Visualisasi Trafik Jaringan Dengan Metode Support Vector Machine (SVM) (Studi Kasus: Universitas Indo Global Mandiri)

Limited network resources and the increasing number of internet users in the current digital era have an impact on high traffic which results in decreased access speed to internet services. This is also a problem that occurs at the Indo Global Mandiri University (UIGM) Palembang, causing access to academic services to be slow. The purpose of this research is to identify the types of network traffic patterns which are then carried out by the process of grouping and visualizing these types of traffic. The data in this study were taken in real-time at the UIGM campus. The data obtained is the result of responses which are then extracted. The extraction results are processed using the Support Vector Machine (SVM) method for the process of grouping and visualizing data. The results of this study can distinguish types of traffic based on communication protocols, namely tcp and udp, where the results of the experiment were carried out six times with the results being the first experiment where 99.7% TCP and 0.1% for UDP, the second experiment 97.6% for TCP and 1.1% for UDP , trial three 99.7 % TCP and 0.2% UDP, trial four 97.5% and 1.3% UDP, trial five 99.5 TCP and 02% UDP, and the sixth or final try 97.7% TCP and 1.1% UDP. The data from the use of the SVM method obtained several types of traffic such as games by 0.4%, mail 0.2%, multimedia 0.4% and the web by 82.8% and this research still produces data that the pattern is not yet recognized by 15.5% Keywords : Network Traffic, Classification, Support Vector Mesi

APPLICATION OF SOFT COMPUTING TECHNIQUES OVER HARD COMPUTING TECHNIQUES: A SURVEY

Soft computing is the fusion of different constituent elements. The main aim of this fusion to solve real-world problems, which are not solve by traditional approach that is hard computing. Actually, in our daily life maximum problem having uncertainty and vagueness information. So hard computing fail to solve this problems, because it give exact solution. To overcome this situation soft computing techniques plays a vital role, because it has capability to deal with uncertainty and vagueness and produce approximate result. This paper focuses on application of soft computing techniques over hard computing techniques

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population

Intrusion Detection: Embedded Software Machine Learning and Hardware Rules Based Co-Designs

Security of innovative technologies in future generation networks such as (Cyber Physical Systems (CPS) and Wi-Fi has become a critical universal issue for individuals, economy, enterprises, organizations and governments. The rate of cyber-attacks has increased dramatically, and the tactics used by the attackers are continuing to evolve and have become ingenious during the attacks. Intrusion Detection is one of the solutions against these attacks. One approach in designing an intrusion detection system (IDS) is software-based machine learning. Such approach can predict and detect threats before they result in major security incidents. Moreover, despite the considerable research in machine learning based designs, there is still a relatively small body of literature that is concerned with imbalanced class distributions from the intrusion detection system perspective. In addition, it is necessary to have an effective performance metric that can compare multiple multi-class as well as binary-class systems with respect to class distribution. Furthermore, the expectant detection techniques must have the ability to identify real attacks from random defects, ingrained defects in the design, misconfigurations of the system devices, system faults, human errors, and software implementation errors. Moreover, a lightweight IDS that is small, real-time, flexible and reconfigurable enough to be used as permanent elements of the system's security infrastructure is essential. The main goal of the current study is to design an effective and accurate intrusion detection framework with minimum features that are more discriminative and representative. Three publicly available datasets representing variant networking environments are adopted which also reflect realistic imbalanced class distributions as well as updated attack patterns. The presented intrusion detection framework is composed of three main modules: feature selection and dimensionality reduction, handling imbalanced class distributions, and classification. The feature selection mechanism utilizes searching algorithms and correlation based subset evaluation techniques, whereas the feature dimensionality reduction part utilizes principal component analysis and auto-encoder as an instance of deep learning. Various classifiers, including eight single-learning classifiers, four ensemble classifiers, one stacked classifier, and five imbalanced class handling approaches are evaluated to identify the most efficient and accurate one(s) for the proposed intrusion detection framework. A hardware-based approach to detect malicious behaviors of sensors and actuators embedded in medical devices, in which the safety of the patient is critical and of utmost importance, is additionally proposed. The idea is based on a methodology that transforms a device's behavior rules into a state machine to build a Behavior Specification Rules Monitoring (BSRM) tool for four medical devices. Simulation and synthesis results demonstrate that the BSRM tool can effectively identify the expected normal behavior of the device and detect any deviation from its normal behavior. The performance of the BSRM approach has also been compared with a machine learning based approach for the same problem. The FPGA module of the BSRM can be embedded in medical devices as an IDS and can be further integrated with the machine learning based approach. The reconfigurable nature of the FPGA chip adds an extra advantage to the designed model in which the behavior rules can be easily updated and tailored according to the requirements of the device, patient, treatment algorithm, and/or pervasive healthcare application

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.This work has received funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE (IT1294-19), EMAITEK and ELK ARTEK programs. D. Camacho also acknowledges support from the Spanish Ministry of Science and Education under PID2020-117263GB-100 grant (FightDIS), the Comunidad Autonoma de Madrid under S2018/TCS-4566 grant (CYNAMON), and the CHIST ERA 2017 BDSI PACMEL Project (PCI2019-103623, Spain)

