Enhancing Cyber Security in Power Sector using Machine Learning

Nowadays, our lives have become very much dependent on the power systems, whether it is in home or in offices or anywhere. Any failure in the power systems can bring our lives to a halt. To ensure no power fault, a continuous and remote monitoring, control and automation are needed. The implementation of constraints increases the efficiency of the power systems. But, to put monitoring, control and automation into practice we need network, and with this come the threat of cyber-attacks. With more open standard-based communication network, the automated power systems have become the target of the cyber-attacks. By exploiting the cyber components in networks, critical cyber components can be manipulated. Intruders can tamper the communication links by injecting false or modified data. To come up with security measures against these attacks, vulnerabilities of the power systems are being assessed to analyze the impacts of the cyber-attacks. Several techniques have been implemented so far to make the power systems less prone to threats. In this paper, technology like Machine Learning is used as anomaly discriminator and to provide security to the power system against the cyber threats.

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Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

Safety in Extreme Environments ◽

10.1007/s42797-021-00025-1 ◽

2021 ◽

Author(s):

Petar Radanliev ◽

David De Roure ◽

Kevin Page ◽

Max Van Kleek ◽

Omar Santos ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Real Time ◽

Cyber Security ◽

Extreme Environments ◽

Learning Technologies ◽

Cyber Attacks ◽

Edge Computing ◽

Mathematical Formulas ◽

Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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UNIVERSITY CYBER SECURITY AS A METHOD FOR ANTI-FISHING FRAUD

ED/2019/1 - The economic discourse ◽

10.36742/2410-0919-2020-1-7 ◽

2020 ◽

pp. 59-67

Author(s):

Irina Tatomur

Keyword(s):

Cyber Security ◽

Educational Institution ◽

Visual Presentation ◽

Academic Community ◽

Cyber Attacks ◽

Asia Pacific ◽

Economic Losses ◽

Educational Institutions ◽

Security Measures ◽

Phishing Attacks

Introduction. With the rapid adoption of computer and networking technologies, educational institutions pay insufficient attention to the implementation of security measures to ensure the confidentiality, integrity and accessibility of data, and thus fall prey to cyber-attacks. Methods. The following methods were used in the process of writing the article: methods of generalization, analogy and logical analysis to determine and structure the motives for phishing attacks, ways to detect and prevent them; statistical analysis of data – to build a chronological sample of the world's largest cyber incidents and determine the economic losses suffered by educational institutions; graphical method – for visual presentation of results; abstraction and generalization – to make recommendations that would help reduce the number of cyber scams. Results. The article shows what role cyber security plays in counteracting phishing scams in the educational field. The motives for the implementation of phishing attacks, as well as methods for detecting and preventing them, have been identified and regulated. The following notions as "phishing", "submarine" and "whaling" are evaluated as the most dangerous types of fraud, targeting both small and large players in the information chain of any educational institution. An analytical review of the educational services market was conducted and a chronological sampling of the largest cyber incidents that occurred in the period 2010-2019 was made. The economic losses incurred by colleges, research institutions and leading universities in the world were described. It has been proven that the US and UK educational institutions have been the most attacked by attackers, somewhat inferior to Canada and countries in the Asia-Pacific region. It is found that education has become the top industry in terms of the number of Trojans detected on devices belonging to educational institutions and the second most listed among the most affected by the ransomware. A number of measures have been proposed to help reduce the number of cyber incidents. Discussion. The obtained results should be taken into account when formulating a strategy for the development of educational institutions, as well as raising the level of awareness of the representatives of the academic community in cybersecurity. Keywords: phishing, cyber security, cyber stalkers, insider threat, rootkit, backdoor.

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Application of Cyber Security in Emerging C4ISR Systems

Crisis Management ◽

10.4018/978-1-4666-4707-7.ch086 ◽

2013 ◽

pp. 1705-1738

Author(s):

Ashfaq Ahmad Malik ◽

Athar Mahboob ◽

Adil Khan ◽

Junaid Zubairi

Keyword(s):

Cyber Security ◽

Oil And Gas ◽

Information Assurance ◽

System Of Systems ◽

Cyber Attacks ◽

Security Measures ◽

Oil And Gas Exploration ◽

Civil Sector ◽

C4isr System ◽

Network Of Networks

C4ISR stands for Command, Control, Communications, Computers, Intelligence, Surveillance & Reconnaissance. C4ISR systems are primarily used by organizations in the defense sector. However, they are also increasingly being used by civil sector organizations such as railways, airports, oil, and gas exploration departments. The C4ISR system is a system of systems and it can also be termed as network of networks and works on similar principles as the Internet. Hence it is vulnerable to similar attacks called cyber attacks and warrants appropriate security measures to save it from these attacks or to recover if the attack succeeds. All of the measures put in place to achieve this are called cyber security of C4ISR systems. This chapter gives an overview of C4ISR systems focusing on the perspective of cyber security warranting information assurance.

