scholarly journals A Threat-Hunting of UNSW-NB15 with Machine Learning Techniques to Achieve Resilience

2019 ◽  
Vol 9 (1S3) ◽  
pp. 488-494
Keyword(s):  
Machine Learning ◽  
Fault Tolerance ◽  
Critical Role ◽  
Cyber Attacks ◽  
Three Dimensions ◽  
Machine Learning Techniques ◽  
Advanced Persistent Threat ◽  
Learning Techniques

In order to focus on the mission and functions of the business of the organizations, cyber resiliency have to play a critical role against the adversaries’ target. The strategy recommended by NIST to reduce the suspect ability of cyber-attacks of the system with the three dimensions such as harden the target, limit the damage to the target and make the target resilient. The threats could be based on cyber and noncyber. The objective is to provide cyber resiliency on the Advanced Persistent Threat (APT), has born with the nature of sophisticated, stealthy, persistent towards target and highly adoptable to the environment. The challenge is to provide cyber resilience to the system from compromising tactics of the adversaries, uncertain in eradication of threat due to its persistent nature, recognizing its adapting ability. The cyber resiliency also links with other disciplines like safety, fault tolerance, privacy, resilience and survivability, reliability and security

A Study of Machine Learning Techniques in Cryptography for Cybersecurity

2021 ◽  
Vol 1 (4) ◽  
pp. 22-26
Author(s):  
Ankita Saha ◽  
Chanda Pathak ◽  
Sourav Saha
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Network Architecture ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Cyber Attack ◽  
Sensitive Data ◽  
Security Controls ◽  
Learning Techniques ◽  
Scope Of Application

The importance of cybersecurity is on the rise as we have become more technologically dependent on the internet than ever before. Cybersecurity implies the process of protecting and recovering computer systems, networks, devices, and programs from any cyber attack. Cyber attacks are an increasingly sophisticated and evolving danger to our sensitive data, as attackers employ new methods to circumvent traditional security controls. Cryptanalysis is mainly used to crack cryptographic security systems and gain access to the contents of the encrypted messages, even if the key is unknown. It focuses on deciphering the encrypted data as it works with ciphertext, ciphers, and cryptosystems to understand how they work and find techniques for weakening them. For classical cryptanalysis, the recovery of ciphertext is difficult as the time complexity is exponential. The traditional cryptanalysis requires a significant amount of time, known plaintexts, and memory. Machine learning may reduce the computational complexity in cryptanalysis. Machine learning techniques have recently been applied in cryptanalysis, steganography, and other data-securityrelated applications. Deep learning is an advanced field of machine learning which mainly uses deep neural network architecture. Nowadays, deep learning techniques are usually explored extensively to solve many challenging problems of artificial intelligence. But not much work has been done on deep learning-based cryptanalysis. This paper attempts to summarize various machine learning based approaches for cryptanalysis along with discussions on the scope of application of deep learning techniques in cryptography.

scholarly journals A Comprehensive Review and meta-analysis on Applications of Machine Learning Techniques in Intrusion Detection

2018 ◽  
Vol 22 ◽  
Author(s):  
Manojit Chattopadhyay ◽  
Rinku Sen ◽  
Sumeet Gupta
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Meta Analysis ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Business Organizations ◽  
Learning Techniques ◽  
Applications Of Machine Learning ◽  
Network Technologies

Securing a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for changing usage pattern by comparing different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies.

scholarly journals MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Sensors ◽  
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.

scholarly journals DMAPT: Study of Data Mining and Machine Learning Techniques in Advanced Persistent Threat Attribution and Detection

2021 ◽  
Author(s):  
P.V. Sai Charan ◽  
P. Mohan Anand ◽  
Sandeep K. Shukla
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Gap Analysis ◽  
Big Bang ◽  
Machine Learning Techniques ◽  
Corporate Networks ◽  
Advanced Persistent Threat ◽  
Learning Techniques ◽  
Long Time ◽  
Indian Power Sector

Modern-day malware is intelligent enough to hide its presence and perform stealthy operations in the background. Advance Persistent Threat (APT) is one such kind of malware attack on sensitive corporate and banking networks to stay there for a long time undetected. In real-time corporate networks, identifying the presence of intruders is a big challenging task for security experts. Recent APT attacks like Carbanak, The Big Bang, and Red Echo attack (targeting the Indian power sector) are ringing alarms globally. New data exfiltration methods and advancements in malware techniques are the two main reasons for rapid and robust APT evolution. Although many traditional and hybrid methods are available to detect this stealthy malware, the number of target-specific attacks are increasing rapidly at global level. Attackers have been crafting payloads resistant to malware sandbox environments so that traditional sandboxing techniques may not work with these APT malware detection. In this paper, we shed light on various Data Mining, Machine Learning techniques and frameworks used in both Attribution and Detection of APT malware. Added to this, our work highlight GAP analysis and need for paradigm shift in existing techniques to deal with evolving modern APT malware.

scholarly journals Cybersecurity Data Science: An Overview from Machine Learning Perspective

2020 ◽  
Author(s):  
Iqbal H. Sarker
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Cyber Attacks ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Research Issues ◽  
Security Incident ◽  
Intelligent Decision Making ◽  
Learning Techniques ◽  
Intelligent Decision

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning-based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

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