Intelligence Community Research Articles

Kecerdasan Religius Umat Islam di Desa Betteng Deata, Kec. Gandangbatu Sillanan, Kab. Tana Toraja, Sulawesi Selatan

Religious intelligence is an intelligence that can be used to face and solve problems of meaning and value. It can be interpreted that this intelligence can place a person's behavior and life in a broader meaning. The diversity of Muslims is characterized by the ability of the Muslim minority community to carry out social activities with non-Muslim communities, such as the inauguration of traditional houses and other social activities in Betteng Deata village. The aim of this research is to determine religious intelligence, forms of religious intelligence and factors that influence religious intelligence in Islamic communities in Betteng Deata Village, Kec. Gandangbatu Sillanan, Kab. Tana Toraja, South Sulawesi. In this research, a qualitative descriptive research method was used by selecting the problem under study, namely the religious intelligence of Muslims in Betteng Deata Village, Kec. Gandangbatu Sillanan District. Tana Toraja, South Sulawesi. The results of this research found religious intelligence in the form of the ability of Muslims to provide a good example to the general public, such as in selecting village leaders, the ability to respect differences in beliefs, such as during the celebration of religious holidays, the ability to resolve problems, such as differences of opinion between groups, the ability to create religious harmony, such as in social activities. Apart from that, several forms of religious intelligence among Muslims were found, such as eradicating illiteracy in the Koran (Dirosa), opening Kindergartens/TPAs, as well as monthly recitations from house to house. As well as various factors that influence religious intelligence, namely environmental factors and congenital factors.

Effective Community Participation Mechanism in Security Control in Mount Elgon Constituency, Bungoma County

This study sought to analyze community participation and security in Mt. Elgon constituency, Bungoma County. The thesis of this study is that community participation seems to be more prominent as an alternative strategy in combating insecurity in Mt Elgon. The objective of the study was to find out how effective community participation mechanisms is in security control in Mt Elgon constituency. This study was informed by complexity theory and Stakeholder theory to offer insights on social relations and behavior relevant for peace building. These theories were purposely selected since it explains and enhances a better understanding of the variables affecting the research questions. Methodologically, the study adopted both the qualitative and quantitative methods, drawn from the primary and secondary sources. This study primarily targeted community policing committees, national government administrative officers, religious leaders and the Kenya police. By use of Yamane formula, 127 respondents were selected to take part in the study. A non-probability sampling technique that adopts a purposive sampling method was used in selecting respondents. Besides, descriptive statistical methods were used in processing and analyzing data. Primary data was obtained using questionnaires and interview guides. A pilot study was carried out to test the validity and reliability of data. The study found out that neighborhood watch, community intelligence gathering, community meetings, community hotlines and community policing are some of the most effective community participation mechanisms effective in security control in Mt Elgon constituency, Bungoma County. Further, the results indicated that indigenous and restorative practices significantly contribute to security control in Mt Elgon region. Nevertheless, there are specific areas like re-integration and compensation that needed improvements. Besides, the relationship between the police and community in Mt Elgon region was poor, thus the need to create trust and hold community meetings to improve the relationship. This study recommends that the NPS should Having units specifically trained on dealing with the community participation. The NPS should also educate and create awareness to the local communities and other stakeholders on the importance of contributing to national security.

Enhancing Industrial IoT Security: Utilizing Blockchain-Assisted Deep Federated Learning for Collaborative Intrusion Detection

The Industrial Internet of Things (IIoT) is a rapidly evolving features with multiple applications, including critical infrastructure. Privacy policies are required to preserve the protection of user data in the threat intelligence community. Blockchain is a modern technology which used recently to provide more secure storage and efficiency. In this research, Blockchain Assisted Deep Federated Learning (BC_DFL) system is used to detect intruders. The three key processes used in the proposed intrusion detection architecture are data collection, pre-processing and intrusion detection. Data normalization, reduction, cleaning and transformation are used in pre-processing to remove extraneous information and improve data quality. This pre-processed data is sent to the Blockchain Assisted Deep Federated Learning (BC_DFL) system for intrusion detection. To detect intruders, the federated learning-based Capsule Auto-Encoder (FL_CAE) architecture first learns the properties from the inputs. Blockchain technology (BCTech) is not only used for storage but also improves security by eliminating the possibility of threatening node and individual server failure. The ToN_IoT and UNSW-NB15 data sets are used in the implementation and performance evaluation study. The proposed model is evaluated using existing in the Results section. In the UNSW-NB15 dataset, proposed model achieved an accuracy, precision, recall and F1 score of 97.26%, 97.28%, 96.89% and 96.96% respectively as well as in ToN_IoT data set proposed model achieved an accuracy, precision, recall and F1 score of 95.74%, 99.54 %, 99.49% and 99.24%, respectively. The execution of the proposed approach takes 2.31 seconds in the UNSW-NB15 dataset and 1.66 seconds in the ToN_IoT dataset. Blockchain offers a transparent and impenetrable ledger for transaction recording and verification. Within the framework of cooperative intrusion detection, it guarantees a secure and reliable exchange of threat intelligence and detection models between various users of IIoT ecosystem.