Umělá inteligence v kybernetické bezpečnosti

Artifcial intelligence (AI) and machine learning (ML) have grown rapidly in recent years, and their applications in practice can be seen in many felds, ranging from facial recognition to image analysis. Recent developments in Artificial intelligence have a vast transformative potential for both cybersecurity defenders and cybercriminals. Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space. In contrast, cybercriminals are aware of the new prospects too and likely to adapt AI techniques to their operations. This thesis presents advances made so far in the field of applying AI techniques in cybersecurity for combating against cyber threats, to demonstrate how this promising technology can be a useful tool for detection and prevention of cyberattacks. Furthermore, the research examines how transnational criminal organizations and cybercriminals may leverage developing AI technology to conduct more sophisticated criminal activities. Next, the research outlines the possible dynamic new kind of malware, called X-Ware and X-sWarm, which simulates the swarm system behaviour and integrates the neural network to operate more efficiently as a background for the forthcoming anti-malware solution. This research proposes how to record and visualize the behaviour of these type of malware when it propagates through the file system, computer network (virus process is known) or by observed data analysis (virus process is not known and we observe only the data from the system). Finally, a paradigm of an anti-malware solution, named Multi agent antivirus system has been proposed in the thesis that gives the insight to develop a more robust, adaptive and flexible defence system.Význam umělé inteligence (AI) a strojového učení (ML) v posledních letech rychle rostl a na jejich aplikacích lze vidět, že v mnoha oblastech, od rozpoznávání obličeje až po analýzu obrazu, byl učiněn velký pokrok. Poslední vývoj v oblasti umělé inteligence má obrovský potenciál jak pro obránce v oblasti kybernetické bezpečnosti, tak pro ůtočníky. AI se stává řešením v otázce obrany proti modernímu malware a hraje tak důležitou roli v detekci a prevenci hrozeb v digitálním prostoru. Naproti tomu kyberzločinci jsou si vědomi nových vyhlídek ve spojení s AI a pravděpodobně přizpůsobí tyto techniky novým generacím malware, vektorům útoku a celkově jejich operacím. Tato práce představuje dosavadní pokroky aplikace technik AI v oblasti kybernetické bezpečnosti. V této oblasti tzn. v boji proti kybernetickým hrozbám se ukázuje jako slibná technologie a užitečný nástroj pro detekci a prevenci kybernetických útoků. V práci si rovněž pokládme otázku, jak mohou nadnárodní zločinecké organizace a počítačoví zločinci využít vyvíjející se technologii umělé inteligence k provádění sofistikovanějších trestných činností. Konečně, výzkum nastíní možný nový druh malware, nazvaný X-Ware, který simuluje chování hejnového systému a integruje neuronovou síť tak, aby fungovala efektivněji a tak se celý X-Ware a X-sWarm dal použít nejen jako kybernetická zbraň na útok, ale i jako antivirové obranné řešení. Tento výzkum navrhuje, jak zaznamenat a vizualizovat chování X-Ware, když se šíří prostřednictvím systému souborů, sítí a to jak analýzou jeho dynamiky (proces je znám), tak analýzou dat (proces není znám, pozorujeme jen data). Nakonec bylo v disertační práci navrženo paradigma řešení proti malwaru, jež bylo nazváno „Multi agent antivirus system“. Tato práce tedy poskytuje pohled na vývoj robustnějšího, adaptivnějšího a flexibilnějšího obranného systému.460 - Katedra informatikyvyhově

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

IoT in smart communities, technologies and applications.

Internet of Things is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The Internet of Things (IoT) for Smart Cities has many different domains and draws upon various underlying systems for its operation, in this work, we provide a holistic coverage of the Internet of Things in Smart Cities by discussing the fundamental components that make up the IoT Smart City landscape, the technologies that enable these domains to exist, the most prevalent practices and techniques which are used in these domains as well as the challenges that deployment of IoT systems for smart cities encounter and which need to be addressed for ubiquitous use of smart city applications. It also presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things. Towards this end, a mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. Within the smart health domain of IoT smart cities, human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. Fall detection is one of the most important tasks in human activity recognition. With an increasingly aging world population and an inclination by the elderly to live alone, the need to incorporate dependable fall detection schemes in smart devices such as phones, watches has gained momentum. Therefore, differentiating between falls and activities of daily living (ADLs) has been the focus of researchers in recent years with very good results. However, one aspect within fall detection that has not been investigated much is direction and severity aware fall detection. Since a fall detection system aims to detect falls in people and notify medical personnel, it could be of added value to health professionals tending to a patient suffering from a fall to know the nature of the accident. In this regard, as a case study for smart health, four different experiments have been conducted for the task of fall detection with direction and severity consideration on two publicly available datasets. These four experiments not only tackle the problem on an increasingly complicated level (the first one considers a fall only scenario and the other two a combined activity of daily living and fall scenario) but also present methodologies which outperform the state of the art techniques as discussed. Lastly, future recommendations have also been provided for researchers