Monitoring the Performance of Lecturers Using Behaviorally Anchor Rating Scale and Management by Objectives Method

March 1, 2023/in Publikasi /by Program Studi Informatika

Monitoring the Performance of Lecturers Using Behaviorally Anchor Rating Scale and Management by Objectives Method

by Muhammad Sabiq Dzakwan, Sunardi Sunardi, Anton Yudhana
JUITA: Jurnal Informatika, jurnalnasional.ump.ac.id

Mutiara Mahakam Samarinda Institute of Health Sciences (STIKES-MM Samarinda) has a system for monitoring and evaluating the performance of lecturers or education staff. This system measures performance achievements in terms of teaching, research, and community service. . Nevertheless, since every segment of the system is not yet fully computerized, this then raises several obstacles in the process of monitoring and evaluating the performance, length of time to obtain the final assessment results and the low accuracy level of the assessment. This study aims to seeks solutions to these obstacles and offers an educator performance monitoring system that combines the Behaviorally Anchor Rating Scale (BARS) and Management by Objectives (MBO) methods to be assessed quantitatively based on the rating scores in measuring the two methods. The BARS method was focused on evaluating behaviour that would affect overall performance with an average score of 4.14%, while the MBO method was focused on evaluating according to Tri Dharma of higher education, namely teaching, research and community service. The assessment system was then implemented to evaluate the performance of lecturers and education staff. Subsequently, the data obtained were analyzed to get the final result of the assessment. In particular for data from the MBO method, the analysis was carried out using step with and without KRA. This exploratory research succeeded in presenting the final results of the performance assessment of each lecturer who was assessed for both the value of the BARS and MBO methods. Data analysis from the MBO method , when calculated with and without KRA and KRA, showed some significant differences in MBO. For all lecturers, the difference in scores, if the average was 3.48%, then this assessment was more inclined to the BARS assessment, which had a better rating than MBO.

source: https://jurnalnasional.ump.ac.id/index.php/JUITA/article/view/15354

Rise of spam and compromised accounts in online social networks: A state-of-the-art review of different combating approaches

March 1, 2023/in Publikasi /by Program Studi Informatika

Rise of spam and compromised accounts in online social networks: A state-of-the-art review of different combating approaches

by R Kaur, S Singh, H Kumar
Journal of Network and Computer Applications, Elsevier

Ever increasing fame and obsession for social networks has also coxswained a dramatic increase in the presence of malicious activities. As a result, various researchers have proposed different features and techniques to detect and reduce this menace. This paper presents an expository study of various state-of-the-art techniques to detect two most interlinked apprehensive problems on social networks namely, spam detection and detection-cum-analysis of compromised accounts. It is evident from the ongoing statistics that despite profuse awareness and some anti-spam policies and techniques being developed, to everyone’s surprise, the severity of spam has only increased. Moreover, with the growing smartness of spammers, existing techniques get bypassed and new features and techniques continuously keep on evolving. Therefore, in continuation to the ongoing research, a study comprising of a comprehensive analysis of different works is also required from time to time. The growing inclination of spammers to compromise the legitimate accounts has evolved as an evasive and more beneficial way to spread spam. Therefore, it has become highly relevant to review the techniques related to the detection of compromised accounts so as to track the spammers adhering to this behavior. In this work, we have performed a qualitative analysis of each paper discussing its pros and cons. In both the domains, the detection approaches have been placed under different categories and have been thoroughly reviewed stating their applicability. In conclusion, the paper carries a discussion of various gaps prevalent in the existing approaches and the corresponding actions to be taken to address them, providing a strong foundation for future researches to be carried out in this domain.

Performance comparison of classifiers on reduced phishing website dataset

March 1, 2023/in Publikasi /by Program Studi Informatika

Performance comparison of classifiers on reduced phishing website dataset

by M Karabatak, T Mustafa

2018 6th International Symposium

source: freepick

The Internet is becoming a necessary and important tool in everyday life. However, Internet users might have poor security for different kinds of web threats, which may lead to financial loss or clients lacking trust in online trading and banking. Phishing is described as a skill of impersonating a trusted website aiming to obtain private and secret information such as a username and password or social security and credit card number. In this paper, phising website dataset taken from UCI was investigated. Its dimension was reduced and the performance comparison of classification algorithms is studied on reduced phishing website dataset. Phishing website dataset was taken from UCI machine learning repository. This dataset consists of 11055 records and 31 features. Feature selection algorithms were applied to reduce the dimension of phishing website dataset and to obtain higher classification performance. Then, the performance of classification algorithms is compared to other data mining classification algorithms. Finally, a comparative classification performance on the reduced dataset by using the common classification algorithms is given.

