Network Intrusion Detection Systems (NIDS) are designed to safeguard the security needs of enterprise networks against cyber-attacks. However, NIDS networks suffer from several limitations, such as generating a high volume of low-quality alerts. Moreover, 99% of the alerts produced by NIDSs are false positives. As well, the prediction of future actions of an attacker is one of the most important goals here. The study has reviewed the state-of-the-art cyber-attack prediction based on NIDS Intrusion Alert, its models, and limitations. The taxonomy of intrusion alert correlation (AC) is introduced, which includes similarity-based, statistical-based, knowledge-based, and hybrid-based approaches. Moreover, the classification of alert correlation components was also introduced. Alert Correlation Datasets and future research directions are highlighted. The AC receives raw alerts to identify the association between different alerts, linking each alert to its related contextual information and predicting a forthcoming alert/attack. It provides a timely, concise, and high-level view of the network security situation. This review can serve as a benchmark for researchers and industries for Network Intrusion Detection Systems’ future progress and development.

/pdf/cyber-attack-prediction-based-on-network-intrusion-detection-1qqs4a31.pdf

Cyber-Attack Prediction Based on Network Intrusion Detection Systems for Alert Correlation Techniques: A Survey

During the last several years, the Internet of Things (IoT), fog computing, computer security, and cyber-attacks have all grown rapidly on a large scale. Examples of IoT include mobile devices such as tablets and smartphones. Attacks can take place that impact the confidentiality, integrity, and availability (CIA) of the information. One attack that occurs is Advanced Persistent Threat (APT). Attackers can manipulate a device’s behavior, applications, and services. Such manipulations lead to signification of a deviation from a known behavioral baseline for smartphones. In this study, the authors present a Systematic Literature Review (SLR) to provide a survey of the existing literature on APT defense mechanisms, find research gaps, and recommend future directions. The scope of this SLR covers a detailed analysis of most cybersecurity defense mechanisms and cutting-edge solutions. In this research, 112 papers published from 2011 until 2022 were analyzed. This review has explored different approaches used in cybersecurity and their effectiveness in defending against APT attacks. In a conclusion, we recommended a Situational Awareness (SA) model known as Observe–Orient–Decide–Act (OODA) to provide a comprehensive solution to monitor the device’s behavior for APT mitigation.

/pdf/exploration-of-mobile-device-behavior-for-mitigating-2syg5ial.pdf

Exploration of Mobile Device Behavior for Mitigating Advanced Persistent Threats (APT): A Systematic Literature Review and Conceptual Framework

The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming technologies and infrastructure, the agricultural sector has become an attractive target for cyberattacks. This paper presents a systematic literature review that assesses the applicability of existing cyber threat intelligence (CTI) techniques within smart farming infrastructures (SFIs). We develop a comprehensive taxonomy of CTI techniques and sources, specifically tailored to the SFI context, addressing the unique cyber threat challenges in this domain. A crucial finding of our review is the identified need for a virtual Chief Information Security Officer (vCISO) in smart agriculture. While the concept of a vCISO is not yet established in the agricultural sector, our study highlights its potential significance. The implementation of a vCISO could play a pivotal role in enhancing cybersecurity measures by offering strategic guidance, developing robust security protocols, and facilitating real-time threat analysis and response strategies. This approach is critical for safeguarding the food supply chain against the evolving landscape of cyber threats. Our research underscores the importance of integrating a vCISO framework into smart farming practices as a vital step towards strengthening cybersecurity. This is essential for protecting the agriculture sector in the era of digital transformation, ensuring the resilience and sustainability of the food supply chain against emerging cyber risks. 

Agriculture 4.0 and Beyond: Evaluating Cyber Threat Intelligence Sources and Techniques in Smart Farming Ecosystems

