A multi-dimensional machine learning approach to predict advanced malware

Bahtiyar, Şerif; Yaman, Mehmet Barış; Altıniğne, Can Yılmaz

doi:10.1016/j.comnet.2019.06.015

Cited by 28 publications

(15 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ultimately, the cost of the invasion varies depending on the effectiveness of the attack. Modern malware has evolved to be a highly efficient method for executing cyber-attacks [5]. Prevention of cyber-attacks on the internet services has now become a key issue for system engineers and research scientists.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of Malware Analysis Techniques for Securing Web Applications through a Decision-Making Framework under Fuzzy Environment

Kumar¹,

Alenezi²,

Ansari³

et al. 2020

IJIES

View full text Add to dashboard Cite

Nowadays, most of the cyber-attacks are initiated by extremely malicious programs known as Malware. Malwares are very vigorous and can penetrate the security of information and communication systems. While there are different techniques available for malware analysis, it becomes challenging to select the most effective approach. In this context, the decision-making process may be an efficient means of empirically assessing the impact of different methods for securing the web applications. In this research study, we have used a methodology that includes the integration of Fuzzy AHP and Fuzzy TOPSIS technique for evaluating the impact of different malware analysis techniques in web application perspective. This study uses different versions of a university’s web application for evaluating the impact of several existing malware analysis techniques. The findings of the study show that the Reverse Engineering approach is the most efficient technique for analyzing complex malware. The outcome of this study would definitely aid the future researchers and developers in selecting the appropriate techniques for scanning the web application code and enhancing the security.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of Malware Analysis Techniques for Securing Web Applications through a Decision-Making Framework under Fuzzy Environment

Kumar¹,

Alenezi²,

Ansari³

et al. 2020

IJIES

View full text Add to dashboard Cite

show abstract

“…In order to get features of apps, a pre-processing in the form of dynamic analysis was applied. Similarly, in the article written by Bahtiyar et al [23], it was tried to predict the malignant apps using multi-layered Stuxnet architecture. It uses regression models.…”

Section: Related Workmentioning

confidence: 99%

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

Arslan

Yurttakal²

2020

iij

View full text Add to dashboard Cite

ABSTRACT Android application platform is making rapid progress in these days. This development has made it the target of malicious application developers. This situation provides a numerical increase in malware apps, diversity in techniques, and rise of damage. Therefore, it is very critical to detect these software and escalation the security of mobile users. Static and dynamic analysis, behaviour scrutiny, machine learning methods are used to ensure security. In this study, K-nearest Neighbourhood (KNN) classifier, one of the machine learning methods, is used. Thus, it is aimed to detect malignant mobile software successfully and quickly. The tests is conducted with dataset includes 492 malware and 697 benign applications. In the proposed algorithm, neighbour number 5 and distance metric is preferred as Minkowski. 80% of dataset randomly selected is reserved for training and 20% for testing. As a result, while 94.1% accuracy is achieved, precision 91.2%, recall 92.7% recall and f1-measure is 92.4%. The high value obtained in f1-measure shows that the proposed model is successful in detecting both malware and benevolent software. The success of using KNN algorithm in classification of malicious apps in the Android has been demonstrated.

show abstract

“…To overcome these disadvantages, a specificationbased technique, which is basically a behavior-based approach, is developed. Researchers have employed data mining and machine learning techniques and obtained good performance in malware detection and classification with high accuracy scores [4][5][6]. These methods provide reliable and accurate results, especially for classifying metamorphic malware.…”

Section: Introductionmentioning

confidence: 99%

“…These signature-based methods fail to detect new, unknown, or obfuscated malware. Data mining and machine learning approaches have overcome disadvantages of signature-based methods by detecting new and unknown malware with high accuracy and detection rate [5,17]. Hence, most of the studies used various machine learning algorithms to detect malware accurately and quickly [4,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…As the volume of cyber-attacks with advanced malware increases, it makes malware hard for detection and classification. Bahtiyar et al [5] proposed a multidimensional machine learning approach to detect advanced malware. They applied various regression models by using five distinguished features: stealthiness, Stuxnet closeness, behavioral instability, conventional malware arsenal and metamorphic engine.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Convolutional Neural Network for Malware Classification Based on API Call Sequence

Schofield¹,

Alicioğlu²,

Binaco³

et al. 2021

Computer Science &Amp; Information Technology (CS &Amp; IT)

View full text Add to dashboard Cite

Malicious software is constantly being developed and improved, so detection and classification of malicious applications is an ever-evolving problem. Since traditional malware detection techniques fail to detect new or unknown malware, machine learning algorithms have been used to overcome this disadvantage. We present a Convolutional Neural Network (CNN) for malware type classification based on the Windows system API (Application Program Interface) calls. This research uses a database of 5385 instances of API call streams labeled with eight types of malware of the source malicious application. We use a 1-Dimensional CNN by mapping API call streams as categorical and term frequency-inverse document frequency (TF-IDF) vectors respectively. We achieved accuracy scores of 98.17% using TF-IDF vector and 95.40% via categorical vector. The proposed 1-D CNN outperformed other traditional classification techniques with overall accuracy score of 91.0%.

show abstract

A multi-dimensional machine learning approach to predict advanced malware

Cited by 28 publications

References 25 publications

Evaluating the Impact of Malware Analysis Techniques for Securing Web Applications through a Decision-Making Framework under Fuzzy Environment

Evaluating the Impact of Malware Analysis Techniques for Securing Web Applications through a Decision-Making Framework under Fuzzy Environment

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

Convolutional Neural Network for Malware Classification Based on API Call Sequence

Contact Info

Product

Resources

About