GRAVITAS: Graphical Reticulated Attack Vectors for Internet-of-Things Aggregate Security

Brown, Jacob; Saha, Tanujay; Jha, Niraj K.

doi:10.1109/tetc.2021.3082525

Cited by 12 publications

(5 citation statements)

References 23 publications

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“…Although the naive method is much more lightweight than series models, it has two drawbacks that limit its applicability in real-world scenarios. First, the naive pattern matching approach cannot detect semantically-similar instruction calls [13]. As a result, the attacker may avoid detection by executing different APIs with the same functionality as the instruction in the signature.…”

Section: Series Checkermentioning

confidence: 99%

Explainable Machine Learning for API Call Sequence Analysis

Khan¹

2022

Preprint

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<p>Although deep learning achieves state-of-the-art performance in tasks like vulnerability detection and classification, they have drawbacks. A significant disadvantage of deep learning methods is their inexplicability. Many deep learning models, especially sequential models like long short-term memories and gated recurrent units, operate as black-boxes. The outputs of these black-box models are uninterpretable by security analysts and software developers. This inexplicability of deep learning models hinders their acceptance in enterprises. It also prevents the knowledgeable system experts from removing the spurious correlations that the model might have learned. Thus, it is highly desirable to have explainable deep learning models to promote their acceptance in real-world systems. Another major drawback of deep learning is adversarial machine learning. Cyber attackers can utilize adversarial machine learning to fool the deep learning-based cyber-attack detectors. Prior research has shown that cyber-defenders can use explainable artificial intelligence to defend against adversarial machine learning attacks. Therefore, adding explainability to deep learning models for cyber-security is highly desirable. This article proposes a method that enhances the explainability of system-call sequence analysis-based vulnerability detection. Our method can potentially pinpoint the precise instruction calls that have triggered the vulnerability. This insight is valuable for the security analyst as he can now evaluate the sequence of system calls more efficiently.</p>

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Section: Series Checkermentioning

confidence: 99%

Explainable Machine Learning for API Call Sequence Analysis

Khan¹

2022

Preprint

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“…We target this problem in this article. ML-based attack graphs have been used previously to analyze the security of IoT and cyber-physical systems [6,15]. We use ML on the attack graphs to enable our framework to scale to larger networks.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems

Saha¹,

Aaraj²,

Jha³

2021

Preprint

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The core network architecture of telecommunication systems has undergone a paradigm shift in the fifth-generation (5G) networks. 5G networks have transitioned to software-defined infrastructures, thereby reducing their dependence on hardware-based network functions. New technologies, like network function virtualization and software-defined networking, have been incorporated in the 5G core network (5GCN) architecture to enable this transition. This has resulted in significant improvements in efficiency, performance, and robustness of the networks. However, this has also made the core network more vulnerable, as software systems are generally easier to compromise than hardware systems. In this article, we present a comprehensive security analysis framework for the 5GCN. The novelty of this approach lies in the creation and analysis of attack graphs of the software-defined and virtualized 5GCN through machine learning. This analysis points to 119 novel possible exploits in the 5GCN. We demonstrate that these possible exploits of 5GCN vulnerabilities generate five novel attacks on the 5G Authentication and Key Agreement protocol. We combine the attacks at the network, protocol, and the application layers to generate complex attack vectors. In a case study, we use these attack vectors to find four novel security loopholes in WhatsApp running on a 5G network.

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“…It is an important challenge to detect DeepFakes in realtime to restore faith in digital media. Machine learning has found applications in various domains of cybersecurity like network security [9], security of internet-ofthings [8,2,10], and anomaly detection [6]. A myriad of approaches from signal processing, graphics and computer vision have been combined in the attempt to detect Deep-Fakes.…”

Section: Introductionmentioning

confidence: 99%

Analyses of Algorithms to Detect DeepFake Videos

Kumar¹

2023

Preprint

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<p>Deep learning has impacted various aspects of artificial intelligence ranging from computer vision to reinforcement learning. However, it has also inspired malicious actors to spread misinformation without detection. Fake videos can now be generated with precision high enough to go undetected by traditional video forensics. These videos, popularly referred to as DeepFakes, are being used to spread misinformation. Deep learning based techniques have evolved over the years to detect such DeepFakes. This article analyzes the DeepFake detection algorithms and implements five such algorithms on a standard dataset. The advantages and challenges of the implemented algorithms are explored in detail.</p>

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GRAVITAS: Graphical Reticulated Attack Vectors for Internet-of-Things Aggregate Security

Cited by 12 publications

References 23 publications

Explainable Machine Learning for API Call Sequence Analysis

Explainable Machine Learning for API Call Sequence Analysis

Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems

Analyses of Algorithms to Detect DeepFake Videos

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