Security Experts Research Articles

Pakistan in the Crosshairs and the Rising Stakes of Strategic Information Warfare

Over the past two decades, information and communication technologies (ICTs) have reaped significant attention from security experts, computer scientists, intelligence agencies, and governments for their potential applications in conflict scenarios. Using ICTs for offensive or defensive purposes to instantly invade, disrupt, or control the opponent's resources is known as strategic information warfare (SIW). Although SIW is as ancient as military history, the communication sciences revolution has altered its character. It has evolved into a double-edged weapon that is equally crucial for strong nations, technically underdeveloped states, non-state entities, and individual software experts. Some nations, most notably the US, Russia, and China, have taken the SIW technology seriously and compared it to the danger posed by WMDs. In the south Asian context, Pakistan faces a direct threat from India, which collaborates closely with Israel, by the use of SIW.
 Additionally, the Western media portrays a highly negative picture of Pakistan's administration, law and order, and economy. The national media, suitably supported by foreign players, has begun a propaganda warfare effort against Pakistan's military forces and intelligence institutions. However, SIW is now a global danger, necessitating a worldwide response. Pakistan must create a proactive national strategy to prepare for the problems erupted from SIW strike on the communication network, which is crucial for the operation of its nuclear facilities.

Development of an Intelligence Framework for Spotting Suicide Bombers Using SmartPLS.

Suicide bombing is an act of terrorizing lives and properties in a community. Despite the advancement in military Munitions to combat terrorist activities, there exists a global rise in terrorism almost daily. Terrorism is an act committed by an unidentified group or individual with the intent to damage or jeopardize a community’s safety. Development of an intelligent model for spotting suicide bombers in a crowd is expedient. Document analysis and interview protocol were used to gather facts from the security experts in South Western Nigeria to get the variables that they possibly used to identify terrorists. Their responses were analyzed thematically to form constructs and sub-constructs that were used for the questionnaires for the study. The responses from the questionnaires were used to form the latent variables for the study, which was analyzed using SmartPLS 4. The PLS-SEM algorithm stopped when it reached the stop criterion of 1.0E-7 (i.e., 0.0000001). The reliability of the model was measured using Cronbach’s Alpha and Composite Reliability (CR) when items have factor loading of (>=0.7). The convergent validity was measured using The Average Variance Extracted (AVE) at (0.500). The result shows that the only variables that can be used to form an intelligent framework for suicide bombers are training and location.

Zero-Trust Security Model Applied to Smart Shipping: Towards a Feasible Architecture

Securing information systems and assets within smart shipping environments is of utmost importance. In practice, however, securing smart shipping is a difficult and tedious task because smart shipping environments are highly dynamic, distributed, and loosely coupled, which cause having large threat/attack vectors without having all security measures under own control. The Zero Trust Security Model (ZTSM) has been suggested by security experts and many national cybersecurity centers as a promising approach for addressing the shortcomings of the traditional perimeter-based security architecture. However, a scalable ZTSM architecture which is applicable to large networks, like those of smart shipping, is missing. In this contribution we aim at investigating how the ZTSM can be made suitable for securing smart shipping. We study smart shipping security requirements and describe three off-the-shelf security services that can contribute to the realization of the ZTSM in such environments. Investigating these example security services inspired us to propose a ZTSM architecture for smart shipping environments, which relies on metadata exchange for trust establishment at various levels among federations of organizations, human or business controlled context and content transfer, and monitoring and controlling data and service usage. The proposed architecture can embody the ZTSM deployment within large networks of cooperating organizations (like those within smart environments). Although this architecture is proposed for and based on the security requirements of smart shipping, we conjecture that it can be applicable to other forms of smart environments as well.