The Islamic Republic of Iran’s Use of Diplomats in Its Intelligence and Terrorist Operations against Dissidents: The Case of Assadollah Assadi

The Islamic Republic of Iran (IRI) has two main intelligence organizations: the Ministry of Intelligence (MOI) and the Intelligence Organization of the Islamic Revolutionary Guard Corps. The two organizations are highly active on foreign soil with malign activities like assassinations. In 2018, the third secretary of the IRI embassy in Austria, Assadollah Assadi, was arrested and later convicted of involvement in terrorism. With the help of three of his assets, they had planned to bomb a gathering in Paris arranged by the Iranian opposition to the IRI. Assadi would later be identified as the principal intelligence officer of the MOI on the European continent. Analyzing the case of Assadi shows that the IRI has the will and resources to conduct significant terrorist operations and that the regime has a vast network of assets throughout Europe. Most significant in the failed Paris plot was that evidently the IRI Intelligence Community is likely compromised and that the regime has considerable problems in trusting its own intelligence officers.

Israeli Intelligence Was Caught Off Guard: The Hamas Attack on 7 October 2023—A Preliminary Analysis

On the morning of 7 October 2023, at 6:29 AM, Israel was surprised when forces of the Hamas organization attacked Israel from the Gaza Strip, along the border between Gaza and Israel, and caused enormous damage to human life and property. On that day, about 1,300 Israelis were killed, most of them civilians and the rest soldiers. About 240 Israelis were kidnapped and taken into Gaza as hostages. Israel had no concrete warning of the coming attack. It was a complete surprise to the Intelligence Community (IC) and security system. Following this attack, Israel went to war against Hamas, in Gaza, calling it the Iron Swords War, with the intent of collapsing its military power and governing rule. The lack of intelligence warning raises many questions about the capability of the Israeli IC, especially the Israel Military Intelligence Directorate (IMI) and the Israel Security Agency (ISA). Israel’s intelligence had known of the strengthening of Hamas, but there was an assumption that Hamas had changed its objectives and aimed to establish its rule and statehood in Gaza. As a result, it was assumed that Hamas would refrain from offensive action that would, in turn, provoke counterattacks by Israel, which would not serve its interests. The Israeli IC assumed that if Hamas decided to attack, the ISA and the IMI would provide early warning. Only on 7 October did Israel learn that Hamas was secretly intensifying its efforts to prepare an attack on Israel.

Mixed Fair Division: A Survey

The fair allocation of resources to agents is a fundamental problem in society and has received significant attention and rapid developments from the game theory and artificial intelligence communities in recent years. The majority of the fair division literature can be divided along at least two orthogonal directions: goods versus chores, and divisible versus indivisible resources. In this survey, besides describing the state of the art, we outline a number of interesting open questions in three mixed fair division settings: (i) indivisible goods and chores, (ii) divisible and indivisible goods (i.e., mixed goods), and (iii) fair division of indivisible goods with subsidy.

Intelligence Agencies’ move to the Cloud: Challenges and Opportunities

The purpose of this research is to discover more about the challenges and opportunities faced by intelligence agencies wishing to move their data to the cloud. Intelligence agencies collect and process enormous amounts of data and information and need the tools to do so. Two intelligence communities have moved to the cloud to face these issues but there is little scientific knowledge about moving an intelligence agency’s data to the cloud. No research on the topic could be found and this study aims to fill part of that gap by using a case study research strategy and interviews with experts in the field. A literature review was completed to understand previously identified challenges when adopting cloud and was used to create two sets of interview questions. Five interviews were conducted, and a thematic analysis done resulting in fourteen themes. The themes revealed that there are many challenges with laws and regulations being the biggest one, while the opportunities brought by a cloud solution are the processing and analysis of data, and information sharing.

