Development Machine Learning Techniques to Enhance Cyber Security Algorithms. (Dept. E)

Abstract The volume of the data has been rocketed since the new information era arrives. How to protect information privacy and detect the threat whenever the intrusion happens has become a hot topic. In this essay, we are going to look into the latest machine learning techniques (including deep learning) which are applicable in intrusion detection, malware detection, and vulnerability detection. And the comparison between the traditional methods and novel methods will be demonstrated in detail. Specially, we would examine the whole experiment process of representative examples from recent research projects to give a better insight into how the models function and cooperate. In addition, some potential problems and improvements would be illustrated at the end of each section.

Download Full-text

Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security

Research Anthology on Artificial Intelligence Applications in Security ◽

10.4018/978-1-7998-7705-9.ch050 ◽

2021 ◽

pp. 1085-1099

Author(s):

S. Abijah Roseline ◽

S. Geetha

Keyword(s):

Machine Learning ◽

Cyber Security ◽

Machine Learning Algorithms ◽

Human Interaction ◽

Machine Learning Techniques ◽

Detection Methods ◽

Security Threat ◽

Signature Detection ◽

Learning Techniques ◽

Feature Based

Malware is the most serious security threat, which possibly targets billions of devices like personal computers, smartphones, etc. across the world. Malware classification and detection is a challenging task due to the targeted, zero-day, and stealthy nature of advanced and new malwares. The traditional signature detection methods like antivirus software were effective for detecting known malwares. At present, there are various solutions for detection of such unknown malwares employing feature-based machine learning algorithms. Machine learning techniques detect known malwares effectively but are not optimal and show a low accuracy rate for unknown malwares. This chapter explores a novel deep learning model called deep dilated residual network model for malware image classification. The proposed model showed a higher accuracy of 98.50% and 99.14% on Kaggle Malimg and BIG 2015 datasets, respectively. The new malwares can be handled in real-time with minimal human interaction using the proposed deep residual model.

Download Full-text

Cyber Security Using Machine Learning: Techniques and Business Applications

Applications of Artificial Intelligence in Business, Education and Healthcare - Studies in Computational Intelligence ◽

10.1007/978-3-030-72080-3_21 ◽

2021 ◽

pp. 385-406

Author(s):

Anish Gupta ◽

Ruchika Gupta ◽

Gagan Kukreja

Keyword(s):

Machine Learning ◽

Cyber Security ◽

Machine Learning Techniques ◽

Business Applications ◽

Learning Techniques

Download Full-text

Data Mining and Machine Learning Techniques for Cyber Security Intrusion Detection

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c5979.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 4084-4090

Keyword(s):

Machine Learning ◽

Data Mining ◽

Cyber Security ◽

Data Stream ◽

Machine Learning Techniques ◽

Complex Nature ◽

Use Of Data ◽

Learning Techniques ◽

Examination Paper ◽

Discovery Rates

An interference discovery framework is customizing that screens a singular or an arrangement of PCs for toxic activities that are away for taking or blue-penciling information or spoiling framework shows. The most methodology used as a piece of the present interference recognition framework is not prepared to deal with the dynamic and complex nature of computerized attacks on PC frameworks. In spite of the way that compelling adaptable methodologies like various frameworks of AI can realize higher discovery rates, cut down bogus alert rates and reasonable estimation and correspondence cost. The use of data mining can realize ceaseless model mining, request, gathering and littler than ordinary data stream. This examination paper portrays a connected with composing audit of AI and data delving procedures for advanced examination in the assistance of interference discovery. In perspective on the number of references or the congruity of a rising methodology, papers addressing each procedure were recognized, examined, and compacted. Since data is so fundamental in AI and data mining draws near, some striking advanced educational records used as a piece of AI and data burrowing are depicted for computerized security is shown, and a couple of recommendations on when to use a given system are given.

Download Full-text

A Systematic Review of Artificial Intelligence and Machine Learning Techniques for Cyber Security

Communications in Computer and Information Science - Big Data and Security ◽

10.1007/978-981-15-7530-3_44 ◽

2020 ◽

pp. 584-593

Author(s):

Rahman Ali ◽

Asmat Ali ◽

Farkhund Iqbal ◽

Asad Masood Khattak ◽

Saiqa Aleem

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Systematic Review ◽

Cyber Security ◽

Machine Learning Techniques ◽

Learning Techniques

Download Full-text

Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security

Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems - Advances in Digital Crime, Forensics, and Cyber Terrorism ◽

10.4018/978-1-5225-8241-0.ch011 ◽

2019 ◽

pp. 211-229

Author(s):

S. Abijah Roseline ◽

S. Geetha

Keyword(s):

Machine Learning ◽

Cyber Security ◽

Machine Learning Algorithms ◽

Human Interaction ◽

Machine Learning Techniques ◽

Detection Methods ◽

Security Threat ◽

Signature Detection ◽

Learning Techniques ◽

Feature Based

Malware is the most serious security threat, which possibly targets billions of devices like personal computers, smartphones, etc. across the world. Malware classification and detection is a challenging task due to the targeted, zero-day, and stealthy nature of advanced and new malwares. The traditional signature detection methods like antivirus software were effective for detecting known malwares. At present, there are various solutions for detection of such unknown malwares employing feature-based machine learning algorithms. Machine learning techniques detect known malwares effectively but are not optimal and show a low accuracy rate for unknown malwares. This chapter explores a novel deep learning model called deep dilated residual network model for malware image classification. The proposed model showed a higher accuracy of 98.50% and 99.14% on Kaggle Malimg and BIG 2015 datasets, respectively. The new malwares can be handled in real-time with minimal human interaction using the proposed deep residual model.