Forensic features of methods of committing theft using methods of social engineering and information and telecommunication technologies

Many online media have recently been focusing on the topic of social engineering fraud. Business leaders, computer security experts, and lawyers, in particular, law enforcement officers, however, do not fully understand the kinds of frauds that can be conducted through or with the aid of the Internet and mobile communication, or the ramifications of such frauds for the future of e-commerce. The article will present a forensic characteristic of the system of actions for the preparation, commission and concealment of fraud using social engineering methods and information and telecommunications technologies. This paper has three principal goals. First, it will identify the principal types of social engineering frauds that law enforcement and regulatory authorities are observing. Second, it will explain the major psychological influence techniques that criminals use in conducting such frauds. Third, it will identify the patterns of preparation, commission and concealment of fraud of this type. The paper will begin by presenting a typology of the criminal and the forensic characteristics of his illegal activity. Also, will explore the principal psychological features of social engineering fraud. In conclusion, the features of the methods of committing fraud of the analyzed category of crimes will be summarized and its characteristic features will be revealed.

Untangling the Link Between Digital Transformation and Information Security Management

Digital transformation is considered a source of competitive advantage and improved organisational performance. Within the public sector, leaders anticipate digital transformation to help them improve transparency and accountability, and stakeholders’ participation in public decision-making. At the same time, the accelerated adoption of digital technologies within the sector has also made information an important asset. Thus, the effectiveness of the information security management put in place determines whether an organisation can realise the benefits of successful digital transformation. Adopting a mixed-method approach (13 interviews and an online survey with 128 information security experts), this study identified various antecedents of effective information security management, i.e., leadership skills and attitudes, organisational culture, organisational structure, IT governance, IT alignment, and human resource management practices. Partial Least Structural Equation Modelling technique was adopted to test the proposed theoretical model and hypothesised causal relationships.

Enhancing intrusion detection performance using explainable ensemble deep learning.

Given the exponential growth of available data in large networks, the need for an accurate and explainable intrusion detection system has become of high necessity to effectively discover attacks in such networks. To deal with this challenge, we propose a two-phase Explainable Ensemble deep learning-based method (EED) for intrusion detection. In the first phase, a new ensemble intrusion detection model using three one-dimensional long short-term memory networks (LSTM) is designed for an accurate attack identification. The outputs of three classifiers are aggregated using a meta-learner algorithm resulting in refined and improved results. In the second phase, interpretability and explainability of EED outputs are enhanced by leveraging the capabilities of SHape Additive exPplanations (SHAP). Factors contributing to the identification and classification of attacks are highlighted which allows security experts to understand and interpret the attack behavior and then implement effective response strategies to improve the network security. Experiments conducted on real datasets have shown the effectiveness of EED compared to conventional intrusion detection methods in terms of both accuracy and explainability. The EED method exhibits high accuracy in accurately identifying and classifying attacks while providing transparency and interpretability.

Toward Reducing IDS Misclassification Using Hybrid DL and ML Approach

Operation centers often face challenges due to the high rate of misclassifications caused by the lower precision in Intrusion Detection System (IDS) models. Despite several research contributions ranging from machine learning and deep learning techniques aiming to reduce false positives and negatives, researchers and security experts consistently encounter a tradeoff between these two types of errors. This indicates a significant opportunity for further contributions in this field. We propose a hybrid model that combines Recurrent Neural Networks (RNN) feature extraction capabilities with Support Vector Machines (SVM) classification abilities. Our model achieves an impressive accuracy rate of 98.2% and significantly reduces misclassification errors compared to contemporary state-of-the-art models. This work shows the potential of hybrid approaches in improving accuracy and reducing false positive and negative errors.

Fuzzy decision-making model for solving the ISAO “risk triangle” in conditions of violation of air-craft separation norms.