Leveraging artificial intelligence in firm-generated online customer communities: a framework and future research agenda

PurposeIn an era where complex technological advances increasingly govern service delivery, it is incumbent on service firms to pioneer innovative strategies to sustain customer engagement and cultivate loyalty. This conceptual paper examines the transformative potential of artificial intelligence (AI) in the realm of online customer communities, with a particular focus on its creation, management and enhancement facets. The authors explore how AI can revolutionize the dynamics of customer interaction, feedback mechanisms and overall engagement within the service industry.Design/methodology/approachThis conceptual paper draws from marketing and management literature focusing on customer communities and AI in service and customer engagement contexts with a robust future research agenda.FindingsA classification of online customer community engagement is provided along with a conceptual framework to guide our understanding of the integration of AI into online customer communities.Originality/valueThis exploration underscores the imperative for service firms to embrace AI-driven approaches to online customer community management, not only as a means to optimize their operations but as a vital strategy to stay competitive in the ever-evolving digital landscape. This paper examines the novel combination of AI with online customer communities and provides the framework in the form of an input-process-output (IPO) model for future research into this integration.

Use case cards: a use case reporting framework inspired by the European AI Act

Despite recent efforts by the Artificial Intelligence (AI) community to move towards standardised procedures for documenting models, methods, systems or datasets, there is currently no methodology focused on use cases aligned with the risk-based approach of the European AI Act (AI Act). In this paper, we propose a new framework for the documentation of use cases that we call use case cards, based on the use case modelling included in the Unified Markup Language (UML) standard. Unlike other documentation methodologies, we focus on the intended purpose and operational use of an AI system. It consists of two main parts: firstly, a UML-based template, tailored to allow implicitly assessing the risk level of the AI system and defining relevant requirements, and secondly, a supporting UML diagram designed to provide information about the system-user interactions and relationships. The proposed framework is the result of a co-design process involving a relevant team of EU policy experts and scientists. We have validated our proposal with 11 experts with different backgrounds and a reasonable knowledge of the AI Act as a prerequisite. We provide the 5 use case cards used in the co-design and validation process. Use case cards allows framing and contextualising use cases in an effective way, and we hope this methodology can be a useful tool for policy makers and providers for documenting use cases, assessing the risk level, adapting the different requirements and building a catalogue of existing usages of AI.

Extraction of object-action and object-state associations from Knowledge Graphs

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is a critical and long-held aim for the Artificial Intelligence community. Training systems with relevant data is a typical approach; however, finding the data required is not always possible, especially when much of this knowledge is commonsense. In this paper, we present a comparison of topology-based and semantics-based methods for extracting information about object-action and object-state association relations from knowledge graphs, such as ConceptNet, WordNet, ATOMIC, YAGO, WebChild and DBpedia. Moreover, we propose a novel method for extracting information about object-action and object-state associations from knowledge graphs. Our method is composed of a set of techniques for locating, enriching, evaluating, cleaning and exposing knowledge from such resources, relying on semantic similarity methods. Some important aspects of our method are the flexibility in deciding how to deal with the noise that exists in the data, and the capability to determine the importance of a path through training, rather than through manual annotation.

Swedish intelligence, Russia and the war in Ukraine: anticipations, course, and future implications

ABSTRACT As the sole plausible military threat facing Sweden, the Soviet Union and then Russia have consistently been at the top of Swedish intelligence collection priorities. In fact, understanding Russia and its military forces well is a source of pride and self-perceived comparative advantage of the Swedish intelligence community. This article contextualizes Swedish intelligence collection on Russia, including its Cold War antecedents and the persistent counterintelligence threat. It then describes Swedish intelligence reporting in anticipation of the war in Ukraine, including a reported misjudgement that Russia would ultimately not conduct a full-scale war of aggression against Ukraine in its entirety. This ‘intelligence failure’ was reportedly based on the assessment that Russia had assembled insufficient military capabilities to subjugate Ukraine, and that such an operation would be too risky and costly. Coming full circle in again beginning to focus mainly on great power competition and military capabilities in its near abroad, Swedish military intelligence has since reported valuable intelligence on Russian military capabilities, drawing on a combination of collection capabilities.

