Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

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A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

The paper discusses a new framework combining the possibilities of Big Data processing and machine leaning developed for security monitoring of mobile Internet of Things. The mathematical foundations and the problem statement are considered. The description of the used data set and the architecture of proposed security monitoring framework are provided. The framework specifies several machine learning mechanisms intended for solving classification tasks. The classifier operation results are exposed to plurality voting, weighted voting, and soft voting. The framework performance and accuracy is assessed experimentally.

Framework for Mobile Internet of Things Security Monitoring Based on Big Data Processing and Machine Learning

Security and Blockchain Convergence with Internet of Multimedia Things: Current Trends, Research Challenges and Future Directions.

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.

Applications of Machine Learning in Cyber Security

Organizing contacts, friends, sharing thoughts, emotions searching content and data are the basic means that a social networking site provides. By sharing, and managing content the users form a social network for example, Facebook, Linkedin, twitter, Orkut, etc. A user may exist on several different social networking sites and the problem to maintain the account on these networks prevails. This brings us to define a Social network aggregation i.e. collecting the social content from different social networks and integrate it at a single location/site. It is an attempt to organize a user's social networking experience as whole. This paper gives a review on different Social network Aggregators and issues in integrating social network. Exposing criminal behaviours in e-commerce, computer intrusions identification, detecting health problems, analysing satellite images.

Study and analysis of Social network Aggregator

A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, to analyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discovery of like mindedness amongst users. This article’s goal exploits the technique of Ensemble K-means clusters to extract the entities and their corresponding interests as per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.

/pdf/clustering-in-aggregated-user-profiles-across-multiple-4j6f2d90l1.pdf

Clustering in Aggregated User Profiles Across Multiple Social Networks

Mobile agent is a software program that migrates from one node to another while performing given tasks on behalf of a user. Mobile agent technology is used to develop many distributed applications. A mobile agent can communicate independently with other agents, with users, and with the hosts in the network and preserves all of its state when it moves from one network node to another. However, the mobility of agents makes it more difficult to trace mobile agents and transfer messages reliably. Therefore, a reliable communication protocol that provides efficient location management and reliable message delivery is the key issue to the design of mobile agent system. Several communication protocols exists in a mobile agent communication environment, however an analytical study indicated that some problems remain unsolved and none have proved to be the best. This paper aims to present a comparison of various protocols with the intention to propose a hybrid version in future.

https://www.longdom.org/open-access/a-comparison-of-communication-protocols-for-mobile-agents-0976-4860-3-114-122.pdf

Charu Virmani

Papers

Applications of Machine Learning in Cyber Security

Study and analysis of Social network Aggregator

Clustering in Aggregated User Profiles Across Multiple Social Networks

A Comparison of Communication Protocols for Mobile Agents

Design of Query Processing System to Retrieve Information from Social Network using NLP