Cybersecurity has become a prominent issue in regard to ensuring information privacy and integrity in the internet age particularly with the rise of interconnected devices. However, advanced persistent threats (APTs) pose a significant danger to the current contemporary way of life, and effective APT detection and defense are vital. Game theory is one of the most sought-after approaches adopted against APTs, providing a framework for understanding and analyzing the strategic interactions between attackers and defenders. However, what are the most recent developments in game theory frameworks against APTs, and what approaches and contexts are applied in game theory frameworks to address APTs? In this systematic literature review, 48 articles published between 2017 and 2022 in various journals were extracted and analyzed according to PRISMA procedures and our formulated research questions. This review found that game-theory approaches have been optimized for the defensive performance of security measures and implemented to anticipate and prepare for countermeasures. Many have been designed as part of incentive-compatible and welfare-maximizing contracts and then applied to cyber–physical systems, social networks, and transportation systems, among others. The trends indicate that game theory provides the means to analyze and understand complex security scenarios based on technological advances, changes in the threat landscape, and the emergence of new trends in cyber-crime. In this study, new opportunities and challenges against APTs are outlined, such as the ways in which tactics and techniques to bypass defenses are likely to evolve in order to evade detection, and we focused on specific industries and sectors of high interest or value (e.g., healthcare, finance, critical infrastructure, and the government).

https://www.mdpi.com/2227-7390/11/6/1353/pdf?version=1678444285

Recent Developments in Game-Theory Approaches for the Detection and Defense against Advanced Persistent Threats (APTs): A Systematic Review

Influence of various chlorine additives on the partitioning of heavy metals during low-temperature two-stage fluidized bed incineration.

During the past decade, mobile attacks have been established as an indispensable attack vector adopted by Advanced Persistent Threat (APT) groups. The ubiquitous nature of the smartphone has allowed users to use mobile payments and store private or sensitive data (i.e., login credentials). Consequently, various APT groups have focused on exploiting these vulnerabilities. Past studies have proposed automated classification and detection methods, while few studies have covered the cyber attribution. Our study introduces an automated system that focuses on cyber attribution. Adopting MITRE’s ATT&CK for mobile, we performed our study using the tactic, technique, and procedures (TTPs). By comparing the indicator of compromise (IoC), we were able to help reduce the false flags during our experiment. Moreover, we examined 12 threat actors and 120 malware using the automated method for detecting cyber attribution.

Automatically Attributing Mobile Threat Actors by Vectorized ATT&CK Matrix and Paired Indicator.

Given the perniciousness of threats posed by state-sponsored advanced persistent threats (APTs), identifying cyber threat attribution of the cyber threat actors (CTA) is of paramount importance for deterring cyber-attacks by APTs. As state-sponsored APT groups have been especially active in the past decade, recent studies have attempted to establish attribution with the limited set of information of the APT groups. Various government agencies and SOC vendors have utilized Indicators of Compromise (IoC) and Tactic, Technique, Procedures (TTPs) to collect intelligence pertaining to the adversaries, to no avail. Recently, MITRE’s ATT&CK® framework has been widely adopted for collecting and documenting the TTPs of the various CTAs. This paper presents an Automated Reclassification for Threat Actors (ART) that quantitatively compares the TTPs from different APT groups. ART crawls cyber threat reports and retrieves the ATT&CK matrix of APT groups. Then, it vectorizes the ATT&CK matrix and calculates the cosine similarity. By reexamining the various aliases of the CTAs with the ATT&CK framework, we believe that ART can help classify the indiscriminately established APT groups.

ART: Automated Reclassification for Threat Actors based on ATT&CK Matrix Similarity

Operating and design parameters which give the significant effect on the performance of the two-stage swirl-flow fluidized bed combustor were discussed. The combustion experiments of anthracite coal with a wide size distribution were carried out to investigate the influence of the various parameters on the performance in the laboratory scale combustor. The parameters considered were swirler nozzle diameter, number of swirler nozzles, combustor diameter of the upper stage and swirler nozzle velocity. The trapping rate of fines in the upper stage increased with reducing the swirler nozzle diameter and with increasing the number of swirler nozzles and the combustor diameter of the upper stage. At constant superficial gas velocity for the upper bed, the trapping rate of fines in the upper stage showed a maximum at the swirler nozzle velocity between 18 m/s and 24 m/s. The combustion efficiency improved as the result of the increase in the trapping rate and of the decrease in the carryover rate.

Effect of parameters on performance of two-stage swirl-flow fluidized bed coal combustors

Alert correlation using diamond model for cyber threat intelligence

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Papers

Automatically Attributing Mobile Threat Actors by Vectorized ATT&CK Matrix and Paired Indicator.

ART: Automated Reclassification for Threat Actors based on ATT&CK Matrix Similarity

Effect of parameters on performance of two-stage swirl-flow fluidized bed coal combustors

Alert correlation using diamond model for cyber threat intelligence