Source: https://ieeexplore.ieee.org/abstract/document/8355357

AMD‐CNN: Android malware detection via feature graph and convolutional neural networks

February 22, 2023/in Publikasi /by Program Studi Informatika

AMD‐CNN: Android malware detection via feature graph and convolutional neural networks

by R. ArslanMurat Tasyurek

Android malware has become a serious threat to mobile device users, and effective detection and defence architectures are needed to solve this problem. Recently, machine learning techniques have been widely used to deal with Android malicious apps. These methods are based on a simple feature set and have difficulty detecting up‐to‐date malware. Therefore, more robust and efficient classification methodologies are needed. In this article, AMD‐CNN, an Android malware detection tool, is proposed, and it uses graphical representations to detect malicious apks. In the first step, the features related to the androidmanifest.xml file are extracted and converted into a vector consisting of one or zero. The feature vector is then converted to 2D‐code images and used in training the CNN network. The model needs low‐resource consumption to run on mobile devices and allow real‐time applications to be analyzed. The experiments with 1920 malicious and benign apks show that the malware detection rate (accuracy) was 96.2% and precision, recall, and F‐score values were 97.9%, 98.2%, and 98.1%, respectively. The average time and memory space to analyze each application are 0.035 s and 3.38 MB. AMD‐CNN is an efficient and robust tool and has advantages over previous studies.

An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks

February 22, 2023/in Publikasi /by Program Studi Informatika

An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks

by Iman M. AlmomaniAala AlkhayerW. El-shafai

Recently, cybersecurity experts and researchers have given special attention to developing cost-effective deep learning (DL)-based algorithms for Android malware detection (AMD) systems. However, the conventional AMD solutions necessitate extensive computations to achieve high accuracy in detecting Android malware apps. Consequently, there is a significant benefit in utilizing convolution neural networks (CNNs) in vision-based AMD applications to quickly and efficiently learn without prior stages of reverse engineering processes. Thus, this paper introduces an efficient and automated vision-based AMD model composed of 16 well-developed and fine-tuned CNN algorithms. This model precludes the need for a pre-designated features extraction process while generating accurate predictions of malware images with minimum cost and high detection speed. Such performance is achieved with colored or grayscale malware images, whether by using balanced or imbalanced datasets. Firstly, the bytecodes of the “classes.dex” files extracted from the Android benign and malware apps were converted to color and grayscale visual images before forwarding them to the developed CNN algorithms for classification. Then, the detection efficiency of the proposed AMD model was examined and evaluated using the imbalanced benchmark Leopard Android dataset that composes 14733 samples of malware apps and 2486 samples of benign apps. Finally, different experimental scenarios were conducted using balanced and imbalanced Android samples of color and grayscale images generated from the Leopard dataset; to extensively and sufficiently validate the detection and classification performance of the suggested model. Comprehensive assessment classification parameters in the evaluation experiments were applied to prove the high capability of the developed fine-tuned CNN algorithms in recognizing Android malware attacks with low computational overhead. As a result, the detection accuracy reached 99.40% for balanced samples and 98.05% for imbalanced samples. Furthermore, the proposed AMD model outperforms the existing approaches that utilize conventional vision-based algorithms and are tested on the same benchmark Android dataset.

DroidFDR: Automatic Classification of Android Malware Using Model Checking

February 20, 2023/in Publikasi /by Program Studi Informatika

DroidFDR: Automatic Classification of Android Malware Using Model Checking

by Zhi YangFang ChaoXing-yuan ChenShuyuan JinLei SunXuehui Du

Android faces an increasing threat of malware attacks. The few existing formal detection methods have drawbacks such as complex code modeling, incomplete and inaccurate expression of family properties, and excessive manual participation. To this end, this paper proposes a formal detection method, called DroidFDR, for Android malware classification based on communicating sequential processes (CSP). In this method, the APK file of an application is converted to an easy-to-analyze representation, namely Jimple, in order to model the code behavior with CSP. The process describing the behavior of a sample is inputted to an FDR model checker to be simplified and verified against a process that is automatically abstracted from the malware to express the property of a family. The sample is classified by detecting whether it has the typical behavior of any family property. DroidFDR can capture the behavioral characteristics of malicious code such as control flow, data flow, procedure calls, and API calls. The experimental results show that the automated method can characterize the behavior patterns of applications from the structure level, with a high family classification accuracy of 99.06% in comparison with another formal detection method.