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Applications of Machine Learning in Cyber Security

Handbook of Research on Machine and Deep Learning Applications for Cyber Security - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-9611-0.ch005 ◽

2020 ◽

pp. 83-103 ◽

Cited By ~ 3

Author(s):

Charu Virmani ◽

Tanu Choudhary ◽

Anuradha Pillai ◽

Manisha Rani

Keyword(s):

Machine Learning ◽

Cyber Security ◽

Machine Learning Algorithms ◽

Technological Systems ◽

The World ◽

Computer Scientists ◽

Potential Risks ◽

To Come ◽

Time Basis ◽

Applications Of Machine Learning

With the exponential rise in technological awareness in the recent decades, technology has taken over our lives for good, but with the application of computer-aided technological systems in various domains of our day-to-day lives, the potential risks and threats have also come to the fore, aiming at the various security features that include confidentiality, integrity, authentication, authorization, and so on. Computer scientists the world over have tried to come up, time and again, with solutions to these impending problems. With time, attackers have played out complicated attacks on systems that are hard to comprehend and even harder to mitigate. The very fact that a huge amount of data is processed each second in organizations gave birth to the concept of Big Data, thereby making the systems more adept and intelligent in dealing with unprecedented attacks on a real-time basis. This chapter presents a study about applications of machine learning algorithms in cyber security.

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Machine Learning Forensics

Advances in Digital Crime, Forensics, and Cyber Terrorism - Confluence of AI, Machine, and Deep Learning in Cyber Forensics ◽

10.4018/978-1-7998-4900-1.ch003 ◽

2021 ◽

pp. 47-66

Author(s):

Angad Gupta ◽

Ruchika Gupta ◽

A. Sankaran

Keyword(s):

Machine Learning ◽

Cyber Security ◽

Defense Responses ◽

Cyber Attacks ◽

Process Data ◽

Cyber Defense ◽

High Expectations ◽

Malicious Activity ◽

Malicious Behavior ◽

Normal Behavior

Machine learning (without human interference) can collect, analyze, and process data. In the case of cyber security, this technology helps to better analyze previous cyber-attacks and develop respective defense responses. This approach enables an automated cyber defense system with a minimum-skilled cyber security force. There are high expectations for machine learning (ML) in cyber security, and for good reasons. With the help of ML algorithms, we can sift through massive amounts of security events looking for anomalies, deviations from normal behavior that are often indicative of malicious activity. These findings are then presented to the analyst for review and vetting, and the results of his determination fed back into the system for training. As we process more and more data through the system, it evolves: it learns to recognize similar events and, eventually, the underlying traits of malicious behavior that we're trying to detect. This chapter explores machine learning forensics.

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Machine Learning–based Cyber Attacks Targeting on Controlled Information

ACM Computing Surveys ◽

10.1145/3465171 ◽

2021 ◽

Vol 54 (7) ◽

pp. 1-36

Author(s):

Yuantian Miao ◽

Chao Chen ◽

Lei Pan ◽

Qing-Long Han ◽

Jun Zhang ◽

...

Keyword(s):

Machine Learning ◽

Success Rate ◽

Cyber Security ◽

Information Leakage ◽

Cyber Attacks ◽

Security Threat ◽

Future Directions ◽

Related Information ◽

New Type ◽

Advanced Analytics

Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years. Due to the booming development and deployment of advanced analytics solutions, novel stealing attacks utilize machine learning (ML) algorithms to achieve high success rate and cause a lot of damage. Detecting and defending against such attacks is challenging and urgent so governments, organizations, and individuals should attach great importance to the ML-based stealing attacks. This survey presents the recent advances in this new type of attack and corresponding countermeasures. The ML-based stealing attack is reviewed in perspectives of three categories of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information. Recent publications are summarized to generalize an overarching attack methodology and to derive the limitations and future directions of ML-based stealing attacks. Furthermore, countermeasures are proposed towards developing effective protections from three aspects—detection, disruption, and isolation.

