Malware Detection Using Machine Learning Projects

Malware Detection Using Machine Learning Thesis Ideas

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1.Malware Detection Using The Machine Learning Based Modified Partial Swarm Optimization Approach

Keywords:

Machine Learning, Malware Detection, Particle Swarm Optimization (PSO), Optimal Solutions

            Our paper uses ML-MD approach that uses static method to classify different malware families using methods. ML based framework can be utilized to detect malware. We extract the dataset using Principal component analysis (PCA). Modified particle swarm optimisation (MPSO) can be proposed to give better malware detection result. By using ML- bases MPSO method we get increased accuracy rate and detection rate.   

2. Detection and Classification of Malware for Cyber Security using Machine Learning Algorithms

Keywords:

Malware, Cybersecurity, Machine Learning Algorithms

            PayPal is frequently imitated by hackers and can provide customers login information. The existing system takes more time and less efficient. To overcome that issue our paper uses combined approach of ML and IoT. Many issues can be caused in previous methods i.e., the Signature based detection is unattainable. Our study uses different detection and classification methods by suggested using ML methods.

3. Malware Attack Detection using Machine Learning Methods for IoT Smart Devices

Keywords:

IoT, Malware, Botnet, Feature Selection

            IoT devices have fast improvement as they are targeted by malware attacks. Because of their computational capabilities they have strong security and are not organised by the device. We used ML methods to detect the attacks and the heavy weight can be challenging to present the response of the attack actions. We also used CART learning method to provide the malware attack detection and can be compared with Naïve Bayes.

4. Identification and Detection of Behavior Based Malware using Machine Learning

Keywords:

Malicious Program, Classifiers Sentiment

            Our paper uses ML methods to detect and identify the behaviour based malware detection to give the better solutions. To identify the real time malwares by utilizing signature matching methods. The classification techniques used in our paper are KNN, j48, Decision Tree, SVM, Naive Bayes, Neural network and Multilayer perceptron. We can identify the Malware effectively by using Proof- of-concept. 

5. Exploring Quantum Machine Learning for Explainable Malware Detection

Keywords:

Performance evaluation, Computers, Quantum algorithm, Neural networks

            We work computer and mobile devices to achieve various tasks and malicious users can ready to execute malicious actions. Our paper uses quantum ML techniques to detect malware. We implement two various class activation mapping methods to focus the image classification of malware families. Our paper also compares the outcome by utilizing the quantum model with CNN method.

6. An Analysis of Android Malware and IoT Attack Detection with Machine Learning

Keywords:

Security, Malware Attacks, Android Malware dataset

            We have found the hope in ML methods to detect the malware attack on IoT environment. Our paper uses ML have to detect IoT android malware threats. Our paper uses ML methods like Naïve Bayes, K- Nearest Neighbour (KNN), Decision Tree and Random Forest to detect malware in IoT. Decision Tree gives the better outcome. 

7. A Comparison Study to Detect Malware using Deep Learning and Machine learning Techniques

Keywords:

byte codes, section, opcodes, random forest, decision tree, support vector machine (classifier), K-nearest neighbor, SGD, Logistic regression, Naıve Bayes, Deep Learning Model, Malware Classification, Windows PC Malware

            Our paper uses seven ML and DL methods to detect malware by utilizing the extracted byte, opcode and section codes. We classify the malware in nine different malware families and at first the byte code, opcode and the section codes are extracted and merged and the classification can be utilized by Random forest, Decision Tree, SVM, KNN, SGD, Logistic Regression, Naïve Bayes and DL methods. Our paper focuses the importance of ML and DL to detect malware.  

8. Malware Detection Using XGBoost based Machine Learning Models – Review

Keywords:

Virus, XGBoost, cybersecurity

            Our study uses the application of ML to detect the malware. We provide a unique strategy for malware detection that tackles the issues by the combination of different ML methods with feature extraction that retrieve both static and dynamic information from malware. Our paper uses XG Boost, Adaboost, Random Forest and Decision tree methods.   

9. Design of Machine Learning-Based Malware Detection Techniques in Smartphone Environment

Keywords:

Feature extraction, Multinomial Naïve Bayes

            Android mobile devices and apps can be hacked by spreading malware. We utilize ML to find the effective software in Android- based gadgets and programs. To support the supervised learning the proposed method that collects the features from APK files. Multinomial Naïve Bayes, Random forest and SVM are the prediction models. When more data has to be used for training the accuracy gets improved.

10. Machine Learning Approaches for Analysing Static features in Android Malware Detection

Keywords

Android, Malware, CICInvesAndMal2019, Trojan, Ransom ware, Adware

            Our paper uses android permission and intent as a dataset and a set of features to look for malware. Principal Component analysis (PCA) can be used to choose features and the various ML methods like Decision tree, Naïve Bayes, Decision tree, Random forest, KNN were utilized to train and test the dataset. RF is the best classifier with success rate.