BigRC-EML: big-data based ransomware classification using ensemble machine learning

Aurangzeb, Sana; Anwar, Haris; Naeem, M. Asif; Aleem, Muhammad

doi:10.1007/s10586-022-03569-4

Cited by 39 publications

(31 citation statements)

References 39 publications

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“…Combining classifiers is a technique used in ensemble-voting mechanisms and has been widely used in data mining. The literature shows that ensemble voting demonstrates significantly lower error rates in classification problems as compared to using any single ML classifier 31 . Moreover, an ensemble reduces the dispersion of the predictions and model performance 32 .…”

Section: The Proposed Approachmentioning

confidence: 99%

Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism

Aurangzeb

Aleem

2023

Sci Rep

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With the rise in popularity and usage of Android operating systems, malicious applications are targeted by applying innovative ways and techniques. Today, malware becomes intelligent that uses several ways of obfuscation techniques to hide its functionality and evade anti-malware engines. For mainstream smartphone users, Android malware poses a severe security danger. An obfuscation approach, however, can produce malware versions that can evade current detection strategies and dramatically lower the detection accuracy. Attempting to identify Android malware obfuscation variations, this paper proposes an approach to address the challenges and issues related to the classification and detection of malicious obfuscated variants. The employed detection and classification scheme uses both static and dynamic analysis using an ensemble voting mechanism. Moreover, this study demonstrates that a small subset of features performs consistently well when they are derived from the basic malware (non-obfuscated), however, after applying a novel feature-based obfuscation approach, the study shows a drastic change indicating the relative importance of these features in obfuscating benign and malware applications. For this purpose, we present a fast, scalable, and accurate mechanism for obfuscated Android malware detection based on the Deep learning algorithm using real and emulator-based platforms. The experiments show that the proposed model detects malware effectively and accurately along with the identification of features that are usually obfuscated by malware attackers.

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Section: The Proposed Approachmentioning

confidence: 99%

Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism

Aurangzeb

Aleem

2023

Sci Rep

Self Cite

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“…In Aurangzeb et al [16], a BigRC-EML technique is proposed to detect and classify ransomware dependent upon static and dynamic characteristics. It can utilize ensemble ML methodologies on big datasets to enhance the detection of ransomware's performance.…”

Section: Related Workmentioning

confidence: 99%

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Alalayah¹,

Alrayes²,

Nour³

et al. 2023

Computer Systems Science and Engineering

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Malware is a 'malicious software program that performs multiple cyberattacks on the Internet, involving fraud, scams, nation-state cyberwar, and cybercrime. Such malicious software programs come under different classifications, namely Trojans, viruses, spyware, worms, ransomware, Rootkit, botnet malware, etc. Ransomware is a kind of malware that holds the victim's data hostage by encrypting the information on the user's computer to make it inaccessible to users and only decrypting it; then, the user pays a ransom procedure of a sum of money. To prevent detection, various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning (ML) algorithm. This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection (LBAAA-OMLMD) approach in Computer Networks. The presented LBAAA-OMLMD model mainly aims to detect and classify the existence of ransomware and goodware in the network. To accomplish this, the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection (LBAAA-FS) model to reduce the curse of dimensionality problems. Besides, the Flower Pollination Algorithm (FPA) with Echo State Network (ESN) Classification model is applied. The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results. The experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset, and the outcomes are inspected in distinct measures. The comprehensive comparative examination demonstrated the betterment of the LBAAA-OMLMD model over recent algorithms.

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“…Interested readers can refer to Liu et al (2020) and Rani et al (2022) for more information. Aurangzeb et al (2022) developed a BigRC-EML model by using ensemble methods and principal component analysis (PCA) to select the most significant features either static or dynamic. The main target was to predict ransomware in the context of big data with a high accuracy ratio.…”

Section: Metaheuristic Algorithms For Solving Ransomware Problemmentioning

confidence: 99%

Android ransomware detection using binary Jaya optimization algorithm

Alazab

2023

Expert Systems

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Ransomware is a serious security concern to mobile devices, as it prevents the use of the device and its contents until a ransom is paid, resulting in considerable financial losses for both people and corporations. The existing anti‐malware measures have shown to be inadequate in combatting new malware variants that utilize advanced evasion strategies like Polymorphic, Metamorphic, Dynamic Code Loading, Time‐based evasion, and Reflection. Furthermore, these primary defences have also suffered from low detection rates, significant false positives, high processing times, and excessive processing and power consumption that is inappropriate for smartphones. This paper offers the binary JAYA (BJAYA) for ransomware detection in Android mobile devices using the BJAYA optimization‐based algorithm. The developed algorithm's effectiveness has been assessed against two datasets, the 0–1 knapsack, and real ransomware dataset. The proposed BJAYA method surpassed the other algorithms on 85% of the 0–1 knapsack datasets. The suggested BJAYA method was also tested on a ransomware dataset in two phases. In the first stage of testing, BJAYA outperformed other standard classifiers with sensitivity and Gmean values of 97% and 98.2%, respectively. In the second stage of testing, BJAYA outperformed other GA, FPA, and PSO metaheuristic algorithms in terms of specificity, sensitivity, and Gmean. These findings indicate the applicability of the suggested BJAYA algorithm for ransomware detection.

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BigRC-EML: big-data based ransomware classification using ensemble machine learning

Cited by 39 publications

References 39 publications

Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism

Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Android ransomware detection using binary Jaya optimization algorithm

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