Multi-dimensional Hybrid Bayesian Belief Network Based Approach for APT Malware Detection in Various Systems

Sharma, Amit Kumar; Gupta, Brij B.; Singh, Awadhesh Kumar; Saraswat, V. K.

doi:10.1007/978-3-031-22018-0_16

Cited by 3 publications

(2 citation statements)

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“…Much in-depth research on sophisticated malware detection has been conducted recently. For instance, [13] is proposing an unusual Bayesian Belief Network-based method to solve the malware detection issue. To improve accuracy and lower false positives in malware detection, the suggested method extracts distinctive characteristics from the static, dynamic, and event analysis of the malware sample.…”

Section: Most Related Workmentioning

confidence: 99%

Enhancing Malware Detection Through Machine Learning Techniques

Jassim,

Kassir

2024

IJDS

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Malware detection is important to computer network security since it is the principal attack vector against modern enterprises. As a result, firms must remove viruses from computer systems. Using artificial intelligence, namely machine learning techniques, to function in real-time with an IT system is the ideal solution to this problem. This issue has yet to be fixed, but it is still significant because a lack of processing power and memory constrains these features. The most popular method for evaluating systems and intrusion detection models is using the Application Program Interface (API) calls via the KDD-CUP99 data set to give this solution. KDD-CUP99 has more than three hundred thousand samples, each with 54 features. However, the data set attributes were designed and chosen to provide us with a high malware detection rate. The quality of this data was lowered to produce results. To get the desired results, the attributes of this data were reduced. Data transformation and purification are used in this process. Inaccurate, unnecessary, duplicated, or missing information is eliminated by data cleansing. Data cleaning eliminates inaccurate, excessive, redundant, or lacking information. By comparing this study to earlier research that employed lengthy sequences of software interface (API) calls with the same machine-learning classifiers, data transformation includes discretization, which transforms the continuous process of discretizing continuous data into discrete forms is a type of data transformation. Using more advanced algorithms to do the task at hand with the best precision and the least expense increases accuracy and performance. The data set was divided into two categories using a Support Vector Machine (SVM), Decision Tree (DT), and Iterative Dichotomiser 3 (ID3). The findings revealed that little previous research uses a five-class classification strategy for malware detection. The accuracy of several works is comparable to the accuracy acquired in the proposed work.

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Section: Most Related Workmentioning

confidence: 99%

Enhancing Malware Detection Through Machine Learning Techniques

Jassim,

Kassir

2024

IJDS

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“…The APT attack has two motives, where critical information theft is the first one and system infrastructure destruction is the second one 8 . Generally, APT 9,10 is becoming increasingly prominent in contemporary networks. APTs are a kind of sophisticated attack discovered by resourceful adversaries by an extensive spectrum of attack procedures and tools 11 .…”

Section: Introductionmentioning

confidence: 99%

Fuzzy inference based feature selection and optimized deep learning for Advanced Persistent Threat attack detection

Kumar,

Noliya,

Makani

2023

Adaptive Control & Signal

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SummaryOne of the attacks that have rapidly happen is Advanced Persistent Threat (APT). APT attacks contain different sophisticated approaches and methods of attacking targets for stealing confidential as well as sensitive information. This research introduced novel and effective APT attack detection techniques, namely Smart Flower Cosine Algorithm‐driven Deep Convolutional Neural Network (SFCA‐DeepCNN). Here, the APT attack detection is done by the DeepCNN, wherein the weight of DeepCNN is updated by the proposed SFCA. The SFCA is modeled by unifying the Smart Flower Optimization Algorithm (SFOA) and Sine Cosine Algorithm (SCA). Additionally, the pre‐processing process is done by Quantile normalization, and the features are chosen based on the fuzzy‐based distance measures. Moreover, data augmentation is done to increase the size of data by performing the oversampling that avoids the overfitting problems. Furthermore, the proposed optimized deep learning scheme detects the accurate APT detection outcome. The performance improvement of the proposed method for testing accuracy is 9.417%, 10.47%, 4.232%, and 3.068% higher than the existing methods, such as, Deep Learning, Support Vector Machine (SVM), Bidirectional Long Short‐Term Memory (Bi‐LSTM), and Hidden Markov Model (HMM).

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