A qualitative analysis of an ontology based issue resolution system for cyber attack management

Simmons, Chris; Shiva, Sajjan G.; Simmons, Lakisha L.

doi:10.1109/cyber.2014.6917483

Cited by 11 publications

(12 citation statements)

References 11 publications

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“…Focus on single issue encountered by AMS [17], [23]- [25], [25]- [29] ✓ Detailed architecture of conventional AMS X ✓…”

Section: Features Previous Work This Papermentioning

confidence: 99%

“…Earlier researchers have emphasized the importance of understanding Threat vectors and proposed attack taxonomies. The authors in [23] proposed a taxonomy of Threat vectors in terms of five classifiers to illustrate the category of an attack using attack vector, defense, operational influence, informational repercussion, and by attack target. Ivaturi and Janczewski [52] also considered increasing Threat vectors as a threat to malware analysis and proposed a taxonomy for social engineering attacks as attackers are using social engineering methods by aiming at the human elements and merging such techniques with conventional technical methods.…”

Section: A Increasing Threat Vectorsmentioning

confidence: 99%

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Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework

Khan,

Durad,

Khan

et al. 2024

IEEE Access

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The cyber realm is overwhelmed with dynamic malware that promptly penetrates all defense mechanisms, operates unapprehended to the user, and covertly causes damage to sensitive data. The current generation of cyber users is being victimized by the interpolation of malware each day due to the pervasive progression of Internet connectivity. Malware is dispersed to infiltrate the security, privacy, and integrity of the system. Conventional malware detection systems do not have the potential to detect novel malware without the accessibility of their signatures, which gives rise to a high False Negative Rate (FNR). Previously, there were numerous attempts to address the issue of malware detection, but none of them effectively combined the capabilities of signature-based and machine learning-based detection engines. To address this issue, we have developed an integrated Anti-Malware System (AMS) architecture that incorporates both conventional signature-based detection and AI-based detection modules. Our approach employs a Generative Adversarial Network (GAN) based Malware Classifier Optimizer (MCOGAN) framework, which can optimize a malware classifier. This framework utilizes GANs to generate fabricated benign files that can be used to train external discriminators for optimization purposes. We describe our proposed framework and anti-malware system in detail to provide a better understanding of how a malware detection system works. We evaluate our approach using the Figshare dataset and state-of-the-art models as discriminators, and our results demonstrate improved malware detection performance compared to existing models.

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“…Focus on single issue encountered by AMS [17], [23]- [25], [25]- [29] ✓ Detailed architecture of conventional AMS X ✓…”

Section: Features Previous Work This Papermentioning

confidence: 99%

Section: A Increasing Threat Vectorsmentioning

confidence: 99%

Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework

Khan,

Durad,

Khan

et al. 2024

IEEE Access

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“…The authentication function is critical to ensuring the security and privacy of IoT devices. The new authentication systems cannot be tested in precise detail (26). It is possible to utilize these programs to download malicious payloads that may be used to spy on a computer, for example, when they are installed.…”

Section: Ecs Transactions 107 (1) 15043-15052 (2022)mentioning

confidence: 99%

Internet of Things: Security Vulnerabilities and Countermeasures

et al. 2022

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People's lives will be transformed by the Internet of Things (IoT), which connects everything from smart phones and sensors to the cloud to mobile apps and web-based controls. Security concerns are on the rise as the number of heterogeneous devices and data processing grows. Most IoT apps and devices are also well-known for their security flaws, making them vulnerable to many forms of attacks. To further complicate matters, threats have variable consequences on the device's security and quality. Unfortunately, enterprises are having difficulty determining which risks they face from their data assets and how to mitigate them. To better identify IoT security risks, this article has built a taxonomy based on the application domain and the design of the architecture. Servers, clients, IoT development boards, sensors, cloud subscriptions, and other components all play a role in this test setup. Vulnerability and network host scanning technologies are used to obtain raw data relevant to IoT-based smart city projects. This article also includes a mitigation approach for IoT network flaws.

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“…Unauthorized disclosures include the unauthorized access or misuse of information and the stripping away of the confidentiality of personal data (Bertot et al, 2012; Layne & Lee, 2001; Picazo-Vela et al, 2012; Zavattaro & Sementelli, 2014). Disruptive cyberattacks, as outlined by Simmons et al (2014), refer to any insertion of malware that disrupts the agency activities, including ransoms or breaches (Bertot et al, 2012). We analyze variation in reports of unauthorized disclosures and disruptive cyberattacks as related to social aspects (e.g., managerial perceptions of social media and open data initiatives) and technology capacity (e.g., social media use, open data efforts, and ICT use for collaborative work purposes).…”

Section: Introductionmentioning

confidence: 99%

Cybersecurity Threats in Local Government: A Sociotechnical Perspective

Frandell

Feeney

2022

The American Review of Public Administration

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The use of information and communication technologies (ICTs) by local governments is widespread and meant to improve managerial effectiveness and public engagement. ICTs are commonly used by governments to collaborate and communicate with stakeholders. Yet, the use of ICTs increases local governments exposure to cyberthreats. Cyberthreats are increasing and local governments are often under-resourced and underprepared for them. While many organizations combat cyberthreats with technological solutions, it is well known that social aspects—including manager vigilance and buy-in—are critical in reducing cyber incidents. Thus, governments require both social and technical solutions to cyberthreats. This research takes a sociotechnical perspective to examine the relationships between social (e.g., values and perceptions) and technical factors (e.g., design and capacity) and cyber incidents in local government. We use data from a 2018 national survey of public managers in 500 U.S. cities, data from city government websites, and the U.S. Census. The results indicate that manager buy-in and perceptions interact with technical aspects to explain reported cyber incidents in government. The findings expand our understanding of how social and technical factors are associated with cyberthreats in government, particularly manager.

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A qualitative analysis of an ontology based issue resolution system for cyber attack management

Cited by 11 publications

References 11 publications

Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework

Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework

Internet of Things: Security Vulnerabilities and Countermeasures

Cybersecurity Threats in Local Government: A Sociotechnical Perspective

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