Evaluating Machine Learning Classifiers to detect Android Malware

February 13, 2023/in Publikasi /by Program Studi Informatika

Evaluating Machine Learning Classifiers to detect Android Malware

by Prerna AgrawalBhushan Trivedi

Malware Detection using conventional methods is incompetent to detect new and generic malware. For the investigation of a variety of malware, there were no ready-made machine learning datasets available for malware detection. So we generated our dataset by downloading a variety of malware files from the world’s famous malware projects. By performing unstructured data collection from the downloaded APK files and feature mining process the final dataset was generated with 16300 records and a total of 215 features. There was a need to evaluate the performance of the generated dataset with supervised machine learning classifiers. So in this paper, we propose a malware detection approach using different supervised machine learning classifiers. Here supervised algorithms, Feature Reduction Techniques, and Ensembling techniques are used to evaluate the performance of the generated dataset. Machine Learning classifiers are evaluated on the evaluation parameters like AUC, FPR, TPR, Cohen Kappa Score, Precision, and Accuracy. We also represented the results of classifiers using Bar plots of Accuracy and plotting the ROC curve. From the results of machine learning classifiers, the performance of the CatBoost Classifier is highest with Accuracy 93.15% having a value of ROC curve as 0.91 and Cohen Kappa Score as 81.56%.

Malware Analysis in IoT & Android Systems with Defensive Mechanism

February 10, 2023/in Publikasi /by Program Studi Informatika

Malware Analysis in IoT & Android Systems with Defensive Mechanism

by Chandra Shekhar YadavJagendra SinghAruna YadavHimansu Sekhar PattanayakRavindra KumarA. KhanMohd Anul HaqAhmed AlhussenSultan Alharby

The Internet of Things (IoT) and the Android operating system have made cutting-edge technology accessible to the general public. These are affordable, easy-to-use, and open-source technology. Android devices connect to different IoT devices such as IoT-enabled cameras, Alexa powered by Amazon, and various other sensors. Due to the escalated growth of Android devices, users are facing cybercrime through their Android devices. This article aims to provide a comprehensive study of the IoT and Android systems. This article classifies different attacks on IoT and Android devices and mitigation strategies proposed by different researchers. The article emphasizes the role of the developer in secure application design. This article attempts to provide a relative analysis of several malware detection methods in the different environments of attacks. This study expands the awareness of certain application-hardening strategies applicable to IoT devices and Android applications and devices. This study will help domain experts and researchers to gain knowledge of IoT systems and Android systems from a security point of view and provide insight into how to design more efficient, robust, and comprehensive solutions. This article discusses different attack vectors and mitigation strategies available to both developers and in the open domain. Certain guidelines are also suggested for application and platform developers, as well as application databases (Google play store), to limit the risk of attack, and users can form their own defense with knowledge regarding keeping hardware and software updated and securing their system with a strong password.

Topographic property of backpropagation artificial neural network: From human functional connectivity network to artificial neural network

January 29, 2023/in Publikasi /by Informatika

Topographic property of backpropagation artificial neural network: From human functional connectivity network to artificial neural network

by Heng Chen, Fengmei Lu, Bifang He

Neurocomputing, Elsevier BV

The human brain network is one of the most complex data processing centers so far and shows several topographic features such as small worldness, modularity, and high efficiency. Inspired by the human brain network, the artificial neural network (ANN) method was proposed and has become an important engineering tool for pattern recognition. A recent study suggested the similarity between the human brain network and ANN. Would the ANN show topographic features that are similar to those of the human brain network? In the present study, we first constructed a simple backpropagation ANN (BP-ANN) with one hidden layer, which is one of the simplest, representative ANN, based on CIFAR10 and CIFAR100 datasets. Then, we explored the topographic features of a network with artificial neurons. We found that artificial neurons of the BP-ANN showed a high local efficiency and modularity similar to the human brain network. However, the global efficiency of artificial neurons of the BP-ANN was comparable with those of a random network. Our results showed that the best model (high accuracy and no overfitting) of a large-scale BP-ANN during epochs of training showed the lowest local and global efficiency. These results might imply that after epochs of training, neurons of the BP-ANN form local highly intra-connected functional modules but lack cooperation at the global level. The topography structure of the BP-ANN also shows the potential to become an early-stop marker during training.

Big Data for 5G Intelligent Network Slicing Management

January 23, 2023/in Publikasi /by Informatika

Big Data for 5G Intelligent Network Slicing Management

by Hatim Chergui, Christos Verikoukis

Institute of Electrical and Electronics Engineers (IEEE)

Network slicing is a powerful tool to harness the full potential of 5G systems. It allows verticals to own and exploit independent logical networks on top of the same physical infrastructure. Motivated by the emergence of the big data paradigm, this article focuses on the enablers of big-databased intelligent network slicing. The article starts by revisiting the architecture of this technology that consists of data collection, storage, processing, and analytics before it highlights their relationship with network slicing concepts and the underlying trade-offs. It then proposes a complete framework for implementing big-data-driven dynamic slicing resource provisioning while respecting SLAs. This includes the development of low-complexity slices’ traffic predictors, resource allocation models, and SLA enforcement via constrained deep learning. The article finally identifies the key challenges and open research directions in this emerging area.

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