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.

/pdf/a-survey-on-machine-learning-techniques-for-cyber-security-i849l0zme0.pdf

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

In recent years, the number of vulnerabilities discovered and publicly disclosed has shown a sharp upward trend. However, the value of exploitation of vulnerabilities varies for attackers, considering that only a small fraction of vulnerabilities are exploited. Therefore, the realization of quick exclusion of the non-exploitable vulnerabilities and optimal patch prioritization on limited resources has become imperative for organizations. Recent works using machine learning techniques predict exploited vulnerabilities by extracting features from open-source intelligence (OSINT). However, in the face of explosive growth of vulnerability information, there is room for improvement in the application of past methods to multiple threat intelligence. A more general method is needed to deal with various threat intelligence sources. Moreover, in previous methods, traditional text processing methods were used to deal with vulnerability related descriptions, which only grasped the static statistical characteristics but ignored the context and the meaning of the words of the text. To address these challenges, we propose an exploit prediction model, which is based on a combination of fastText and LightGBM algorithm and called fastEmbed. We replicate key portions of the state-of-the-art work of exploit prediction and use them as benchmark models. Our model outperforms the baseline model whether in terms of the generalization ability or the prediction ability without temporal intermixing with an average overall improvement of 6.283% by learning the embedding of vulnerability-related text on extremely imbalanced data sets. Besides, in terms of predicting the exploits in the wild, our model also outperforms the baseline model with an F1 measure of 0.586 on the minority class (33.577% improvement over the work using features from darkweb/deepweb). The results demonstrate that the model can improve the ability to describe the exploitability of vulnerabilities and predict exploits in the wild effectively.

FastEmbed: Predicting vulnerability exploitation possibility based on ensemble machine learning algorithm.

Huge amounts of data are circulating in the digital world in the era of the Industry 5.0 revolution. Machine learning is experiencing success in several sectors such as intelligent control, decision making, speech recognition, natural language processing, computer graphics, and computer vision, despite the requirement to analyze and interpret data. Due to their amazing performance, Deep Learning and Machine Learning Techniques have recently become extensively recognized and implemented by a variety of real-time engineering applications. Knowledge of machine learning is essential for designing automated and intelligent applications that can handle data in fields such as health, cyber-security, and intelligent transportation systems. There are a range of strategies in the field of machine learning, including reinforcement learning, semi-supervised, unsupervised, and supervised algorithms. This study provides a complete study of managing real-time engineering applications using machine learning, which will improve an application's capabilities and intelligence. This work adds to the understanding of the applicability of various machine learning approaches in real-world applications such as cyber security, healthcare, and intelligent transportation systems. This study highlights the research objectives and obstacles that Machine Learning approaches encounter while managing real-world applications. This study will act as a reference point for both industry professionals and academics, and from a technical standpoint, it will serve as a benchmark for decision-makers on a range of application domains and real-world scenarios.

/pdf/a-review-on-machine-learning-strategies-for-real-world-24eciezf.pdf

A Review on Machine Learning Strategies for Real-World Engineering Applications

Nowadays, the designing of cyber-physical systems has a significant role and plays a substantial part in developing a sustainable computing ecosystem for secure and scalable network architecture. The introduction of Cyber Threat Intelligence (CTI) has emerged as a new security system to mitigate existing cyber terrorism for advanced applications. CTI demands a lot of requirements at every step. In particular, data collection is a critical source of information for analysis and sharing; it is highly dependent on the reliability of the data. Although many feeds provide information on threats recently, it is essential to collect reliable data, as the data may be of unknown origin and provide information on unverified threats. Additionally, effective resource management needs to be put in place due to the large volume and diversity of the data. In this paper, we propose a blockchain-based cyber threat intelligence system architecture for sustainable computing in order to address issues such as reliability, privacy, scalability, and sustainability. The proposed system model can cooperate with multiple feeds that collect CTI data, create a reliable dataset, reduce network load, and measure organizations’ contributions to motivate participation. To assess the proposed model’s effectiveness, we perform the experimental analysis, taking into account various measures, including reliability, privacy, scalability, and sustainability. Experimental results of evaluation using the IP of 10 open source intelligence (OSINT) CTI feeds show that the proposed model saves about 15% of storage space compared to total network resources in a limited test environment.

https://www.mdpi.com/2071-1050/12/16/6401/pdf

Blockchain-Based Cyber Threat Intelligence System Architecture for Sustainable Computing

The awareness about software vulnerabilities is crucial to ensure effective cybersecurity practices, the development of high-quality software, and, ultimately, national security. This awareness can be better understood by studying the spread, structure and evolution of software vulnerability discussions across online communities. This work is the first to evaluate and contrast how discussions about software vulnerabilities spread on three social platforms-Twitter, GitHub, and Reddit. Moreover, we measure how user-level e.g., bot or not, and content-level characteristics e.g., vulnerability severity, post subjectivity, targeted operating systems as well as social network topology influence the rate of vulnerability discussion spread. To lay the groundwork, we present a novel fundamental framework for measuring information spread in multiple social platforms that identifies spread mechanisms and observables, units of information, and groups of measurements. We then contrast topologies for three social networks and analyze the effect of the network structure on the way discussions about vulnerabilities spread. We measure the scale and speed of the discussion spread to understand how far and how wide they go, how many users participate, and the duration of their spread. To demonstrate the awareness of more impactful vulnerabilities, a subset of our analysis focuses on vulnerabilities targeted during recent major cyber-attacks and those exploited by advanced persistent threat groups. One of our major findings is that most discussions start on GitHub not only before Twitter and Reddit, but even before a vulnerability is officially published. The severity of a vulnerability contributes to how much it spreads, especially on Twitter. Highly severe vulnerabilities have significantly deeper, broader and more viral discussion threads. When analyzing vulnerabilities in software products we found that different flavors of Linux received the highest discussion volume. We also observe that Twitter discussions started by humans have larger size, breadth, depth, adoption rate, lifetime, and structural virality compared to those started by bots. On Reddit, discussion threads of positive posts are larger, wider, and deeper than negative or neutral posts. We also found that all three networks have high modularity that encourages spread. However, the spread on GitHub is different from other networks, because GitHub is more dense, has stronger community structure and assortativity that enhances information diffusion. We anticipate the results of our analysis to not only increase the understanding of software vulnerability awareness but also inform the existing and new analytical frameworks for simulating information spread e.g., disinformation across multiple social environments online.

/pdf/multiple-social-platforms-reveal-actionable-signals-for-whqamvfs4e.pdf

Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit

Various embodiments for predicting which software vulnerabilities will be exploited by malicious hackers and hence prioritized by patching are disclosed.

Systems and methods for prioritizing software vulnerabilities for patching

We present an approach that combines social network analysis with machine learning techniques to predict future cyber threats through darkweb/deepweb discussions with hacking-related content. Our approach harnesses features derived from hacker social networks and from online sources of cybersecurity advisories. We address the problem of predicting the exploitability of software vulnerabilities to show that features computed from hacker social networks are important indicators of future cybersecurity incidents. We conduct a suite of experiments on real-world hacker and exploit data and demonstrate that social network data improves recall by about 19%, F1 score by about 6% while maintaining precision. We believe this is because social network structures related to certain exploit authors is indicative of their ability to write exploits that are subsequently employed in an attack.

Predicting Cyber Threats through Hacker Social Networks in Darkweb and Deepweb Forums

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Systems and methods for prioritizing software vulnerabilities for patching

Predicting Cyber Threats through Hacker Social Networks in Darkweb and Deepweb Forums