M. Asha Jerlin

Bio: M. Asha Jerlin is an academic researcher from VIT University. The author has contributed to research in topics: Malware & Factor cost. The author has an hindex of 2, co-authored 6 publications receiving 43 citations.

A New Malware Detection System Using Machine Learning Techniques for API Call Sequences

Abstract: The detection and classification of malwares in windows executables is an important and demanding task in the field of data mining. The malwares can easily damage the system by creating harm in the user's system, so some of the existing techniques are developed in the traditional works for an accurate malware detection. But, it lacks some major drawbacks such as inaccurate detection, not highly efficient, requires a large amount of time to classify the malware type, and an increased computational complexity. To solve these issues, this article develops an efficient system for detecting the malwares in an Application Programmable Interfaces (APIs), and classifying its type as worms, virus, Trojans, or normal. Initially, the input dataset is preprocessed by normalizing the data, then its upper and lower boundaries are estimated during feature extraction. Furthermore, the Rete algorithm is implemented to generate the rules based on the pattern matching process. Here, the Multi-Dimensional Naive Bayes...

A Dynamic Malware Analysis for Windows Platform - A Survey

Abstract: Background: The progression of malware is on upsurge lately. The architects of malware make it robust and sheath such that it becomes untraceable while running and hence users fall-prey for these malicious software. These malicious software programs developed by attackers are polymorphic and metamorphic which have the capability to alter their code as they propagate. Methods: The existing malware detection and prevention tools need to be enhanced when it comes to these newly developed malwares. So, to prevent this we take a generic approach that integrates the methods and tools which already exist in order to detect the malware with utmost accuracy and efficiency. Findings: The survey on this paper gives a picture to make use of Op-Code frequency and n-gram for feature extraction and efficient way for detecting malware incase gets on to the system by any means. Different authors claim that they five the best results by increasing the true positives and decreasing the False positive rates. Application: Dynamic and Hybrid methods can be used to detect known and unknown malwares.

Smart Irrigation system using Internet of Things

Abstract: Central Intelligent Agency (CIA) fact book ranked India a number 2 out of 238 countries. India takes of 17% of world’s population, but with 4% of fresh water resource. Out of which SO% of water is used for agriculture. Country like India, has very good natural resources, but not used in a congruous way. This lead to make water as an adequate resource. So it is time for us to utilized the available water in an efficient way and to amend the victuals productivity of the nation to compete with the world’s growth. The most critical thing is to manage the water system with the available amount of water. In most of the agriculture lands the crops are over watered with out checking the soil dampness. This leads to the waste of water resource which can be utilized in some other areas where there is in need of water. Issue related water system are constantly obstructing the improvement of the nation. So to enhance the water management system, some of the smart techniques are evolved. The paper is to present an internet of things (IOT) based smart irrigation system to identify the dampness in the soil and to control the watering of the crops automatically. The primary motivation behind the ventures to keep up soil dampness level so that there is no damage to the harvests. Soil dampness sensors fundamentally utilized for estimating the gauge volumetric water content. Microcontroller are utilized for getting the information from the water system sensors and after that pass the information on the web utilizing GPRS module. The most intriguing highlights of this activities are shrewd water system with brilliant control and around right choice dependent on the continuous field information. The controlling procedure of these tasks should be possible utilizing the remote sensors or framework associated with the Internet and every one of the activities should be possible by combining every one of the sensors, for example, WI-FI or THINGS SPEAK modules. Since much of the general population are not known with the smart techniques, the primary focus is to make common layman to know and to use these techniques to improve the productivity with adequate resources.

A smart parking system using IoT

Abstract: The primary vision of developing smart cities is to enable growing technologies in to our daily activities, that will helps us to resolve our daily issues in house management, health care management, traffic management system. Due to the overcrowding of cities and increase in the number of vehicles finding a free space to park vehicles has become a major issue to the drivers especially in peak hours. Though many traditional approaches and technologies are deployed there have been many flaws are suspected and identified. Though lot of solutions has been proposed over the parking solution problems they have certain limitation and constraints over the devices or technology used as well as the cost factor required for implementation. So considering such factors we have proposed a prototype model to experiment our system which can very effectively optimise the parking solution with low-cost parking solutions.

Design and analysis of smart card-based authentication scheme for secure transactions

Abstract: Remote authentication scheme utilising smart cards have become a prevalent concept due to their convenience and simplicity. Recently, Lee (2015) proposed a low cost authentication scheme without verifier tables. However, in this paper we show that Lee's scheme is susceptible to various attacks and fails to provide essential security properties. We then present our own scheme and perform an informal analysis to substantiate the claim that our scheme is able to resist the previous scheme's weaknesses.

Ransomware detection using machine learning algorithms

Abstract: The number of ransomware variants has increased rapidly every year, and ransomware needs to be distinguished from the other types of malware to protect users' machines from ransomware‐based attacks. Ransomware is similar to other types of malware in some aspects, but other characteristics are clearly different. For example, ransomware generally conducts a large number of file‐related operations in a short period of time to lock or to encrypt files of a victim's machine. The signature‐based malware detection methods, which have difficulties to detect zero‐day ransomware, are not suitable to protect users' files against the attacks caused by risky unknown ransomware. Therefore, a new protection mechanism specialized for ransomware is needed, and the mechanism should focus on ransomware‐specific operations to distinguish ransomware from other types of malware as well as benign files. This paper proposes a ransomware detection method that can distinguish between ransomware and benign files as well as between ransomware and malware. The experimental results show that our proposed method can detect ransomware among malware and benign files.

