Optimizing an artificial immune system algorithm in support of flow-Based internet traffic classification

Schmidt, Brian; Al-Fuqaha, Ala; Gupta, Ajay; Kountanis, Dionysios

doi:10.1016/j.asoc.2017.01.016

Cited by 28 publications

(14 citation statements)

References 24 publications

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“…DE and AIS (artificial immune system) are some other heuristic search optimization techniques that have been recently used successfully for traffic engineering applications [65][66][67][68][69]. AIS is inspired by human biological immune system.…”

Section: Previous Studiesmentioning

confidence: 99%

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

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Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.

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Section: Previous Studiesmentioning

confidence: 99%

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

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“…Hence, the process of negative selection maps the negative space of a given class as such the given examples of the "self" class. Historically, the negative selection algorithm firstly appeared in 1994 [20]. By adopting this clonal selection process, the NIS is able to adjust itself in order to provide the most efficient response against pathogens' attacks.…”

Section: Training Methods [19]mentioning

confidence: 99%

An Immune Memory and Negative Selection to Manage Tensions in Emergency Services

Berquedich¹,

Kamach²,

Masmoudi³

2019

IJIES

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The management of emergency services within hospitals typically requires an effective manipulation and capitalization of data sources comprising worth information useful for hospitals' security, safety and efficiency. To meet these requirements, we dispose an agile approach of decision-making that addresses massive crowds occurring at the emergency departments. This approach considers the major constraints that might face these departments, as such lacks of human resources, high costs, and confusedness of patient's prioritization cases, non-sufficient capacity and logistics. We, basically, inspired from the biological immune defence processes to illustrate a piloting emergency mean known as the artificial immune system (AIS). This system intelligently assists hospital's decisionmakers to enhance their supplying strategies, provides relevant traces from tracking previous information that assist hospital's staffs that face the massive patient flow, and fosters the execution of efficient solutions. This paper investigates AIS data and relevant traces of overcrowding dilemmas, in which these data could be exploited to increase the reception planning, decrease tension-related anomalies within the emergency department, and supplying hospital chiefs working under stress. The main objective is to present the basic concepts of a new approach that aims to provide hospital decision-makers with relevant traces to assist them in their decision-making in the difficult situations. The expected benefits (main findings) for using the considered approach are not only to provide crisis managers with a relevant computerized decision support system, but also to minimize financial costs, reduce the response time and positively impact the crisis management.

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“…Table 2 presents the accuracy, precision, recall and F-Measure for the Kullback-Leibler and Euclidean methods for all 8 classes tested. After evaluating the performance of the classification proposing the use of Kullback-Leibler divergence, we compared the performance with methods already found and tested in the literature [16], [22], [23]. For our comparison, we use of the Euclidian distance and Support Vector Machine (SVM).…”

Section: Performance Of the Classifiermentioning

confidence: 99%

“…KL divergence has previously been used for speaker identification/verification and image classification [18] and for detection of low-rate Distributed Denial of Service (DDoS) attacks [27]. Euclidean distance has previously been used for optimizing an artificial immune system algorithm used for flow-based Internet traffic classification [16]. Here, both KL divergence and Euclidean distance are used to build classifiers without the need to combine them with other methods.…”

Section: Introductionmentioning

confidence: 99%

Classification of Encrypted Internet Traffic Using Kullback-Leibler Divergence and Euclidean Distance

Cunha

Zavala

Inácio

et al. 2020

Advanced Information Networking and Applications

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The limitations of traditional classification methods based on port number and payload inspection to classify encrypted or obfuscated Internet traffic, often with randomized port numbers, have lead to significant research efforts focusing on classification approaches based on Machine Learning techniques using Transport Layer statistical features. However, these approaches also have their own limitations, leading to the study of a set of other alternative approaches, including statisticsbased approaches. Statistical approaches can be an alternative to machine learning, because in real-time traffic classification with new types of data, the entire traffic classifier has to be retrained in order to adapt to the new change by combining the old training data with the new training data. This article investigates the classification of encrypted traffic using statistical methods applied to network traffic classification. We propose two statistical classifiers for encrypted Internet traffic based on Kullback Leibler divergence and Euclidean distance, which are computed using the flow and packet size obtained from some of the protocols used by applications. In our experiments, we evaluate the two classifiers based on statistical methods and compare them with a classifier based on Support Vector Machine (SVM). During our study, we were able to classify the traffic by using few features without compromising the performance of the classifier. The experimental results illustrate the effectiveness of our models used for traffic classification.

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Optimizing an artificial immune system algorithm in support of flow-Based internet traffic classification

Cited by 28 publications

References 24 publications

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

An Immune Memory and Negative Selection to Manage Tensions in Emergency Services

Classification of Encrypted Internet Traffic Using Kullback-Leibler Divergence and Euclidean Distance

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