AI-based anomaly detection for cyberattacks on Windows systems - Creation of a prototype for automated monitoring of the process environment

2020 ◽  
Vol 2020 (3) ◽  
pp. 331-1-331-13
Author(s):  
Benjamin Yüksel ◽  
Klaus Schwarz ◽  
Reiner Creutzburg

Cyber security has become an increasingly important topic in recent years. The increasing popularity of systems and devices such as computers, servers, smartphones, tablets and smart home devices is causing a rapidly increasing attack surface. In addition, there are a variety of security vulnerabilities in software and hardware that make the security situation more complex and unclear. Many of these systems and devices also process personal or secret data and control critical processes in the industry. The need for security is tremendously high. The owners and administrators of modern computer systems are often overwhelmed with the task of securing their systems as the systems become more complex and the attack methods increasingly intelligent. In these days a there are a lot of encryption and hiding techniques available. They are used to make the detection of malicious software with signature based scanning methods very difficult. Therefore, novel methods for the detection of such threats are necessary. This paper examines whether cyber threats can be detected using modern artificial intelligence methods. We develop, describe and test a prototype for windows systems based on neural networks. In particular, an anomaly detection based on autoencoders is used. As this approach has shown, it is possible to detect a wide range of threats using artificial intelligence. Based on the approach in this work, this research topic should be continued to be investigated. Especially cloud-based solutions based on this principle seem to be very promising to protect against modern threats in the world of cyber security.

2020 ◽  
Vol 1 (1) ◽  
pp. 35-42
Author(s):  
Péter Ekler ◽  
Dániel Pásztor

Összefoglalás. A mesterséges intelligencia az elmúlt években hatalmas fejlődésen ment keresztül, melynek köszönhetően ma már rengeteg különböző szakterületen megtalálható valamilyen formában, rengeteg kutatás szerves részévé vált. Ez leginkább az egyre inkább fejlődő tanulóalgoritmusoknak, illetve a Big Data környezetnek köszönhető, mely óriási mennyiségű tanítóadatot képes szolgáltatni. A cikk célja, hogy összefoglalja a technológia jelenlegi állapotát. Ismertetésre kerül a mesterséges intelligencia történelme, az alkalmazási területek egy nagyobb része, melyek központi eleme a mesterséges intelligencia. Ezek mellett rámutat a mesterséges intelligencia különböző biztonsági réseire, illetve a kiberbiztonság területén való felhasználhatóságra. A cikk a jelenlegi mesterséges intelligencia alkalmazások egy szeletét mutatja be, melyek jól illusztrálják a széles felhasználási területet. Summary. In the past years artificial intelligence has seen several improvements, which drove its usage to grow in various different areas and became the focus of many researches. This can be attributed to improvements made in the learning algorithms and Big Data techniques, which can provide tremendous amount of training. The goal of this paper is to summarize the current state of artificial intelligence. We present its history, introduce the terminology used, and show technological areas using artificial intelligence as a core part of their applications. The paper also introduces the security concerns related to artificial intelligence solutions but also highlights how the technology can be used to enhance security in different applications. Finally, we present future opportunities and possible improvements. The paper shows some general artificial intelligence applications that demonstrate the wide range usage of the technology. Many applications are built around artificial intelligence technologies and there are many services that a developer can use to achieve intelligent behavior. The foundation of different approaches is a well-designed learning algorithm, while the key to every learning algorithm is the quality of the data set that is used during the learning phase. There are applications that focus on image processing like face detection or other gesture detection to identify a person. Other solutions compare signatures while others are for object or plate number detection (for example the automatic parking system of an office building). Artificial intelligence and accurate data handling can be also used for anomaly detection in a real time system. For example, there are ongoing researches for anomaly detection at the ZalaZone autonomous car test field based on the collected sensor data. There are also more general applications like user profiling and automatic content recommendation by using behavior analysis techniques. However, the artificial intelligence technology also has security risks needed to be eliminated before applying an application publicly. One concern is the generation of fake contents. These must be detected with other algorithms that focus on small but noticeable differences. It is also essential to protect the data which is used by the learning algorithm and protect the logic flow of the solution. Network security can help to protect these applications. Artificial intelligence can also help strengthen the security of a solution as it is able to detect network anomalies and signs of a security issue. Therefore, the technology is widely used in IT security to prevent different type of attacks. As different BigData technologies, computational power, and storage capacity increase over time, there is space for improved artificial intelligence solution that can learn from large and real time data sets. The advancements in sensors can also help to give more precise data for different solutions. Finally, advanced natural language processing can help with communication between humans and computer based solutions.


Author(s):  
Wendy Flores-Fuentes ◽  
Moises Rivas-Lopez ◽  
Daniel Hernandez-Balbuena ◽  
Oleg Sergiyenko ◽  
Julio Cesar Rodriguez-Quiñonez ◽  
...  

This chapter presents the application of optoelectronic devices fusion as the base for those systems with non-linear behavior supported by artificial intelligence techniques, which require the use of information from various sensors for pattern recognition to produce an enhanced output. It also included a deep survey to define the state of the art in industrial applications following this tendency to identify and recognize the most used optoelectronic sensors, interconnectivity, raw data collection, data processing and interpretation, data fusion, intelligent decision algorithms, software and hardware instrumentation and control. Finally, it exemplifies how these technologies implemented in the industry can also be useful for other kinds of sector applications.


