Hybrid Analysis Technique to detect Advanced Persistent Threats

2018 ◽  
Vol 14 (2) ◽  
pp. 59-76 ◽  
Author(s):  
S Sibi Chakkaravarthy ◽  
V Vaidehi ◽  
P Rajesh
Keyword(s):  
Current System ◽  
Detection Methods ◽  
System State ◽  
Hybrid Analysis ◽  
Dynamic Memory ◽  
Advanced Persistent Threats ◽  
Analysis Technique ◽  
Clean System ◽  
Behavior Based ◽  
State Analysis

Advanced persistent threats (APT) are major threats in the field of system and network security. They are extremely stealthy and use advanced evasion techniques like packing and behaviour obfuscation to hide their malicious behaviour and evade the detection methods. Existing behavior-based detection technique fails to detect the APTs due to its high persistence mechanism and sophisticated code nature. Hence, a novel hybrid analysis technique using Behavior based Sandboxing approach is proposed. The proposed technique consists of four phases namely, Static, Dynamic, Memory and System state analysis. Initially, static analysis is performed on the sample which involves packer detection and signature verification. If the sample is found stealthy and remains undetected, then it is executed inside a sandbox environment to analyze its behavior. Further, memory analysis is performed to extract memory artefacts of the current system state. Finally, system state analysis is performed by correlating clean system state and infected system state to determine whether the system is compromised

Optimising design decision-making for steel structures in fire using a hybrid analysis technique

2017 ◽  
Vol 91 ◽  
pp. 532-541 ◽  
Author(s):  
Obinna Akaa ◽  
Anthony Abu ◽  
Michael Spearpoint ◽  
Sonia Giovinazzi
Keyword(s):  
Decision Making ◽  
Steel Structures ◽  
Design Decision ◽  
Hybrid Analysis ◽  
Analysis Technique ◽  
In Fire

scholarly journals MALGRA: Machine Learning and N-Gram Malware Feature Extraction and Detection System

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1777
Author(s):  
Muhammad Ali ◽  
Stavros Shiaeles ◽  
Gueltoum Bendiab ◽  
Bogdan Ghita
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Detection Methods ◽  
Normal Operation ◽  
Analysis Technique ◽  
N Gram

Detection and mitigation of modern malware are critical for the normal operation of an organisation. Traditional defence mechanisms are becoming increasingly ineffective due to the techniques used by attackers such as code obfuscation, metamorphism, and polymorphism, which strengthen the resilience of malware. In this context, the development of adaptive, more effective malware detection methods has been identified as an urgent requirement for protecting the IT infrastructure against such threats, and for ensuring security. In this paper, we investigate an alternative method for malware detection that is based on N-grams and machine learning. We use a dynamic analysis technique to extract an Indicator of Compromise (IOC) for malicious files, which are represented using N-grams. The paper also proposes TF-IDF as a novel alternative used to identify the most significant N-grams features for training a machine learning algorithm. Finally, the paper evaluates the proposed technique using various supervised machine-learning algorithms. The results show that Logistic Regression, with a score of 98.4%, provides the best classification accuracy when compared to the other classifiers used.

scholarly journals A Travel Behavior-Based Skip-Stop Strategy Considering Train Choice Behaviors Based on Smartcard Data

2019 ◽  
Vol 11 (10) ◽  
pp. 2791 ◽  
Author(s):  
Eun Hak Lee ◽  
Inmook Lee ◽  
Shin-Hyung Cho ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Choice Model ◽  
Current System ◽  
Optimal Solution ◽  
Multinomial Logit Model ◽  
Gaussian Mixture ◽  
Total Travel Time ◽  
Facility Condition ◽  
Behavior Based

This study analyzes a skip-stop strategy considering four types of train choice behavior with smartcard data. The proposed model aims to minimize total travel time with realistic constraints such as facility condition, operational condition, and travel behavior. The travel time from smartcard data is decomposed by two distributions of the express trains and the local trains using a Gaussian mixture model. The utility parameters of the train choice model are estimated with the decomposed distribution using the multinomial logit model. The optimal solution is derived by a genetic algorithm to designate the express stations of the Bundang line in the Seoul metropolitan area. The results indicate the travel times of the transfer-based strategy and the high ridership-based strategy are estimated to be 21.2 and 19.7 min/person, respectively. Compared to the travel time of the current system, the transfer-based strategy has a 5.8% reduction and the high ridership-based strategy has a 12.2% reduction. For the travel behavior-based strategy, the travel time was estimated to be 18.7 minutes, the ratio of the saved travel time is 17.9%, and the energy consumption shows that the travel behavior-based strategy consumes 305,437 (kWh) of electricity, which is about 12.7% lower compared to the current system.

Applications of Infrared Spectroscopy in the Study of Wood Plastic Composites

2010 ◽  
Vol 113-116 ◽  
pp. 2003-2006 ◽  
Author(s):  
Dong Fang Li ◽  
Li Li ◽  
Jin Chi Zhou
Keyword(s):  
Infrared Spectroscopy ◽  
Detection Methods ◽  
Wood Plastic Composite ◽  
Wood Powder ◽  
Spectroscopy Analysis ◽  
Filling Materials ◽  
Plastic Composite ◽  
Plastic Composites ◽  
Environmental Friendly ◽  
Analysis Technique

Wood plastic composite (WPC) is an environmental-friendly material, which is made of virgin or used wood powder and plastic. It is necessary to find the detection methods of WPC with the development of WPC in further studies. For resolving some problems existing in the study of WPC, this article introduces the application of the Infrared Spectroscopy analysis technique in matrix materials, filling materials, supplementary materials, and production process of WPC and forecast the widely usage of Infrared Reflectance (IR) Spectroscopy in the study of WPC.

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