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A lightweight cryptographic solution to secure digital transmissions on resource-constrained environments

General Letters in Mathematics ◽

10.31559/glm2021.10.2.4 ◽

2021 ◽

Vol 10 (2) ◽

pp. 38-45

Author(s):

Saiida Lazaar

Keyword(s):

Cyber Security ◽

Hash Functions ◽

Cyber Attacks ◽

Constrained Systems ◽

Sensitive Data ◽

Constrained Environments ◽

Secure Networks ◽

Cyber Space ◽

To Come ◽

Main Components

The great revolution of technology and its fast growth have led to a cyber space increasingly vulnerable to cyber-attacks. For this reason, cyber security becomes paramount to protect our cyber space by presenting and implementing important solutions to protect sensitive data from malicious persons. Thereby various measures of protection have been developed and aim to minimize the risks and damages of attacks. Among them, cryptography plays a vital and crucial role in protecting sensitive transmissions and electronic exchanges through complex networks. Numerous scientific studies have emerged with the advent of the cloud and the Internet of Things (IoT); all of them have expressed a strong need for building secure, efficient and fast cryptosystems targeting confidentiality, integrity and authentication. The last two objectives are essentially built on hash functions which are the main components of many applications and secure networks. The purpose of this paper is to give recent advances of lightweight cryptographic solutions that meet the requirements of constrained systems, and to present a study, in terms of security, energy-consuming and efficiency, of the main hash functions standardized by NIST (National Institute of Standards and Technology). In the end, the paper will give a comparison between the studied hash functions aiming to come up with a recommendation of good lightweight hash functions suitable for implementation in an IoT framework.

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DATA SCIENCE, BIG DATA, AND PREDICTIVE ANALYTICS: A PLATFORM FOR CYBERSPACE SECURITY INTELLIGENCE

Jurnal Pertahanan & Bela Negara ◽

10.33172/jpbh.v7i2.192 ◽

2017 ◽

Vol 7 (2) ◽

Author(s):

Dicky R. M. Nainggolan

Keyword(s):

Machine Learning ◽

Big Data ◽

Cyber Security ◽

High Speed ◽

Data Science ◽

Predictive Analytics ◽

Cyber Attacks ◽

Artificial Intelligence Technology ◽

Science Methodology ◽

Cyberspace Security

Abstract – Data are the prominent elements in scientific researches and approaches. Data Science methodology is used to select and to prepare enormous numbers of data for further processing and analysing. Big Data technology collects vast amount of data from many sources in order to exploit the information and to visualise trend or to discover a certain phenomenon in the past, present, or in the future at high speed processing capability. Predictive analytics provides in-depth analytical insights and the emerging of machine learning brings the data analytics to a higher level by processing raw data with artificial intelligence technology. Predictive analytics and machine learning produce visual reports for decision makers and stake-holders. Regarding cyberspace security, big data promises the opportunities in order to prevent and to detect any advanced cyber-attacks by using internal and external security data. Keywords: Big Data, Cyber Security, Data Science, Intelligence, Predictive Analytics Abstrak – Data merupakan unsur terpenting dalam setiap penelitian dan pendekatan ilmiah. Metodologi sains data digunakan untuk memilah, memilih dan mempersiapkan sejumlah data untuk diproses dan dianalisis. Teknologi big data mampu mengumpulkan data dengan sangat banyak dari berbagai sumber dengan tujuan untuk mendapatkan informasi dengan visualisasi tren atau menyingkapkan pengetahuan dari suatu peristiwa yang terjadi baik dimasa lalu, sekarang, maupun akan datang dengan kecepatan pemrosesan data sangat tinggi. Analisis prediktif memberikan wawasan analisis lebih dalam dan kemunculan machine learning membawa analisis data ke tingkat yang lebih tinggi dengan bantuan teknologi kecerdasan buatan dalam tahap pemrosesan data mentah. Analisis prediktif dan machine learning menghasilkan laporan berbentuk visual untuk pengambil keputusan dan pemangku kepentingan. Berkenaan dengan keamanan siber, big data menjanjikan kesempatan dalam rangka untuk mencegah dan mendeteksi setiap serangan canggih siber dengan memanfaatkan data keamanan internal dan eksternal. Kata Kunci: Analisis Prediktif, Big Data, Intelijen, Keamanan Siber, Sains Data

