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Multi-objective evolutionary algorithms for IDSs for IoT
- 2. MULTI- OBJECTIVE INTELLIGENT COMPUTATIONAL ALGORITHMS: ANAPPROACH TOWARD INTRUSION DETECTION IN INTERNET OF THINGS ERA BY MARIA HABIB SUPERVISED BY DR. IBRAHIM ALJARAH THURSDAY 25, 2019
- 3. Outline Introduction Quick Background Glance to LiteratureReview Methodology Results Conclusion
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- 5. Introduction Whydoes the security ofIoTdevices matter tous? Gartner Inc. has estimated the number of IoT connected will reach 20.6 billion devices in 2020. The economical gain of IoT, it has reached a peak of $120 billion in 2016, and will reach $6.2 trillion by 2027.
- 6. Introduction IoTCont. .. Hewlett Packard(HP) reports that 80% failed to have adequate password complexity. In 2015, a drug pump was hacked, and the attacker has increased the dosage of drug for the patient without the notice of nurses. Mirai (IoT botnet) malware in 2016 infected up to 300,000 devices.
- 7. Objective Several security solutionshave been proposed were led to heavy computational load. • Used simulated (were no real) IoT datasets. • Limited number of IoT attacks. Designing an approach for IDSs based on machine learning (multi-objective evolutionary algorithms) for real IoT networks.
- 8. Multi- Objective Optimization (MOO) Two orthree objective functions. Dominance vs relational operators . Pareto optimal set (non-dominated). f1 & f2 are minimization objectives (b) dominates (c) (a & b) are non-dominated solutions (15,3) (10,2) (8,3)
- 9. Feature Selection Optimization IoT results inbig, high-dimensional data. Designing a machine learning based IDS for IoT requires preprocessing large amount of features. Feature selection considered NP-hard optimization problem for high-dimensional data. Metaheuristics.
- 10. Metaheuristics are nature-inspiredalgorithms that mimic the natural evolution process and inspired by the Darwinian principles in nature. Nature-Inspired Algorithms Evolutionary- based Swarm-based Evolutionary Algorithms John Henry Holland EAs were the root, which basically established by Holland. https://en.wikipedia.org/wiki/John_Henry_Holland
- 11. Multi-objective (MOO) vs Single- objective (SOO) MOO can produceefficient set of solutions (trade-off) at single run, whereas SOO needs running so many times. MOO does not require a priori knowledge (preferences) before model building, SOO needs a priori knowledge to design the fitness function. MOO avoid the burden of weights tuning of the objective function.
- 12. PSO is metaheuristicalgorithm that inspired by the social behavior of bird flocking or fish schooling. Population of particles, each has velocity & position. Follows pbest, gbest to move within the search space. PSO
- 13. MOPSO Classical MOPSO (2002)the first attempt to extend PSO into MOPSO (high probability to stuck in local optimal & premature convergence). MOPSO (2004), secondary external archive, and a mutation operator. to update gbest
- 14. Glance to RelatedWorks RF, NB,NN, Gradient Boosting utilized for attacks detection in IoT. Hamamoto et al. (2018) designed network anomaly detection technique based on genetic algorithm and fuzzy logic. Hajimirzaei and Navimipour (2018) proposed IDS that classifies network traffic into normal and malicious using Multilayer Perceptron that trained by Artificial Bee Colony algorithm. Sanchez-Pi et al. (2018) applied Voronoi diagram-based Evolutionary Algorithm (VorEAl) in order to detect intrusions using "Australian defense force academy Linux" dataset (doesn't represent real IoT traffic). Very few implemented EAs or MOEAs for IDSs for IoT.
- 15. Methodology Design A modifiedMOPSO based on Lévy Flight (with sensitivity analysis). Compare The modified MOPSO with the original MOPSO (Coello,2004). Compare The performance of modified MOPSO with other MOEAs. Compare The modified MOPSO with filter- based methods.
- 16. Binary MOPSO Sigmoid(S1) Particles’ positions vectorencode all features
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- 19. Lévy Flight & Lévy Distribution PaulPierre Lévy Lévy distribution: heavy-tailed distribution, with large step length. Lévy Flight is a random walk where the jump's size follows Lévy distribution. Examples: foraging behavior of bacteria, albatross, and bumblebees. https://seekingalpha.com/article/3670366
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- 22. Created Datasets UCI(2018) A stratified sampleswere created (2000 instances) Normal class is the minor class & Anomaly is the major class
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- 24. Evaluation Measures (Eqs. Inpgs. 78-80) Accuracy TruePositive Rate (TPR) False Alarm Rate (FAR) True Negative Rate (TNR) G-mean Area Under Curve (AUC) No. of Features
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- 32. MOPSO-Lévy vs MOEAs (Average Pareto Front) Errorrate=1-accuracy
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- 35. Conclusion Designed an approachmachine learning-based intrusion detection for IoT (Multi-Objective evolutionary approach). The proposed approach compared with original MOPSO, MOEAs, filter-based method. The proposed approach achieved very good results to be recommened for IDSs.
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