A New Generic Taxonomy on Hybrid Malware Detection Technique
Y. Robiah, S. Siti Rahayu, M. Mohd Zaki, S. Shahrin, M. A. Faizal, R. Marliza
备注：
6 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423, http://sites.google.com/site/ijcsis/
发表信息：
Robiah Y, Siti Rahayu S., Mohd Zaki M, Shahrin S., Faizal M. A., Marliza R., International Journal of Computer Science and Information Security, IJCSIS, Vol. 5, No. 1, pp. 56-61, September 2009, USA
Malware is a type of malicious program that replicate from host machine and propagate through network. It has been considered as one type of computer attack and intrusion that can do a variety of malicious activity on a computer. This paper addresses the current trend of malware detection techniques and identifies the significant criteria in each technique to improve malware detection in Intrusion Detection System (IDS). Several existing techniques are analyzing from 48 various researches and the capability criteria of malware detection technique have been reviewed. From the analysis, a new generic taxonomy of malware detection technique have been proposed named Hybrid Malware Detection Technique (Hybrid MDT) which consists of Hybrid Signature and Anomaly detection technique and Hybrid Specification based and Anomaly detection technique to complement the weaknesses of the existing malware detection technique in detecting known and unknown attack as well as reducing false alert before and during the intrusion occur.
Malware such as banking Trojans are popular with financially-motivated cybercriminals. Detection of banking Trojans remains a challenging task, due to the constant evolution of techniques used to obfuscate and circumvent existing detection and security solutions. Having a malware taxonomy can facilitate the design of mitigation strategies such as those based on evolutionary computational intelligence. Specifically, in this paper, we propose a cyber kill chain based taxonomy of banking Trojans features. This threat intelligence based taxonomy providing a stage-by-stage operational understanding…
Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University（北京交通大学智能交通信息安全与隐私保护北京市重点实验室）
Malware is being increasingly threatening and malware detectors based on traditional signature-based analysis are no longer suitable for current malware detection. Recently, the models based on machine learning (ML) are developed for predicting unknown malware variants and saving human strength. However, most of the existing ML models are black-box, which made their pre-diction results undependable, and therefore need further interpretation in order to be effectively deployed in the wild. This paper aims to examine and categorize the existing researches on ML-based malware detector interpretab…
Nowadays, Android is the most dominant operating system in the mobile ecosystem, with billions of people using its apps daily. As expected, this trend did not go unnoticed by miscreants, and Android became the favorite platform for discovering new victims through malicious apps. Moreover, these apps have become so sophisticated that they can bypass anti-malware measures to protect the users. Therefore, it is safe to admit that traditional anti-malware techniques have become cumbersome, sparking the urge to develop an efficient way to detect Android malware. This paper presents hybrid-Flacon, a…
San Jose State University（圣何塞州立大学）；Università degli Studi del Sannio（桑尼奥大学）
In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs ) on both static and dynamic feature sets and compare the resulting detection rates over a substantial number of malware families. We also consider hybrid cases, where dynamic analysis is used in the training phase, with static techniques used in the detection phase, and vice versa. In our experiments, a fully dynamic approach generally yields the best detection rates. We discuss the implications of this research for malware detection based …
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Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspects of static and dynamic malware investigation. We believe that machine learning aided static analysis can be used as a methodological approach in tech…
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Unraveling the Key of Machine Learning-based Android Malware Detection
National University of Singapore（新加坡国立大学）；University College London（伦敦大学学院）；The State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）
Accelerating Incident Response: A Hybrid Approach for Data Breach Reporting
IMT School for Advanced Studies Lucca（卢卡高等研究学院）；University of Cagliari（卡利亚里大学）；University of Salerno（萨莱诺大学）；Sant’Anna School of Advanced Studies（圣安娜高等研究学院）；University of Camerino（卡梅里诺大学）
Pakistan Institute of Engineering & Applied Sciences（巴基斯坦工程与应用科学学院）；PIEAS Artificial Intelligence Center（PIEAS人工智能中心）；Centre for Mathematical Sciences, Pakistan Institute of Engineering and Applied Sciences（巴基斯坦工程与应用科学学院数学科学中心）；College of Computer and Information Sciences, King Saud University（沙特国王大学计算机与信息科学学院）
