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.

Machine Learning-Driven Scaling and Placement of Virtual Network Functions at the Network Edges

January 21, 2023/in Publikasi /by Informatika

Machine Learning-Driven Scaling and Placement of Virtual Network Functions at the Network Edges

by Tejas Subramanya, Roberto Riggio

2019 IEEE Conference on Network Softwarization (NetSoft)

Network Function Virtualization is a promising technology that proposes to decouple the network functions from their underlying hardware and transform them into software entities called Virtual Network Functions (VNFs). This approach offers network operators with more flexibility to instantiate, configure, scale, and migrate VNFs at runtime depending on the demand. On introducing these VNFs at the network edges (e.g., base stations), emerging use cases such as connected cars can be supported. However, in such an environment, efficient VNF placement and orchestration mechanisms are needed to address the challenges of continuously changing network dynamics, service latency requirements and user mobility patterns. The purpose of this paper is twofold. Firstly, we propose a neural-network model (i.e., a subset of machine learning) that can assist in proactive auto-scaling by predicting the number of VNF instances required as a function of the network traffic they should process. Based on the traffic traces collected over a commercial mobile network, the model achieves a prediction accuracy of 97%. Secondly, we provide an Integer Linear Programming formulation for placing these VNFs at the edge nodes with a primary objective of minimizing end-to-end latency from all users to their respective VNFs. Our results show an improvement in latency by upto 75% when VNFs are placed at the network edges.

Botnet detection based on network flow summary and deep learning

January 14, 2023/in Publikasi /by Informatika

Botnet detection based on network flow summary and deep learning

by Abdurrahman Pektaş, Tankut Acarman

International Journal of Network Management

A botnet is a group of compromised Internet-connected devices controlled remotely by cyber criminals to launch coordinated attacks and to perform various malicious activities. Since botnets continuously adapt themselves to the evolving countermeasures introduced by both network and host-based detection mechanism, the traditional approaches do not provide adequate protection to botnet threat. On the one hand, behavioral analysis of network traffic can play a key role to detect botnets. For instance, behavioral analysis can be applied to observe and discover communication patterns that botnets operate during their life cycle. On the other hand, deep learning has been successfully applied to various classification tasks, and it is also a promising solution for botnet discovery. In this paper, we apply deep neural network to detect botnet by modeling network traffic flow. The performance of the proposed method is evaluated with publicly available large-scale communication traces. The experimental results illustrate that deep learning is an efficient and effective method for identifying botnet traffic with a high true positive rate (attack detection rate) and low false positive alarm rate.

Network anomaly detection based on logistic regression of nonlinear chaotic invariants

January 13, 2023/in Publikasi /by Informatika

Network anomaly detection based on logistic regression of nonlinear chaotic invariants

by Francesco Palmieri

Journal of Network and Computer Applications

Network anomaly detection has the essential goal of reliably identifying malicious activities within traffic observations collected at specific monitoring points, in order to raise alarms and timely trigger specific reactions and countermeasures. This, ideally, should be done also in presence of previously unknown phenomena, also known as zero-day attacks. However, distinguishing anomalous events due to attacks from normal spikes or sharp variations in traffic flows can become a classic “finding a needle in a haystack” problem, due to the very complex and unpredictable nature of Internet traffic, which is extremely affected by randomness and background noise effects. To face this challenge we leveraged machine learning for developing a novel network anomaly detection solution, based on the exploitation of nonlinear invariant properties of the Internet traffic. These properties, by capturing its chaotic and fractal features, are better suited to represent the more intrinsic and discriminative dynamics within an inductively learned model to be used for effectively classifying, through logistic regression, previously unseen traffic aggregates or individual flows into “normal” or “anomalous” ones. The results of the performance evaluation, obtained within a standard and reproducible experimental validation framework, show that the approach is able to effectively isolate very different kinds of volumetric Denial of Service attacks within the context of complex mixes of traffic flows, with really satisfactory accuracy and precision.

Reliability for Smart Healthcare: A Network Slicing Perspective

January 11, 2023/in Publikasi /by Informatika

Reliability for Smart Healthcare: A Network Slicing Perspective

by Andressa Vergutz, Guevara Noubir, Michele Nogueira

Institute of Electrical and Electronics Engineers (IEEE)

Pursuing improvements in the healthcare system is mandatory for its efficiency and cost reduction. The fast popularization of implantable and wearable sensors promotes the diversity of healthcare applications and services, ranging from real-time and critical care monitoring to telemedicine. For smart healthcare (s-health), reliability plays an essential role, given the sensitivity of its data and services. In this article, we envision an architecture based on network slicing that can provide reliability for s-health applications and services. The architecture relies on fingerprinting healthcare applications to quickly customize resources and meet the level of reliability required for each s-health application. A fingerprinting study case is presented for s-health, based on a dataset containing real traffic. Results show that application fingerprinting reaches 90 percent accuracy, assisting in network customization. Finally, we discuss the main open issues and opportunities that network slicing technology provides for s-health.

Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems

January 10, 2023/in Publikasi /by Informatika

Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems

by Qiang Liu, Tao Han, Nirwan Ansari

Institute of Electrical and Electronics Engineers (IEEE)

There is a pressing need to interconnect physical systems such as power grid and vehicles for efficient management and safe operations. Due to the diverse features of physical systems, there is hardly a one-size-fits-all networking solution for developing cyber-physical systems. Network slicing is a promising technology that allows network operators to create multiple virtual networks on top of a shared network infrastructure. These virtual networks can be tailored to meet the requirements of different cyber-physical systems. However, it is challenging to design secure network slicing solutions that can efficiently create end-to-end network slices for diverse cyber-physical systems. In this article, we discuss the challenges and security issues of network slicing, study learning-assisted network slicing solutions, and analyze their performance under the denial-of-service attack. We also present a design and implementation of a small-scale testbed for evaluating the network slicing solutions.

Efficient Coastal Communications with Sparse Network Coding

January 7, 2023/in Publikasi /by Informatika

Efficient Coastal Communications with Sparse Network Coding

by Ye Li, Jue Wang, Shibing Zhang, Zhihua Bao, Jiangzhou Wang

Institute of Electrical and Electronics Engineers (IEEE)

The demand for wideband communication in the coastal area (i.e., ≤ 100 km from the coastline) has been rapidly increasing in recent years. Compared to the terrestrial scenario, the coastal environment has long-distance and highly dynamic channels, and the communication devices are more strictly constrained by energy supplies. While the RLNC has the fountain erasure-correction property and is suitable for transmissions over the long-distance dynamic channels, it suffers from high coding coefficient delivery cost and decoding complexity. In this article, we look into the application of sparse network coding in coastal communication systems. We identify two typical multicast scenarios that may appear in coastal communications, namely the relay-aided multicast and multicast from a shore-based base station with D2D communication enabled among the subscribers. We provide a detailed comparison of existing sparse network coding schemes. Based on that, we demonstrate through simulations that an appropriate choice of sparse codes is critical to meet the unique requirements in coastal communication systems. We show that batched sparse code is suitable for relay-aided multicast, and subset-based sparse codes are preferable for D2D-enabled multicast.

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