Malware Detection Using Machine Learning Projects

Malware Detection Using Machine Learning Projects

Do you have a passion for cybersecurity and machine learning? The field of malware detection using machine learning is buzzing with exciting research. We would love to hear your ideas for Malware Detection Using Machine Learning Projects. Our team is here to guide you and provide you with the latest and greatest ideas in this field. Check out this article we have for you, which outlines a step-by-step procedure for constructing a malware detection project using machine learning. Let’s dive into this fascinating ML world together!

  1. Define Our Objective :

Our main goal is clearly depict that what we require to attain, that involves,

  • Binary classification: Whether the given file is harmless or malicious?
  • Multi-class classification: In which species of malware does a provided file belong to?
  • Anomaly detection: Does the given file behave diversely from typical benign files?
  1. Data Collection:

Datasets of malicious and harmless instances are gathered by us. Usually, this is a more complicated task.

  • Public Datasets: This includes numerous public malware datasets like,
  • The MalwareBazaar
  • VirusTotal that demands API keys and possess usage restrictions.
  • Malware Genome Project
  • Lab Collection: If we possess available resources, then create a composed environment and accumulate malware models and their characteristics.
  1. Feature Engineering:

We detect the features that are deployed to classify within benign and malicious models.

  • Static Analysis :

It depends on following categories,

  • File size
  • File entropy
  • Strings analysis
  • Opcode frequency
  • PE( Portable Executable) header analysis
  • Dynamic Analysis:

This based on,

  • System cells
  • Network Activity
  • File System Activity
  1. Model Selection and Training:

The various machine learning models are examined to find the appropriate and best one that suits our problem.

  • Traditional ML Models :

This ML (Machine Learning) model involves,

  • SVM (Support Vector Machine)
  • Random Forest and
  • Gradient Boosting Machines, etc.
  • Deep Learning:

The models of deep learning occupy in this like,

  • CNNs for raw byre sequences or opcode sequences.
  • RNNs or Transformers for sequential data. (e.g., system calls)
  • Hybrid models
  1. Evaluation:

Depends on the application, the metrics are determined by us like,

  • Accuracy,Precision,Recall,F1-score
  • ROC curve and AUC

Bear in mind that, it is efficiently significant in cybersecurity to reduce the false negatives (that is missing malware) as well as it must assure the logical rate of false positives.

  1. Deployment:

Once our model is satisfied by its performance, then implement it as an API or merging it into present security results.

  1. Continuous Learning:

Generally, malwares are rapidly developing, so it is crucial to upgrade and we train the model with novel data.

 Idea Pitches:

  1. Malware Family Classification: The malwares are categorized into their relevant families.
  2. Malware Generation: GANs (Generative Adversarial Networks) is employed for creating similar malware samples and checking that our classifier possesses the ability to identify them. This is the best method for enhancing the classifier.
  3. Transfer Learning for Malware Detection: Pre-trained models are accomplished by us to perform a relevant task and adjust them for malware detection.
  4. Explainability: Deeply understand the reason why a specific model is making its decision. Tools such as SHAP, LIME or integrated gradients are useful.


  • Imbalanced Dataset: Commonly, we consist of several benign (harmless) samples and less malware samples.
  • Evolving Malware: Strategies are modified by the malware authors to avoid being detected.
  • Feature Selection: Every feature is not necessarily essential. Feature selection and dimensionality reduction is possibly required.
  • Scalability: Make sure that the result manages the huge amount of data.

Frequently, verify whether we are sensibly managing the malware samples. Keep in mind that not to circulate them or perform in the outside of a composed surroundings.

Malware Detection Using Machine Learning ideas

Malware Detection Using Machine Learning Thesis Ideas

Once you share your requirements with us, we are more than happy to showcase the most recent projects completed by our team. Our research work is conducted using cutting-edge methodologies and algorithms to ensure its effectiveness. Rest assured, we strictly adhere to your university’s guidelines and guarantee a plagiarism-free outcome.

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


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


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


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


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


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


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


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


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


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


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.           

Live Tasks
Technology Ph.D MS M.Tech
NS2 75 117 95
NS3 98 119 206
OMNET++ 103 95 87
OPNET 36 64 89
QULANET 30 76 60
MININET 71 62 74
MATLAB 96 185 180
LTESIM 38 32 16
CONTIKI OS 42 36 29
GNS3 35 89 14
NETSIM 35 11 21
EVE-NG 4 8 9
TRANS 9 5 4
PEERSIM 8 8 12
RTOOL 13 15 8
VNX and VNUML 8 7 8
WISTAR 9 9 8
CNET 6 8 4
ESCAPE 8 7 9
VIRL 9 9 8
SWAN 9 19 5
JAVASIM 40 68 69
SSFNET 7 9 8
TOSSIM 5 7 4
PSIM 7 8 6
ONESIM 5 10 5
DIVERT 4 9 8
TINY OS 19 27 17
TRANS 7 8 6
CONSELF 7 19 6
ARENA 5 12 9
VENSIM 8 10 7
NETKIT 6 8 7
GEOIP 9 17 8
REAL 7 5 5
NEST 5 10 9

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