Taking into account the prevailing influence of the human factor on the occurrence of the vast majority of aviation events and serious incidents, the leading role of such a component of the current ISAO flight safety concept as “the attitude of aviation personnel towards dangerous actions or conditions” has been determined, which well explains the mutual influence of other components of this paradigm. In turn, the components of this relationship are determined through indicators of the influence of the human factor on decision-making by frontline aviation operators, in particular air traffic controllers. This approach is logical, since, firstly, the professional activity of an air traffic controller is easy to imagine as a continu-ous chain of decisions; secondly, according to statistics, unwanted aviation events are the result of false decisions; thirdly, the vast majority of aviation security experts believe that the point is not that “front line” aviation operators do not make decisions at all, but that these decisions are untimely, incor-rect, ineffective, etc. In the context of this publication, such a component of decision making is considered as fuzzy risk as-sessment models, in particular violations of standard operating procedures, namely aircraft separation standards. Using the modifier “very” and the methodology of fuzzy mathematics, a scale (term set) of the linguistic variable “danger level” was formed. The dimension of the scale is larger than that recommended by ISAO, which allows for a more detailed analysis of the dangers of violations of separation standards. On the other hand, scale reduction does not cause difficulties in the conditions of using fuzzy operations of “concentration”, “stretching”, “merging”. Having adapted the well-known “Cooper-Harper scale” for research needs, a survey was conducted of professional air traffic controllers m = 70, employees of the Unified Air Traffic Control System of the Republic of Azerbaijan, who expressed their attitude to the dangers of violations of the separation norms of ACC, ARR aircraft S = 20 km in the form of a “point on the parameter scale”, which contributed to the use of the so-called “hint matrix” for constructing membership functions of the linguistic variable “hazard level” with smooth falling fronts. It has been established that all points of intersection of the membership functions of neighboring terms of the linguistic variable “hazard level” are greater than the “transition point” of L. Zadeh, therefore the distances between them, established on the continuum of the studied separation norm, “rather belong” to the corresponding linguistic assessment of the established scale. The reduction of this scale to the ISAO dimension contributed to the solution of the corresponding “risk triangle”.

Religious Insurgency and The Quest for Sustainable Peace in Nigeria: The Islamic Model as a Panacea

Peace advocates, conflict analysts, security experts and academia have effortlessly analyzed and advanced various ways to contain the religious insurgency championed by the Boko Ḥarām movement which has threatened Nigeria's quest for peace for more than a decade despite the counter-insurgency measures of successive Nigerian governments. However, recent situations depict that the insurgency remains unabated. This paper examined Nigeria's turbulent decade of religious insurgency, its position within the Islamic context, and Islamically-inclined practicable solution to the threat. It used analytical research method based on existing secondary literature and primary Islamic law sources. The paper reveals that a turbulent decade of Boko Ḥarām insurgency has wreaked havoc on the peace and tranquility previously existing among Nigeria's diverse ethno-religious entities. The analysis of Quranic texts, aḥādīth and opinions of notable scholars also indicates that religious insurgency associated with Boko Ḥarām is inconsistent with Islamic doctrines. Tolerance among religionists, proper religious teachings and interpretation of divine scripture, genuine inter and intra-faith dialogues, Islamic leadership-cum-governance paradigms, among others, were the identified Islamic-modelled panacea to the insurgency. The paper concludes that if the identified Islamic model can be embraced and considered in the scheme of things by religious and political leaders, as well as their ardent followers, the current religious insurgency would be drastically curtailed, thereby advancing sustainable peace in Nigeria.

A Static Approach for Malware Analysis: A Guide to Analysis Tools and Techniques

Abstract: Malicious code presents a severe risk to computer systems, making work difficult for information security and cyber experts. Malware analysis is in great demand because of its importance and function in digital forensics and cyber security. Malware, often known as malicious software, is purposefully written software that harms or damages people, computers, servers, or networks. An overview of malware analysis methods and techniques in the fields of digital forensics and cyber security is given in this article. The study examines several malware types, their characteristics, and analysts' challenges in locating and analyzing them. It also highlights the importance of continuing this field's research and development to stay ahead of evolving malware threats. Trojan horses, worms, backdoors, rootkits, and adware are examples of malware. There are several methods for analyzing malware, but one of the most well-known is static analysis. This article will look at several methods for doing malware analysis and detection on corporate systems, as well as the resources available to assist with sample inspection to reduce the impact of malware assaults on an organization's operations. The investigator must first choose which methods and instruments to use for analysis. Static analysis, which includes malware scanners and detectors, is the first line of defense against malware. As technology advances, malware creators use various techniques to conceal their source code from scanners and detectors that search for strings, pattern matching, and other similar patterns to determine hash values that may be used to identify the infection. Malware experts decompress the packed file into unpacked one to examine obfuscated malware. This study examines efforts to investigate the many techniques and instruments malware uses in the real world.

