Patent AU-2021105509-A4 (AU)
Title: Security measures for Intelligent Mobile Agents in e - Commerce Decentralized Communities using Machine Learning based Predictive Modeling
Inventor: MISHRA PRIYANKA
IPC: G06F21/12
Filed: 2021-08-14
Granted: 2021-11-11
Abstract: Security measures for Intelligent Mobile Agents in 
e-Commerce Decentralized Communities using 
Machine Learning based Predictive Modeling 
ABSTRACT 
The present invention " Security measures for Intelligent Mobile Agents in e
commerce Decentralized Communities using Machine Learning based 
Predictive Modeling " service for allowing relates to the creation of the increase of Intelligent 
Secure Quantifying Trust (01) based Reputation for securing Mobile Agents (02) from malicious 
hosts in order to perform successful Internet transactions in e-commerce applications. The main 
motive of the designed system is to investigate (03) the security issues in digital trust and 
reputation systems designed for securing Mobile Agent dispatched in open systems. Trust 
deliberation is a soft security (10) approach used in the problem (04) of Mobile Agent security 
where by an agent is protected from malicious behavior (011) of hosts within the system. We 
propose MRep, short for Reputation Intelligent systems for Mobile Agent security (05). Though 
numerous architectural approaches and mathematical models to compute the trust already exist, 
but we tried to provide a system enabling trust (06) to be utilized by Mobile Agents to predict 
reputation of hosts before initiating a transaction with them. As trust is subjective, we envisage a 
system where Direct and Indirect Reputation (07) of hosts is calculated by formalizing (09) basic 
trust factors.  
Our designed new system is decentralized in nature where each individual host carries rating 
information and gives recommendation about other hosts when asked by the agent (012).  
Recommendation credibility is ensured by calculating F-score through recommender's past 
recommendation history. This helps to effectively reduce bias in the Final Reputation calculated 
for Destination Host by the agent. Conceptualizing each trust factor in the time domain, Final 
Reputation of Destination Host is calculated (013). For this, adaptive weights are assigned to 
different recommender groups. In MRep, group of recommenders are developed according to 
distance from assumed pre-trusted host called Source Host. Recommender group is the name 
given to a hierarchy of hosts. There are four recommender groups in MRep namely R for Relative, 
F for Friend, N for Neighbor and S for Stranger (014). Weights to recommender group decrease 
with an increase in distance from Source Host. Analytic Hierarchy Process (AHP) is used to 
assess and verify weight assignments to recommender groups (015). AHP verifies consistencyin 
adaptive weight assignments to recommender groups for different trust relationship networks.  
Our work identifies and proposes detection (016) and prevention methodology for three basic 
attacks in proposed reputation system (017). Two attack detection methodologies are verified 
statistically and tested against synthetic generated data. A prevention mechanism is proposed for 
third attack using game-theoretic approach, like Slander Attacks (by individual malicious host), 
Colluder Attacks (018) (by a coalition of malicious hosts) and Whitewashing Attacks (by new 
joining hosts to clear their past bad reputation and reenter with new identity).
PRIYANKA MISHRA and BOSUBABU SAMBANA, 1/7 
Security measures for Intelligent Mobile Agents in e-commerce Decentralized 
Communities using Machine Learning based Predictive Modeling 
DRAWINGS 
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01 
Figure 1: Input and MRep hierarchical view 
05 
04 
03 
06 02 
Figure 2: Community level view of MRep.