AFP Intelligence Community’s Strategic Risk Management Process in Response to External Aggression

In the ever-changing global security landscape, characterized by volatility, uncertainty, complexity, and ambiguity (VUCA), this study serves as a capstone project to provide valuable insights to the AFP Intelligence Community (AFPIC). Its primary focus is to effectively tackle the exceptional non-military security threats a foreign adversary poses. China’s rapid and significant ascent as an Asian hegemon and a global superpower has driven its pursuit of resources and influence worldwide. These hegemonic actions have not only elicited resentment from smaller and weaker nations adversely affected by them but also drawn the ire of the United States and its allies. The Philippines, strategically located in the South China Sea and the Pacific region, has become a prime target of China’s unconventional warfare strategy, where they seek to subdue their adversaries without resorting to kinetic force. China’s United Front Works (UFW) strategy is a crucial element of its hybrid warfare approach, providing a platform for intelligence, influence, and interference operations designed to systematically undermine its enemies. This study aims to describe the current state of the AFPIC’s risk management capabilities in terms of doctrines, organization, and training concerning the security threats posed by China’s UFW. It also seeks to identify the challenges in these areas and propose appropriate solutions to address them. Overall, the objective of this study is to contribute to the long-term endeavor of enhancing the AFPIC’s doctrines, organization, and training to effectively address the threats posed by China’s UFW and other hybrid security threats. The qualitative data gathered through document analysis, key informant interviews, and focused group discussions involving multiple participants from various units and offices within the AFPIC and the AFP Triad, as well as education and doctrine development circles, underwent an iterative and hybrid process of thematic and content analysis. Tools such as SWOT, TOWS, and the causal-impact analytical tool were utilized during this analytical process. The results of this analysis provided valuable insights into the current state of AFPIC’s risk management capability in terms of doctrine, organization, and training, highlighting both the advantages and disadvantages within these areas. Further exploration revealed the primary challenges present in these three domains, which encompassed internal and external dynamics that hindered the optimal implementation of related initiatives. Subsequently, numerous recommendations were formulated to enhance the AFPIC’s doctrine, organization, and training capabilities in response to the exceptional security threats posed by China’s united front works. Central to the research findings were three crucial factors that influenced the success or failure of the doctrinal, organizational, and training endeavors: the support of advocates and champions, the role of knowledge management, and the significance of engaging foreign stakeholders. Keywords: Armed Forces of the Philippines, Intelligence, Risk Management, United Front Works, National Security, hybrid security threats, unconventional warfare, political warfare

Understanding Putin’s Russia: a continuing challenge for Western intelligence

ABSTRACT For Western intelligence communities, charged with providing advance notice of adverse developments around the world, Russia under Putin has had to become top priority for study, as was the Soviet Union during the Cold War. Using secret intelligence to help assess the motives and intent of the Kremlin leadership is essential but remains as hard, not least because of the extreme violence of the regime towards anyone suspected of betraying confidences to assist the West. Putin has provided his own explanation for his invasion of Ukraine in 2022 that illuminates his world view and belief in Russia as a Slavic nation, of which Ukraine is seen as part. Putin’s background in the KGB is regime is seen as providing continuity with the brutal repressions of the Stalin era. The different methods used by Western intelligence to assess Russia are reviewed highlighting the additional digital sources available today. The warning failures of Western intelligence to provide strategic notice of Putin’s aggressive actions are explained by inadequate situational awareness, erroneous explanation of his motives, leading to poor estimation of his subsequent moves, the exception being US and UK prediction of the 2022 invasion itself.

Valuing Diversity in the U.S. Intelligence Community

The U.S. Intelligence Community has sought to diversify its workforce for decades. Both proponents and opponents of diversity, equity, and inclusion (DEI) initiatives have advanced a functionalist, “business case” approach to the assessment of DEI initiatives that undermines DEI’s moral and ethical dimensions as well as its benefits under the right conditions. This article moves beyond the business case to present resources for intelligence stakeholders in cultivating strategic, systemic, and sustainable approaches to diversity management.

