems. Industrial systems are commonly used for several decades, updates are often difficult and expensive. Furthermore, most industrial protocols do not have inherent authentication or encryption mechanisms, allowing for easy lateral movement of an intruder once the perimeter is breached. In this work, an algorithm for motif discovery in time series, Matrix Profiles, is used to detect outliers in the timing behaviour of an industrial process. This process was monitored in an experimental environment, containing ground truth labels after attacks were performed. Furthermore, the graph representations of a different industrial data set that has been emulated are used to detect malicious activities. These activities can be derived from anomalous communication patterns, represented as edges in the graph. Finally, an integration concept for both methods is proposed.• Security and privacy → Intrusion detection systems; Network security; • Theory of computation → Design and analysis of algorithms; • Applied computing → Enterprise architectures.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Using Temporal and Topological Features for Intrusion Detection in Operational Networks

Antón¹,

Fraunholz²,

Schotten³

2019

Proceedings of the 14th International Conference on Availability, Reliability and Security

View full text Add to dashboard Cite

show abstract

“…Authors in [10], proposed an ARIMA model time series to detect DDoS attack. Number of packets in every one minute is sampled.…”

Section: Related Workmentioning

confidence: 99%

Statistical measures: Promising features for time series based DDoS attack detection

Fouladi¹,

Kayatas²,

Anarım³

2018

2018 26th Signal Processing and Communications Applications Conference (SIU)

View full text Add to dashboard Cite

Abstract-Data availability should be guaranteed by a web service in order to satisfy customers. One of the main challenges of information security professionals is DDoS attack which affects the availability. By masquerading itself as a legitimate user, a DDoS attacker tries to overwhelm a server by sending numerous amount of useless packets that influences the quality of service (QoS) of the network. DDoS attack can result in a great damage to network services. Useless packets similar to normal ones are dispatched by the attacker which leaves the intrusion detection system impotent of detection. Transferring from conventional packet-based analysis methods to time series based (flow-based) algorithms would be a promising alternative to spot DDoS attacks. In this work, we extract four measures of periodicity, kurtosis, skewness and self similarity of a time series and investigate the performance of these parameters in separating DDoS attack from normal traffic.

show abstract

“…Time series modeling is considered as one of the dynamic research areas that are attracting the intentions of the researches to model a forecasting system based on the application (Tabatabaie Nezhad et al , 2016). The time series modeling is designed with carefully collecting the data from the past and precisely grabs knowledge from the past history of the observations from time to time.…”

Section: Introductionmentioning

confidence: 99%

Stock market data modelling using fitness-oriented JAYA algorithm-based deep belief network

Swamy¹,

Sarojamma

2019

View full text Add to dashboard Cite

Purpose Data mining plays a major role in forecasting the open price details of the stock market. However, it fails to address the dimensionality and expectancy of a naive investor. Hence, this paper aims to study a future prediction model named time series model is implemented. Design/methodology/approach In this model, the stock market data are fed to the proposed deep neural networks (DBN), and the number of hidden neurons is optimized by the modified JAYA Algorithm (JA), based on the fitness function. Hence, the algorithm is termed as fitness-oriented JA (FJA), and the proposed model is termed as FJA-DBN. The primary objective of this open price forecasting model is the minimization of the error function between the modeled and actual output. Findings The performance analysis demonstrates that the deviation of FJA–DBN in predicting the open price details of the Tata Motors, Reliance Power and Infosys data shows better performance in terms of mean error percentage, symmetric mean absolute percentage error, mean absolute scaled error, mean absolute error, root mean square error, L1-norm, L2-Norm and Infinity-Norm (least infinity error). Research limitations/implications The proposed model can be used to forecast the open price details. Practical implications The investors are constantly reviewing past pricing history and using it to influence their future investment decisions. There are some basic assumptions used in this analysis, first being that everything significant about a company is already priced into the stock, other being that the price moves in trends Originality/value This paper presents a technique for time series modeling using JA. This is the first work that uses FJA-based optimization for stock market open price prediction.

show abstract

A Novel DoS and DDoS Attacks Detection Algorithm Using ARIMA Time Series Model and Chaotic System in Computer Networks

Cited by 107 publications

References 6 publications

Using Temporal and Topological Features for Intrusion Detection in Operational Networks

Using Temporal and Topological Features for Intrusion Detection in Operational Networks

Statistical measures: Promising features for time series based DDoS attack detection

Stock market data modelling using fitness-oriented JAYA algorithm-based deep belief network

Contact Info

Product

Resources

About