Digital Transformation in Accounting and Auditing: Insights from The ChatGPT Example

This research delves into the evolving role of the accounting and auditing professions in the digital age, with a focus on the implications of ChatGPT, a sophisticated artificial intelligence tool developed by OpenAI. The study employs a novel approach by engaging ChatGPT to predict the future of these professions, further probing with targeted inquiries to deepen the understanding. The analysis reveals that despite the rapid digital transformation and integration of AI in accounting processes, the core accounting profession is likely to persist. However, it underscores a significant shift in skill requirements, emphasizing the importance for accountants to enhance their expertise in data analysis, cyber security, and technological adaptability. The study also explores AI's potential applications in auditing, suggesting that AI can augment efficiency, accuracy, and risk assessment, while also highlighting the irreplaceable value of human judgment in complex scenarios. Interestingly, the insights generated through interactions with ChatGPT show a strong alignment with existing literature, indicating the tool's utility and reliability in professional contexts. The findings suggest that embracing technological advancements and continuous learning are imperative for accountants to remain relevant and competitive in this rapidly evolving digital landscape.

Secure software design evaluation and decision making model for ubiquitous computing: A two-stage ANN-Fuzzy AHP approach

Ubiquitous computing systems are deployed everywhere to process and service complex applications in real time. It has been noticed that most ubiquitous computing practitioners are trying to develop a highly secure design while maintaining usability. Unfortunately, the highly secure design of software becomes worthless because the usability of software is very low. When improving the security of software design, underlying security design threats (SDTs) and secure design practices (SDPs) play an important role in improving the design security of ubiquitous computing projects. Therefore, the objective of this study is to develop an SSD evaluation and decision-making model using artificial neural network (ANN) and fuzzy analytical hierarchy process (FAHP), for analyzing the SSD threats and its practices to improve ubiquitous computing. Initially, we identified 11 major SDTs and 50 SDPs to mitigate these threats by conducting an empirical study with 50 ubiquitous computing security experts. Moreover, 13 ubiquitous computing security expert panel reviews were conducted to analyze the interrelationships among the SDTs and SDPs using ANN and FAHP approaches. The ANN outcome presents that the foremost significant independent threat in predicting SSD intention is “SDT2: failure to adhere to accepted security design principles” (100%) importance. Furthermore, the FAHP was used to prioritize the list of SDPs. The results and analysis of this study provide a ranked-based decision-making model, which assists the ubiquitous computing practitioners in considering the most weighted SSD practice (“SDP: Implement Sandboxing and respect for User Privacy” having global weight: 0.05088 and global rank: 1) on priority. The study findings will assist real-world industry experts and academic researchers to improve and develop new techniques for the improvements of ubiquitous computing projects.

IoT Security in the Era of Ubiquitous Computing: A Multidisciplinary Approach to Addressing Vulnerabilities and Promoting Resilience

The Internet of Things (IoT) has rapidly become a pivotal, transformative force, seamlessly integrating billions of physical devices through sophisticated networks of embedded sensors, software, and internet connectivity. This expansive and interconnected ecosystem offers a broad spectrum of applications, significantly benefiting urban infrastructure with innovative solutions, enhancing industrial operations through optimization, and enriching consumer experiences with smart devices for safety and convenience. Despite the numerous benefits, the widespread adoption of IoT technologies has challenges, particularly in security and privacy. The proliferation of IoT devices has opened up new avenues for potential cyber threats, posing risks of data breaches and privacy violations. An in-depth analysis of notable IoT security incidents, such as the 2015 Jeep Hack, the Owlet WiFi Baby Heart Monitor Hack, and the TRENDnet Webcam Hack, highlights the critical vulnerabilities inherent in many IoT systems.
 Organizations must adopt comprehensive and robust security measures to address these security concerns, including implementing advanced encryption protocols, deploying effective firewalls stringent access control mechanisms, and conducting regular security audits. A multi-layered security architecture becomes essential in mitigating such threats and ensuring the integrity of IoT networks. Furthermore, integrating blockchain technology presents a promising enhancement to IoT security and privacy protocols. Blockchain's inherent features of decentralization, transparency, and immutability offer an additional layer of security, making it more difficult for unauthorized entities to compromise IoT systems.
 Equally crucial is the need to elevate IoT security awareness among organizations; this can be achieved through persistent research, fostering collaborations with security experts, and promoting best practices in IoT security. By actively addressing these security challenges, organizations can not only harness the full potential of IoT but also protect their reputations, build trust with stakeholders, and ensure the privacy and safety of their data. Therefore, while IoT presents an array of opportunities for innovation and efficiency, the importance of vigilance in security cannot be overstated. Balancing the benefits of IoT with robust security measures will be vital to realizing its full potential safely and reliably.