Mitigating the Energy Consumption and Carbon Emissions of a Residential Area in a Tropical City Using Digital Twin Technology: A Case Study of Bertam, Penang

As of 2022, roughly 79.8% of Malaysia’s population resides in urban areas, increasing the population density of its cities. The hot and humid climate in Malaysia necessitates the constant use of air conditioning, especially in cities, resulting in high residential electric consumption and carbon emissions. The residential sector significantly contributes to global climate change, accounting for 27% of global energy consumption and 17% of carbon emissions. To address this concern, the local framework of the National Low Carbon Cities Masterplan (NLCCM) is advocating for a minimum 33% reduction in carbon emissions by 2030 in urban settings, aligning with the commitments made at the 15th Conference of Parties (COP 15). The aim of this study was to determine the energy consumption and carbon emission of residential areas in a tropical city, as well as explore potential energy and carbon savings. Utilizing the Intelligent Communities Lifecycle–Intelligent Community Design (iCL-iCD) energy simulation software, a digital twin of Bertam City, Penang, was developed. This digital model included 65.4% residential housing, which, in the specific scenario under study, accounted for 36% of the city’s electrical energy consumption for cooling purposes only. An early simulation of the residential areas of the city estimated the baseline energy consumption and carbon emissions to be 607 GWh and 314,736 tCO2e, respectively. Several energy-efficient measures were applied to the residential area of Bertam City, revealing a potential saving of 37.3% in both energy and carbon emissions.

The Consequences of a Politicized Intelligence Community

The Consequences of a Politicized Intelligence Community

A novel multi-hybrid differential evolution algorithm for optimization of frame structures

Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm. Firstly, an iterative division for improved exploration and exploitation is used, then an adaptive proportional population size reduction mechanism is followed for reducing the computational complexity. It also incorporated Weibull distribution and Gaussian random sampling to mitigate premature convergence. The proposed framework is validated by using IEEE CEC benchmark suites (CEC 2005, CEC 2014 and CEC 2017). The algorithm is applied to four engineering design problems and for the weight minimization of three frame design problems. Experimental results are analysed and compared with recent hybrid algorithms such as laplacian biogeography based optimization, adaptive differential evolution with archive (JADE), success history based DE, self adaptive DE, LSHADE, MVMO, fractional-order calculus-based flower pollination algorithm, sine cosine crow search algorithm and others. Statistically, the Friedman and Wilcoxon rank sum tests prove that the proposed algorithm fares better than others.

Critical review of the analysis of competing hypotheses technique: lessons for the intelligence community

ABSTRACT Intelligence communities regularly produce important assessments that inform policymakers. The Analysis of Competing Hypotheses technique (ACH) is one of the most widely-touted methods for improving the accuracy of those assessments. But does ACH work? This critical review identified seven articles describing six experiments testing ACH. The results indicate ACH – as a whole – has little to no overall benefit on judgment quality, and may even harm it, even though some aspects of ACH might be beneficial. We consequently discourage intelligence organizations from mandating the training or use of ACH, and we recommend greater integration of science into intelligence practices in general.

Intelligence Theory and Democratic Governance: An Epistemological Approach from Political Science

The contemporary concept of national and state security is currently undergoing a significant change with regard to its scope and structure. In the light of the risks that threaten present-day societies and their democratic political systems, the viability of the old paradigm of intelligence is being called into question. The increase in the Intelligence Community calls on a plurality of actors from different epistemological fields inside social sciences to contribute to the strengthening of a new paradigm of intelligence suitable to promote and strengthen peacebuilding and democratic governance capabilities. Exploring the changes that underlie proposals for a new approach to intelligence, this article formulates a proactive and preventive approach from the conceptual, methodological, professional, and academic perspective of political science.

A CONTINGENCY APPROACH TO PUBLIC SECTOR PERFORMANCE MANAGEMENT: THE CASE OF THE CANADIAN INTELLIGENCE COMMUNITY

On November 13, 2023, Dr. Joe Faragone presented A Contingency Approach to Public Sector Performance Management: The Case of the Canadian Intelligence Community for this year’s West Coast Security Conference. The presentation was followed by a question-and-answer period with questions from the audience and CASIS Vancouver executives. The key points discussed were the limitations of universalistic performance management approaches, the critical need to reassess NPM reforms in Public Administration, and the necessity to explicitly define the distinctiveness of the Intelligence Community.
 
 Received: 12-11-2023
 Revised: 01-26-2024