K-Nearest Neighbour Classifier Usage for Permission Based Malware Detection in Android

Arslan, Recep Sinan; Yurttakal, Ahmet Haşim

doi:10.46291/icontechvol4iss2pp15-27

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…A classification algorithm combined with a feature selection technique should be fast enough to determine results. k-NN is advantageous in producing results faster at a low cost in minimum processing time [43]. The HTs are self-designed in which three trojans are triggered in a sequence with different functionality [31] and secret key leakage type of trojan [32] are implemented in the AES circuit.…”

Section: Performance Analysismentioning

confidence: 99%

A Multiobjective Approach for Side-Channel Based Hardware Trojan Detection Using Power Traces

MOHANRAJ,

PARAMASIVAM

2024

IEICE Trans. Fundamentals

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The detection of hardware trojans has been extensively studied in the past. In this article, we propose a side-channel analysis technique that uses a wrapper-based feature selection technique for hardware trojan detection. The whale optimization algorithm is modified to carefully extract the best feature subset. The aim of the proposed technique is multiobjective: improve the accuracy and minimize the number of features. The power consumption traces measured from AES-128 trojan circuits are used as features in this experiment. The stabilizing property of the feature selection method helps to bring a mutual trade-off between the precision and recall parameters thereby minimizing the number of false negatives. The proposed hardware trojan detection scheme produces a maximum of 10.3% improvement in accuracy and reduction up to a single feature by employing the modified whale optimization technique. Thus the evaluation results conducted on various trust-hub cryptographic benchmark circuits prove to be efficient from the existing state-of-art methods.

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Section: Performance Analysismentioning

confidence: 99%

A Multiobjective Approach for Side-Channel Based Hardware Trojan Detection Using Power Traces

MOHANRAJ,

PARAMASIVAM

2024

IEICE Trans. Fundamentals

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“…In this study, for the 1st and 2nd layers to design the ensemble model, 8 different classifiers were used, namely Random Forest, Logistic Regression, Multilayer Perceptron, Support Vector Classifier, K-nearest neighbor, XGB, Gaussian Naïve Bayes, and Decision Tree, which have different architectural structures and are widely used to solve various problems [25][26][27]. After this, each classifier was tested independently of each other.…”

Section: Classifier Selection For Ensemble Modelmentioning

confidence: 99%

Suç Kategorisi Tespiti için Yığınlama Topluluk Öğrenimi Modeli Kullanan Çatı Tasarımı

ARSLAN,

DÜLGEROĞLU

2023

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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Crime refers to an action legally defined as harmful to society, and it is important to understand the type of crime to prevent these actions. However, crime can occur at any time and place, making it difficult to predict. Data generated based on previously committed crimes contributes to overcoming this difficulty. This study proposes a novel model for classifying criminal activities using a Doc2Vec that can cause a numerical representation of texts regardless of length and a stacking ensemble model that includes 8 different machine-learning models. Unlike the literature, the model processes the features as text and converts them into vectors rather than categorically. In this way, it enables using features that cannot be used in the literature. The proposed model is tested using a distributed online competition database, Francisco Crime Classification, which contains crimes committed over 12 years. An accuracy value of 99.28% was obtained for the 15 crime categories with the highest crime records, while precision, recall, and f-score values were 99.18%, 99.38%, and 99.20%, respectively. With cross-validation (k=10), 99.80% performance was achieved with a std. value of 0.001. These performance values are higher than those of all the studies in the literature using categorical feature structures. The results show that converting criminal activity reports, which contain text-based features, into vectors that can be processed with natural language processing techniques such as Doc2vec instead of using them categorically in model training can directly contribute to the classification performance and provide a more efficient model with less preprocessing.

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“…An assessment into the future prospects for Android malware detection research based on machine learning was lastly reviewed and shown to provide higher effectiveness. kNN was used by [20] as a scalable and quick machine learning method for the detection of malignant mobile application. The proposed method utilized 5 nearest neighbours and Minkowski as preferred distance metric.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Android Malware Detection: A Comparative Analysis of Six Classifiers

Gyunka¹,

Abikoye

Adekunle³

2021

Communications in Computer and Information Science

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The high proliferation rate of Android devices has exposed the platform to wider vulnerabilities of increasing malware attacks. Emerging trends of the malware threats are employing highly sophisticated and dynamic detection avoidance techniques. This has continued to weaken the capacity of existing signature-based detection systems in their protection against new and unknown threats. Thus, the need for effective detection approaches for unknown and novel Android malware has remained a growing challenge in the field of mobile and information security. This study therefore aimed at investigating the best performing machine learning classification algorithm for the anomaly Android malware detection, leveraging on permission-based feature sets, by conduction a performance comparison analysis between six different classification algorithms namely: Naïve Bayes, Simple Logistics, Random Forest, PART, k-Nearest Neighbours (k-NN), and Support Vector Machine (SVM). The Machine learning tool that was used for the preprocessing of the feature sets and the classification processes is WEKA 3.8.2 suite. Findings of the study showed that Random Forest had the best detection result with false alarm rate of 2.2%, accuracy of 97.4%, error rate of 2.6% and ROC Area of 99.6%. The study concluded that, using Android permission features, Random Forest and k-Nearest Neighbours recoded best performances in Android malware detection, followed by Support Vector Machine and Simple Logistics classification algorithms. Partial Decision Tree (PART) performed relatively well, while Naïve Baye recorded the least performance. Consequently, the deployment of Random Forest model and k-NN model are recommended for the development of an anomaly Android malware detection paradigm.

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K-Nearest Neighbour Classifier Usage for Permission Based Malware Detection in Android

Cited by 8 publications

References 30 publications

A Multiobjective Approach for Side-Channel Based Hardware Trojan Detection Using Power Traces

A Multiobjective Approach for Side-Channel Based Hardware Trojan Detection Using Power Traces

Suç Kategorisi Tespiti için Yığınlama Topluluk Öğrenimi Modeli Kullanan Çatı Tasarımı

Anomaly Android Malware Detection: A Comparative Analysis of Six Classifiers

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