Secure software development and testing: A model-based methodology

Modern industries widely rely upon software and IT services, in a context where cybercrime is rapidly spreading in more and more sectors. Unfortunately, despite greater general awareness of security risks and the availability of security tools that can help to cope with those risks, many organizations (especially medium/small-size ones) still lag when it comes to building security into their services. This is mainly due to the limited security skills of common developers/IT project managers and to the typically high costs of security procedures. In fact, while automated tools exist to perform code analysis, vulnerability scanning, or security testing, the manual intervention of security experts is still required not only for security analysis and design, but also to configure and elaborate the output of the security testing tools.In this paper, we propose a novel secure software development methodology aimed at supporting developers from security design to security testing, suitable for integration within modern DevOps pipelines according to a DevSecOps (or SecDevOps) approach. The proposed methodology leverages a model-based process that enables identifying existing threats, selecting appropriate countermeasures to enforce, and verify their mitigation effectiveness through both static assessment procedures and targeted security tests. To demonstrate our approach's feasibility and concretely illustrate the devised activities, we provide a step-by-step description of the whole process concerning a containerized microservice-based application case study. In addition, we discuss the application of the proposed methodology, in its threat modeling and security testing phases, to a well-known vulnerable web application widely used for security training purposes, to illustrate that we can identify most of the existing vulnerabilities and determine appropriate test plans to assess and mitigate such vulnerabilities.

State of the Art - Cybercrimes and Cyber Security Policies and Countermeasures

Abstract: The world has become a global village because of the availability of Internet Connectivity and mobile devices to most of the masses around the world. The benefits of digitalization goes hand in hand with a steep rise in cybercrimes, cyber threats and cyberattacks. Cyber security has become crucial in our day to day life in order to protect our information systems. Cyber security has become a buzz word and has kindled interests among cyber security experts, researchers, academicians, private organizations and the government alike. This paper explores the state of the art in Cyber Security and the challenges caused by cybercrimes, cyber threats and cyberattacks. Various methods to protect individuals, organizations, and governments from cybercrimes has been addressed in this paper. Importantly, ways to detect and report cybercrimes are also dealt with.

Survey: Automatic generation of attack trees and attack graphs

Graphical security models constitute a well-known, user-friendly way to represent the security of a system. These classes of models are used by security experts to identify vulnerabilities and assess the security of a system. The manual construction of these models can be tedious, especially for large enterprises. Consequently, the research community is trying to address this issue by proposing methods for the automatic generation of such models. In this work, we present a survey illustrating the current status of the automatic generation of two popular kinds of graphical security models: Attack Trees and Attack Graphs. The goal of this survey is to present the current methodologies used in the field, compare them, and present the challenges and future directions to the research community.