2018 ◽  
Vol 60 (1) ◽  
pp. 173-201
Author(s):  
Stefan A. Kaiser

With an increasing influence of computers and software, automation is affecting many areas of daily life. Autonomous systems have become a central notion, but many systems have reached only a lower level of automation and not yet full autonomy. Information technology and software have a strong impact and their industries are introducing their own business cultures. Even though autonomy will enable systems to act independently from direct human input and control in complex scenarios, the factors of responsibility, control, and attribution are of crucial importance for a legal framework. Legal responsibility has to serve as a safeguard of fundamental rights. Responsibility can be attributed by a special legal regime, and mandatory human override and fallback modes can assure human intervention and control. It is proposed to establish a precautionary regulatory regime for automated and autonomous systems to include general principles on responsibility, transparency, training, human override and fallback modes, design parameters for algorithms and artificial intelligence, and cyber security. States need to take a positivist approach, maintain their regulatory prerogative, and, in support of their exercise of legislative and executive functions, establish an expertise independent of industry in automation, autonomy, algorithms, and artificial intelligence.


2021 ◽  
Vol 1 ◽  
pp. 24-31
Author(s):  
S.I. Alpert ◽  
◽  
M.I. Alpert ◽  
P.Yu. Katin ◽  
N.O. Litvinova ◽  
...  

Due to modern microcomputers and platforms based on microprocessors such as, for example, Raspberry Pi, Orange Pi, Nano Pi, Rock Pi, Banana Pi, Asus Tinker Board – the development of prototypes of em-bedded systems is possible in a «design» mode. The software part is implemented on the basis of operat-ing systems and standard technologies based on well-known programming languages such as C / C++, Python, C#, Java, etc. In such case the control channel for the embedded system can be either imple-mented via a web service separated by a communication channel or controlled independently. It is im-portant to understand that creating an embedded system on a standard platform is much more expensive than buying a ready-made mass-produced device with the same functionality. Therefore, it makes sense to use platforms like the Raspberry Pi mainly for individual artificial devices. If it is necessary to build a project of embedded systems and there is a problem with choosing a hardware platform for the client side, then currently there is a wide range of boards and solutions for building an efficient and inexpen-sive system using ready-made modules. The number of expansion cards and various sensors, video cam-eras, internet connection via Ethernet, Wi-Fi and Bluetooth provides a wide range of opportunities for building almost any solution based on this component base. The foundation can be made within a small budget, with minimal time spent, using large blocks and ready-made libraries for programming embed-ded systems. This article presents the results of research and development work on the creation of a software and hardware infrastructure of a terrestrial platform with the elements of artificial intelligence. Based on the actual results of the research, a deployment diagram and a component diagram of such an infrastructure have been constructed.


2021 ◽  
Vol 9 (2) ◽  
pp. 1214-1219
Author(s):  
Sheha kothari, Et. al.

Artificial intelligence (AI) has made incredible progress, resulting in the most sophisticated software and standalone software. Meanwhile, the cyber domain has become a battleground for access, influence, security and control. This paper will discuss key AI technologies including machine learning in an effort to help understand their role in cyber security and the implications of this new technology. This paper discusses and highlights the different uses of machine learning in cyber security.


AI Magazine ◽  
2018 ◽  
Vol 39 (4) ◽  
pp. 45-56
Author(s):  
Bruno Bouchard ◽  
Kevin Bouchard ◽  
Noam Brown ◽  
Niyati Chhaya ◽  
Eitan Farchi ◽  
...  

The AAAI-18 workshop program included 15 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 2–7, 2018, at the Hilton New Orleans Riverside in New Orleans, Louisiana, USA. This report contains summaries of the Affective Content Analysis workshop; the Artificial Intelligence Applied to Assistive Technologies and Smart Environments; the AI and Marketing Science workshop; the Artificial Intelligence for Cyber Security workshop; the AI for Imperfect-Information Games; the Declarative Learning Based Programming workshop; the Engineering Dependable and Secure Machine Learning Systems workshop; the Health Intelligence workshop; the Knowledge Extraction from Games workshop; the Plan, Activity, and Intent Recognition workshop; the Planning and Inference workshop; the Preference Handling workshop; the Reasoning and Learning for Human-Machine Dialogues workshop; and the the AI Enhanced Internet of Things Data Processing for Intelligent Applications workshop.


2020 ◽  
pp. 43-50
Author(s):  
Yauheniya N. Saukova

It is shown that the issues of metrological traceability for extended self-luminous objects with a wide range of brightness have not yet been resolved, since the rank scales of embedded systems are used for processing digital images. For such scales, there is no “fixed” unit, which does not allow you to get reliable results and ensure the unity of measurements. An experiment is described to evaluate the accuracy of determining the intensity (coordinates) of the color of self-luminous objects. In terms of repeatability and intermediate precision compared to the reference measurement method, the color and chromaticity coordinates of self-luminous objects (reference samples) were determined by their multiple digital registration using technical vision systems. The possibilities of the developed methodology for colorimetric studies in hardware and software environments from the point of view of constructing a multidimensional conditional scale are determined.


AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 67-78
Author(s):  
Guy Barash ◽  
Mauricio Castillo-Effen ◽  
Niyati Chhaya ◽  
Peter Clark ◽  
Huáscar Espinoza ◽  
...  

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.


Author(s):  
Juveriya Afreen

Abstract-- With increase in complexity of data, security, it is difficult for the individuals to prevent the offence. Thus, by using any automation or software it’s not possible by only using huge fixed algorithms to overcome this. Thus, we need to look for something which is robust and feasible enough. Hence AI plays an epitome role to defense such violations. In this paper we basically look how human reasoning along with AI can be applied to uplift cyber security.


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