In the case of malware analysis, categorization of malicious files is an essential part after malware detection. Numerous static and dynamic techniques have been reported so far for categorizing malware. This research presents a deep learning-based malware detection (DLMD) technique based on static methods for classifying different malware families. The proposed DLMD technique uses both the byte and ASM files for feature engineering, thus classifying malware families. First, features are extracted from byte files using two different Deep Convolutional Neural Networks (CNN). After that, essenti…
Android is the most popular OS worldwide. Therefore, it is a target for various kinds of malware. As a countermeasure, the security community works day and night to develop appropriate Android malware detection systems, with ML-based or DL-based systems considered as some of the most common types. Against these detection systems, intelligent adversaries develop a wide set of evasion attacks, in which an attacker slightly modifies a malware sample to evade its target detection system. In this survey, we address problem-space evasion attacks in the Android OS, where attackers manipulate actual A…
Malicious activities in cyberspace have gone further than simply hacking machines and spreading viruses. It has become a challenge for a nations survival and hence has evolved to cyber warfare. Malware is a key component of cyber-crime, and its analysis is the first line of defence against attack. This work proposes a novel deep boosted hybrid learning-based malware classification framework and named as Deep boosted Feature Space-based Malware classification (DFS-MC). In the proposed framework, the discrimination power is enhanced by fusing the feature spaces of the best performing customized …
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The popularity of Windows attracts the attention of hackers/cyber-attackers, making Windows devices the primary target of malware attacks in recent years. Several sophisticated malware variants and anti-detection methods have been significantly enhanced and as a result, traditional malware detection techniques have become less effective. This work presents MalBehavD-V1, a new behavioural dataset of Windows Application Programming Interface (API) calls extracted from benign and malware executable files using the dynamic analysis approach. In addition, we present MalDetConV, a new automated beha…
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University of Trento（特伦托大学）；Hongik University（弘益大学）；University of Dayton（代顿大学）；Purdue University Fort Wayne（普渡大学韦恩堡分校）
Modern malware evolves various detection avoidance techniques to bypass the state-of-the-art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning techniques to classify and detect malware. However, existing works in this field only perform simple image transformation methods that limit the accuracy of the detection. In this paper, we introduce a novel approach to classify malware by using a deep network on images transformed from binary samples. In particular, we first develop a novel hybrid image transformation method to …
Gebze Technical University（盖布泽技术大学）；Kadir Has University（卡迪尔哈斯大学）
Nowadays, malware and malware incidents are increasing daily, even with various antivirus systems and malware detection or classification methodologies. Machine learning techniques have been the main focus of the security experts to detect malware and determine their families. Many static, dynamic, and hybrid techniques have been presented for that purpose. In this study, the static analysis technique has been applied to malware samples to extract API calls, which is one of the most used features in machine/deep learning models as it represents the behavior of malware samples. Since the rapid …
The current pandemic situation has increased cyber-attacks drastically worldwide. The attackers are using malware like trojans, spyware, rootkits, worms, ransomware heavily. Ransomware is the most notorious malware, yet we did not have any defensive mechanism to prevent or detect a zero-day attack. Most defensive products in the industry rely on either signature-based mechanisms or traffic-based anomalies detection. Therefore, researchers are adopting machine learning and deep learning to develop a behaviour-based mechanism for detecting malware. Though we have some hybrid mechanisms that perf…
Android is the predominant mobile operating system for the past few years. The prevalence of devices that can be powered by Android magnetized not merely application developers but also malware developers with criminal intention to design and spread malicious applications that can affect the normal work of Android phones and tablets, steal personal information and credential data, or even worse lock the phone and ask for ransom. Researchers persistently devise countermeasures strategies to fight back malware. One of these strategies applied in the past five years is the use of deep learning me…
Combining Post-Quantum Cryptography and Quantum Key Distribution: Taxonomy, Constructions, Proofs, Standards, Deployments and Open Problems
College of Science and Engineering, Qatar University（卡塔尔大学科学与工程学院）；Qatar Center for Quantum Computing, College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院量子计算中心）