A New Malware Detection System Using Machine Learning Techniques for API Call Sequences

Abstract: The detection and classification of malwares in windows executables is an important and demanding task in the field of data mining. The malwares can easily damage the system by creating harm in the user's system, so some of the existing techniques are developed in the traditional works for an accurate malware detection. But, it lacks some major drawbacks such as inaccurate detection, not highly efficient, requires a large amount of time to classify the malware type, and an increased computational complexity. To solve these issues, this article develops an efficient system for detecting the malwares in an Application Programmable Interfaces (APIs), and classifying its type as worms, virus, Trojans, or normal. Initially, the input dataset is preprocessed by normalizing the data, then its upper and lower boundaries are estimated during feature extraction. Furthermore, the Rete algorithm is implemented to generate the rules based on the pattern matching process. Here, the Multi-Dimensional Naive Bayes...

FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques: FSDroid.

Abstract: With the recognition of free apps, Android has become the most widely used smartphone operating system these days and it naturally invited cyber-criminals to build malware-infected apps that can steal vital information from these devices. The most critical problem is to detect malware-infected apps and keep them out of Google play store. The vulnerability lies in the underlying permission model of Android apps. Consequently, it has become the responsibility of the app developers to precisely specify the permissions which are going to be demanded by the apps during their installation and execution time. In this study, we examine the permission-induced risk which begins by giving unnecessary permissions to these Android apps. The experimental work done in this research paper includes the development of an effective malware detection system which helps to determine and investigate the detective influence of numerous well-known and broadly used set of features for malware detection. To select best features from our collected features data set we implement ten distinct feature selection approaches. Further, we developed the malware detection model by utilizing LSSVM (Least Square Support Vector Machine) learning approach connected through three distinct kernel functions i.e., linear, radial basis and polynomial. Experiments were performed by using 2,00,000 distinct Android apps. Empirical result reveals that the model build by utilizing LSSVM with RBF (i.e., radial basis kernel function) named as FSdroid is able to detect 98.8% of malware when compared to distinct anti-virus scanners and also achieved 3% higher detection rate when compared to different frameworks or approaches proposed in the literature.

Malware Detection Issues, Challenges, and Future Directions: A Survey

Abstract: The evolution of recent malicious software with the rising use of digital services has increased the probability of corrupting data, stealing information, or other cybercrimes by malware attacks. Therefore, malicious software must be detected before it impacts a large number of computers. Recently, many malware detection solutions have been proposed by researchers. However, many challenges limit these solutions to effectively detecting several types of malware, especially zero-day attacks due to obfuscation and evasion techniques, as well as the diversity of malicious behavior caused by the rapid rate of new malware and malware variants being produced every day. Several review papers have explored the issues and challenges of malware detection from various viewpoints. However, there is a lack of a deep review article that associates each analysis and detection approach with the data type. Such an association is imperative for the research community as it helps to determine the suitable mitigation approach. In addition, the current survey articles stopped at a generic detection approach taxonomy. Moreover, some review papers presented the feature extraction methods as static, dynamic, and hybrid based on the utilized analysis approach and neglected the feature representation methods taxonomy, which is considered essential in developing the malware detection model. This survey bridges the gap by providing a comprehensive state-of-the-art review of malware detection model research. This survey introduces a feature representation taxonomy in addition to the deeper taxonomy of malware analysis and detection approaches and links each approach with the most commonly used data types. The feature extraction method is introduced according to the techniques used instead of the analysis approach. The survey ends with a discussion of the challenges and future research directions.

System Call Analysis of Android Malware Families

Abstract: Background/Objectives: Now a days, Android Malware is coded so wisely that it has become very difficult to detect them. The static analysis of malicious code is not enough for detection of malware as this malware hides its method call in encrypted form or it can install the method at runtime. The system call tracing is an effective dynamic analysis technique for detecting malware as it can analyze the malware at the run time. Moreover, this technique does not require the application code for malware detection. Thus, this can detect that android malware also which are difficult to detect with static analysis of code. As Android was launched in 2008, so there were fewer studies available regarding the behavior of Android Malware Families and their characteristics. The aim of this work is to explore the behavior of 10 popular Android Malware Families focused on System Call Pattern of these families. Methods/Statistical Analysis: For this purpose, the authors have extracted the system call trace of 345 malicious applications from 10 Android Malware Families named FakeInstaller, Opfake, Plankton, DroidKungFu, BaseBridge, Iconosys, Kmin, Adrd and Gappusin using strace android tool and compared it with the system calls pattern of 300 Benign Applications to justify the behavior of malicious application. Findings: During the experiment, it is observed that the malicious applications invoke some system calls more frequently than benign applications. Different Android malware invokes the different set of system calls with different frequency. Applications/Improvements: This analysis can prove helpful in designing intrusion-detection systems for an android mobile device with more accuracy.