Feature Selection Using Relative Fuzzy Entropy and Ant Colony Optimization Applied to Real-time Intrusion Detection System

Varma, P. Ravi Kiran; Kumari, V. Valli; Kumar, S. Srinivas

doi:10.1016/j.procs.2016.05.203

Cited by 48 publications

(15 citation statements)

References 15 publications

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“…Fuzzy Entropy has been used in [ 22 ] in combination with the empirical Wavelet Transform for the monitor and diagnose of faults of motor bearing, where it has demonstrated its high identification performance. Fuzzy Entropy has been applied in combination with ant colony optimization in [ 23 ] to a problem similar to the one presented in this paper: intrusion detection in communication networks, which have different characteristics than in-vehicle network traffic.…”

Section: Related Workmentioning

confidence: 99%

On the Application of Entropy Measures with Sliding Window for Intrusion Detection in Automotive In-Vehicle Networks

Baldini

2020

Entropy

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The evolution of modern automobiles to higher levels of connectivity and automatism has also increased the need to focus on the mitigation of potential cybersecurity risks. Researchers have proven in recent years that attacks on in-vehicle networks of automotive vehicles are possible and the research community has investigated various cybersecurity mitigation techniques and intrusion detection systems which can be adopted in the automotive sector. In comparison to conventional intrusion detection systems in large fixed networks and ICT infrastructures in general, in-vehicle systems have limited computing capabilities and other constraints related to data transfer and the management of cryptographic systems. In addition, it is important that attacks are detected in a short time-frame as cybersecurity attacks in vehicles can lead to safety hazards. This paper proposes an approach for intrusion detection of cybersecurity attacks in in-vehicle networks, which takes in consideration the constraints listed above. The approach is based on the application of an information entropy-based method based on a sliding window, which is quite efficient from time point of view, it does not require the implementation of complex cryptographic systems and it still provides a very high detection accuracy. Different entropy measures are used in the evaluation: Shannon Entropy, Renyi Entropy, Sample Entropy, Approximate Entropy, Permutation Entropy, Dispersion and Fuzzy Entropy. This paper evaluates the impact of the different hyperparameters present in the definition of entropy measures on a very large public data set of CAN-bus traffic with millions of CAN-bus messages with four different types of attacks: Denial of Service, Fuzzy Attack and two spoofing attacks related to RPM and Gear information. The sliding window approach in combination with entropy measures can detect attacks in a time-efficient way and with great accuracy for specific choices of the hyperparameters and entropy measures.

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Section: Related Workmentioning

confidence: 99%

On the Application of Entropy Measures with Sliding Window for Intrusion Detection in Automotive In-Vehicle Networks

Baldini

2020

Entropy

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“…However, with the increase in the number of iterations, the traditional pheromone updating method leads to the local drop-dead halt of the ant colony algorithm (AC algorithm). The fuzzy method [18] was used by many researchers and so many works were investigated, including robot soccer games [19], retinal vessel detection [20], and image segmentation [21]. Therefore, a fuzzy method is used to design a more effective pheromone update strategy for the ant colony algorithm.…”

Section: Collaborative Mission Planning For Multi-uavmentioning

confidence: 99%

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

2020

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Unmanned aerial vehicles (UAVs) received an unprecedented surge of people’s interest worldwide in recent years. This paper investigates the specific problem of cooperative mission planning for multiple UAVs on the battlefield from a hierarchical decision-making perspective. From the view of the actual mission planning issue, the two key problems to be solved in UAV collaborative mission planning are mission allocation and route planning. In this paper, both of these problems are taken into account via a hierarchical decision-making model. Firstly, we use a target clustering algorithm to divide the original targets into target subgroups, where each target subgroup contains multiple targets. Secondly, a fuzzy ant colony algorithm is used to calculate the global path between target subgroups for a single-target group. Thirdly, a fuzzy ant colony algorithm is also used to calculate the local path between multiple targets for a single-target subgroup. After three levels of decision-making, the complete path for multiple UAVs can be obtained. In order to improve the efficiency of a collaborative task between different types of UAVs, a cooperative communication strategy is developed, which can reduce the number of UAVs performing tasks. Finally, experimental results demonstrate the effectiveness of the proposed cooperative mission planning and cooperative communication strategy for multiple UAVs.

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“…Further, it is used in feature selection for IDS with interesting approach, but it is still limited. In this section, we present three improving works which are [67][68][69]. Table 13 and Table 14 illustrate the different proposed approach of ant colony for feature selection and their results.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Feature Selection Algorithms in Intrusion Detection System: A Survey

2018

KSII TIIS

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Regarding to the huge number of connections and the large flow of data on the Internet, Intrusion Detection System (IDS) has a difficulty to detect attacks. Moreover, irrelevant and redundant features influence on the quality of IDS precisely on the detection rate and processing cost. Feature Selection (FS) is the important technique, which gives the issue for enhancing the performance of detection. There are different works have been proposed, but a map for understanding and constructing a state of the FS in IDS is still need more investigation. In this paper, we introduce a survey of feature selection algorithms for intrusion detection system. We describe the well-known approaches that have been proposed in FS for IDS. Furthermore, we provide a classification with a comparative study between different contribution according to their techniques and results. We identify a new taxonomy for future trends and existing challenges.

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Feature Selection Using Relative Fuzzy Entropy and Ant Colony Optimization Applied to Real-time Intrusion Detection System

Cited by 48 publications

References 15 publications

On the Application of Entropy Measures with Sliding Window for Intrusion Detection in Automotive In-Vehicle Networks

On the Application of Entropy Measures with Sliding Window for Intrusion Detection in Automotive In-Vehicle Networks

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

Feature Selection Algorithms in Intrusion Detection System: A Survey

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