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SAINS DATA, BIG DATA, DAN ANALISIS PREDIKTIF: SEBUAH LANDASAN UNTUK KECERDASAN KEAMANAN SIBER

Jurnal Pertahanan & Bela Negara ◽

10.33172/jpbh.v7i2.187 ◽

2017 ◽

Vol 7 (2) ◽

Author(s):

Dicky R. M. Nainggolan

Keyword(s):

Machine Learning ◽

Big Data ◽

Cyber Security ◽

High Speed ◽

Data Science ◽

Predictive Analytics ◽

Cyber Attacks ◽

Artificial Intelligence Technology ◽

Science Methodology ◽

Many Sources

Abstrak – Data merupakan unsur terpenting dalam setiap penelitian dan pendekatan ilmiah. Metodologi sains data digunakan untuk memilah, memilih dan mempersiapkan sejumlah data untuk diproses dan dianalisis. Teknologi big data mampu mengumpulkan data dengan sangat banyak dari berbagai sumber dengan tujuan untuk mendapatkan informasi dengan visualisasi tren atau menyingkapkan pengetahuan dari suatu peristiwa yang terjadi baik dimasa lalu, sekarang, maupun akan datang dengan kecepatan pemrosesan data sangat tinggi. Analisis prediktif memberikan wawasan analisis lebih dalam dan kemunculan machine learning membawa analisis data ke tingkat yang lebih tinggi dengan bantuan teknologi kecerdasan buatan dalam tahap pemrosesan data mentah. Analisis prediktif dan machine learning menghasilkan laporan berbentuk visual untuk pengambil keputusan dan pemangku kepentingan. Berkenaan dengan keamanan siber, big data menjanjikan kesempatan dalam rangka untuk mencegah dan mendeteksi setiap serangan canggih siber dengan memanfaatkan data keamanan internal dan eksternal. Kata Kunci: analisis prediktif, big data, intelijen, keamanan siber, sains dataAbstract – Data are the prominent elements in scientific researches and approaches. Data Science methodology is used to select and to prepare enormous numbers of data for further processing and analysing. Big Data technology collects vast amount of data from many sources in order to exploit the information and to visualise trend or to discover a certain phenomenon in the past, present, or in the future at high speed processing capability. Predictive analytics provides in-depth analytical insights and the emerging of machine learning brings the data analytics to a higher level by processing raw data with artificial intelligence technology. Predictive analytics and machine learning produce visual reports for decision makers and stake-holders. Regarding cyberspace security, big data promises the opportunities in order to prevent and to detect any advanced cyber-attacks by using internal and external security data. Keywords: big data, cyber security, data science, intelligence, predictive analytics

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Cyber Security Issues in Navigation Systems of Marine Vessels From a Control Perspective

Volume 7B: Ocean Engineering ◽

10.1115/omae2017-61771 ◽

2017 ◽

Cited By ~ 4

Author(s):

Vahid Hassani ◽

Naveena Crasta ◽

António M. Pascoal

Keyword(s):

Cyber Security ◽

Cyber Attacks ◽

Gps Data ◽

Positioning System ◽

Navigation Systems ◽

State Variables ◽

Security Measures ◽

Security Issues ◽

Linear Control Theory ◽

Marine Vessels

Autonomous marine vessels are the way forward to revolutionize maritime operations. However, the safety and success of autonomous missions depend critically on the availability of a reliable positioning system and time information generated using global positioning system (GPS) data. GPS data are further used for guidance, navigation, and control (GNC) of vehicles. At a mission planning level GPS data are commonly assumed to be reliable. From this perspective, this article aims to highlight the perils of maritime navigation attacks, showing the need for the enhancement of standards and security measures to intercept any serious threats to marine vessels emanating from cyber attacks and GPS spoofing. To this end, we consider a case where a cyber attacker blocks the real GPS signals and dupes the GPS antennas on board the marine vehicle with fake signals. Using the Nomoto model for the steering dynamics of a marine vessel and exploiting tools from linear control theory we show analytically, and verify using numerical simulations, that it is possible to influence the state variables of the marine vessel by manipulating the compromised GPS data.

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