Intelligent Automated Intrusion Response System based on fuzzy decision making and risk assessment

Berenjian, Samaneh; Shajari, Mehdi; Farshid, Nadieh; Hatamian, Majid

doi:10.1109/is.2016.7737389

Cited by 15 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The process of truncating a given function f at the value y = y 0 generates a new function g that has the same output as f except that any output value greater than y 0 becomes y 0 , as shown in (8).…”

Section: Definitionmentioning

confidence: 99%

“…Solutions known as fuzzy inference systems (FIS) were then designed to map a set of inputs to outputs, using fuzzy logic and fuzzy sets to define vague conditions. These characteristics allowed fuzzy logic and fuzzy set theory to find a lot of uses in various areas, such as medicine [5]- [7], computer security [8]- [14], networking [15], [16], aeronautics [17], stock trading [18], and many others [4], [19]- [22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

BDFIS: Binary Decision Access Control Model Based On Fuzzy Inference Systems

Regateiro

Pereira

Aguiar

2019

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

Access control is a ubiquitous feature in almost all computer systems, and as data becomes more and more of an important asset for organizations, so do the associated access control policies. However, with the increase in the amount of data being produced, e.g. in IoT and social networks, the interest in simpler access control is increasing as well since more subjects (public, researchers, etc.) are now requesting access to it. Defining the exact conditions to allow each subject to access the data can be difficult, especially when vaguely defined conditions such as "expertise of a researcher" come into play. Fuzzy Inference Systems (FIS) allow to process these vague conditions and enables access control mechanisms to be more easily applied. The contribution of this paper lies in showing how a FIS can be used to output binary access control decisions (grant/deny) and what are the differences in the inference process that stems from restricting the output to these two output values.

show abstract

Section: Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BDFIS: Binary Decision Access Control Model Based On Fuzzy Inference Systems

Regateiro

Pereira

Aguiar

2019

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…Finding a malicious user from its neighbors is a selection problem. Methods such as fuzzy decision making [17], [18] and genetic-based algorithms [19] can be used to select the free-riders from multiple criteria. A successful trust management system that uses this approach is [20].…”

Section: Related Workmentioning

confidence: 99%

An Incentive Security Model to Provide Fairness for Peer-to-Peer Networks

Berenjian¹,

Hajizadeh²,

Atani³

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Peer-to-Peer networks are designed to rely on resources of their own users. Therefore, resource management plays an important role in P2P protocols. Therefore, resource management plays an important role in P2P protocols. Early P2P networks did not use proper mechanisms to manage fairness. However, after seeing difficulties and rise of freeloaders in networks like Gnutella, the importance of providing fairness for users have become apparent. In this paper, we propose an incentive based security model which leads to a network infrastructure that lightens the work of Seeders and makes Leechers to contribute more. This method is able to prevent betrayals in Leecher-to-Leecher transactions and more importantly, helps Seeders to be treated more fairly. This is what other incentive methods such as Bittorrent are incapable of doing. Additionally, by getting help from cryptography and combining it with our method, it is also possible to achieve secure channels, immune to spying, next to a fair network. The simulation results clearly show that how our proposed approach can overcome free-riding issue. In addition, our findings revealed that our approach is able to provide an appropriate level of fairness for the users and can decrease the download time.

show abstract

“…We exploit fuzzy logic as an appropriate method that is widely adopted in a mixed variety of IT systems such as wireless networks, intrusion detection systems, etc. [18][19][20], especially, as it has been extensively employed as the core of decision-making systems [21]. The output of a fuzzy controller is obtained from the fuzzification of inputs using the associated membership functions (MFs).…”

Section: Fair: Fuzzy Alarming Index Rulementioning

confidence: 99%

FAIR: Fuzzy Alarming Index Rule for Privacy Analysis in Smartphone Apps

Hatamian

Serna

Rannenberg

et al. 2017

Trust, Privacy and Security in Digital Business

Self Cite

View full text Add to dashboard Cite

In this paper, we introduce an approach that aims at increasing individuals' privacy awareness. We perform a privacy risk assessment of the smartphone applications (apps) installed on a user's device. We implemented an app behaviour monitoring tool that collects information about access to sensitive resources by each installed app. We then calculate a privacy risk score using a fuzzy logic based approach that considers type, number and frequency of access on resources. The combination of these two concepts provides the user with information about the privacy invasiveness level of the monitored apps. Our approach enables users to make informed privacy decisions, i.e. restrict permissions or report an app based on resource access events. We evaluate our approach by analysing the behaviour of selected apps and calculating their associated privacy score. Initial results demonstrate the applicability of our approach, which allows the comparison of apps by reporting to the user the detected events and the resulting privacy risk score.

show abstract

Intelligent Automated Intrusion Response System based on fuzzy decision making and risk assessment

Cited by 15 publications

References 13 publications

BDFIS: Binary Decision Access Control Model Based On Fuzzy Inference Systems

BDFIS: Binary Decision Access Control Model Based On Fuzzy Inference Systems

An Incentive Security Model to Provide Fairness for Peer-to-Peer Networks

FAIR: Fuzzy Alarming Index Rule for Privacy Analysis in Smartphone Apps

Contact Info

Product

Resources

About