Quantifying potential cyber-attack risks in maritime transportation under Dempster–Shafer theory FMECA and rule-based Bayesian network modelling

Maritime cyber security is a growing concern in the shipping industry as reliance on technology increases. With the potential for cyber attacks to disrupt vessel operations, compromise sensitive information, and endanger crew and cargo, assessing the risks and developing effective risk management strategies is crucial. On the other hand, cyber risk assessments in maritime transportation have been limited, and there is a lack of probabilistic databases of cyber threats. To remedy this gap, this paper presents a probabilistic approach to estimate cyber threats, especially for the bridge navigational systems in the maritime sector, focusing on the Bayesian network model to evaluate cyber risks for integrated bridge navigational systems onboard, and marine security experts evaluate 32 threats with respect to FMECA (Failure modes, Effect and Criticality Analysis) parameters. Dempster-Shafer theory is utilised to consolidate expert opinions for cyber risk analysis. The findings of the research showed that AIS spoofing poses the highest risk. GPS jamming is the other significant threat to ship bridge navigational systems during cyber attacks. The research provides a basis for identifying cyber threats and risks, calculating the highest risk values and developing control actions to maintain effective risk management strategies for safe and secure maritime transportation.

School Security and Safety Challenges in Nigeria: Evaluating the Existential Threats of Domestic Terrorism in the North East Region


 Abstract
 
 The ubiquity of terrorism cannot be overemphasized as it has become a global common phenomenon. Schools are easy prey of terrorist attack since they lack the maximum security apparatuses that other state institutions possess. All over the world, terrorist attacks have taken place inside and beside school premises raising serious concern for government, security experts and school authorities. The recurring events of school shootings that keep taking place in the United States of America, Beslan Russia and even in Norway have proven that no school is totally immune from attacks by insiders, criminals, terrorists and non-state actors. Consequently, schools should be protected from these threats given the public emotion, sensitivity and global concerns about places of learning. In the last decade or so, Nigeria has gone through the harrowing experiences of terrorist attacks on schools especially in the Northern part of the country and the North East in particular by Boko Haram, ISWAP and currently armed bandits. This study therefore examines this ugly menace that has defied sustainable solutions to insecurity and safety threats in Schools. The study is descriptive in nature and gathered information from materials already available and questionnaires from individuals and schools authorities.

Who will keep the lights on? Expertise and inclusion in cyber security visions of future energy systems

Increasing internet connectivity in smart energy systems poses serious cyber security concerns. Using a lens of “sociotechnical imaginaries”, we explored how experts envision the future(s) of cyber security governance in the energy sector. The three imaginaries identified present cyber security issues as a matter of design, as a support function, and as public trust. We argue that while each vision prioritises a different set of actions, none of them adequately considers the interplay between inclusivity and expertise. By exploring what each of these potential futures enables, hinders, or assumes, as well as how they compete or complement each other, we derive a number of recommendations to ensure cyber security expertise becomes more inclusive, democratic and participatory. The paper contributes to energy social science debates by furthering the understanding of how energy experts conceptualise future computing systems for security and sustainability.

An Ensemble of Text Convolutional Neural Networks and Multi-Head Attention Layers for Classifying Threats in Network Packets

Using traditional methods based on detection rules written by human security experts presents significant challenges for the accurate detection of network threats, which are becoming increasingly sophisticated. In order to deal with the limitations of traditional methods, network threat detection techniques utilizing artificial intelligence technologies such as machine learning are being extensively studied. Research has also been conducted on analyzing various string patterns in network packet payloads through natural language processing techniques to detect attack intent. However, due to the nature of packet payloads that contain binary and text data, a new approach is needed that goes beyond typical natural language processing techniques. In this paper, we study a token extraction method optimized for payloads using n-gram and byte-pair encoding techniques. Furthermore, we generate embedding vectors that can understand the context of the packet payload using algorithms such as Word2Vec and FastText. We also compute the embedding of various header data associated with packets such as IP addresses and ports. Given these features, we combine a text 1D CNN and a multi-head attention network in a novel fashion. We validated the effectiveness of our classification technique on the CICIDS2017 open dataset and over half a million data collected by The Education Cyber Security Center (ECSC), currently operating in South Korea. The proposed model showed remarkable performance compared to previous studies, achieving highly accurate classification with an F1-score of 0.998. Our model can also preprocess and classify 150,000 network threats per minute, helping security agents in the field maximize their time and analyze more complex attack patterns.