Download Full-text

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Sensors ◽

10.3390/s20226578 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6578

Author(s):

Ivan Vaccari ◽

Giovanni Chiola ◽

Maurizio Aiello ◽

Maurizio Mongelli ◽

Enrico Cambiaso

Keyword(s):

Machine Learning ◽

Cyber Security ◽

Detection System ◽

Critical Issue ◽

Cyber Attacks ◽

Machine Learning Techniques ◽

Detection Systems ◽

Learning Techniques ◽

Iot Devices ◽

Definition Of

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

Download Full-text

Survey on Applications of Deep Learning and Machine Learning Techniques for Cyber Security

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v14i16.16953 ◽

2020 ◽

Vol 14 (16) ◽

pp. 210

Author(s):

Mohammed I. Alghamdi

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Cyber Security ◽

Secondary Data ◽

Extensive Study ◽

Machine Learning Techniques ◽

Working Mechanism ◽

Learning Methods ◽

Learning Techniques ◽

Future Direction

The research aimed to conduct an extensive study of machine learning and deep learning methods in cybersecurity. To accomplish the objectives, the research carried out a qualitative study based on secondary data collection to review the available studies and literature. The research has examined three machine learning methods and three deep learning methods to study the most popular techniques used in cybersecurity. During the research, the working mechanism of each method was studied along with their strengths and weaknesses. Also, a comparative discussion has been made to examine the most effective method for cybersecurity. Limitations in the current literature were also identified, and future direction is also given to target and develop the weak areas of machine learning and deep learning methods.

Download Full-text

Cost Benefits of Using Machine Learning Features in NIDS for Cyber Security in UK Small Medium Enterprises (SME)

Future Internet ◽

10.3390/fi13080186 ◽

2021 ◽

Vol 13 (8) ◽

pp. 186

Author(s):

Nisha Rawindaran ◽

Ambikesh Jayal ◽

Edmond Prakash ◽

Chaminda Hewage

Keyword(s):

Machine Learning ◽

Home Environment ◽

Cyber Security ◽

Working Environment ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Set Up ◽

The Uk ◽

Medium Enterprises ◽

At Home

Cyber security has made an impact and has challenged Small and Medium Enterprises (SMEs) in their approaches towards how they protect and secure data. With an increase in more wired and wireless connections and devices on SME networks, unpredictable malicious activities and interruptions have risen. Finding the harmony between the advancement of technology and costs has always been a balancing act particularly in convincing the finance directors of these SMEs to invest in capital towards their IT infrastructure. This paper looks at various devices that currently are in the market to detect intrusions and look at how these devices handle prevention strategies for SMEs in their working environment both at home and in the office, in terms of their credibility in handling zero-day attacks against the costs of achieving so. The experiment was set up during the 2020 pandemic referred to as COVID-19 when the world experienced an unprecedented event of large scale. The operational working environment of SMEs reflected the context when the UK went into lockdown. Pre-pandemic would have seen this experiment take full control within an operational office environment; however, COVID-19 times has pushed us into a corner to evaluate every aspect of cybersecurity from the office and keeping the data safe within the home environment. The devices chosen for this experiment were OpenSource such as SNORT and pfSense to detect activities within the home environment, and Cisco, a commercial device, set up within an SME network. All three devices operated in a live environment within the SME network structure with employees being both at home and in the office. All three devices were observed from the rules they displayed, their costs and machine learning techniques integrated within them. The results revealed these aspects to be important in how they identified zero-day attacks. The findings showed that OpenSource devices whilst free to download, required a high level of expertise in personnel to implement and embed machine learning rules into the business solution even for staff working from home. However, when using Cisco, the price reflected the buy-in into this expertise and Cisco’s mainframe network, to give up-to-date information on cyber-attacks. The requirements of the UK General Data Protection Regulations Act (GDPR) were also acknowledged as part of the broader framework of the study. Machine learning techniques such as anomaly-based intrusions did show better detection through a commercially subscription-based model for support from Cisco compared to that of the OpenSource model which required internal expertise in machine learning. A cost model was used to compare the outcome of SMEs’ decision making, in getting the right framework in place in securing their data. In conclusion, finding a balance between IT expertise and costs of products that are able to help SMEs protect and secure their data will benefit the SMEs from using a more intelligent controlled environment with applied machine learning techniques, and not compromising on costs.

Download Full-text