[
    {
        "id": "osp-19763",
        "type": "article-journal",
        "title": "A Structured State Space Sequence Model for Multi-Class Classification of Malware",
        "author": [
            {
                "family": "Andam",
                "given": "Emmanuela"
            },
            {
                "family": "Shaaban",
                "given": "Rana"
            },
            {
                "family": "Grant",
                "given": "Emanuel"
            },
            {
                "family": "Kaabouch",
                "given": "Naima"
            }
        ],
        "URL": "https://omanscience.com/en/articles/a-structured-state-space-sequence-model-for-multi-class-classification-of-malware",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "By 2030, Internet of Things (IoT) devices are projected to reach 40 billion, with fast-paced technological advancements in fields such as industry, healthcare, agriculture, automobiles, and building/home automation systems. This expansion has created a large attack surface for cybercrime, as the majority of these devices open the door for cybercriminals to exploit vulnerabilities, as they lack adequate built-in security. Cybercriminals launch malware attacks to compromise systems or steal sensitive data, and once a system is compromised, a ransom is typically demanded for its release. Current cybersecurity measures in place are being outpaced by the rapid growth of the IoT, which is accompanied by a subsequent growth in malware variants being created per day. Recognizing this pitfall, this research examines and proposes a novel approach to malware detection and classification to safeguard devices from further attacks and make IoT systems more robust and secure. The framework proposed utilizes a Structured State Space Sequence (S4) model, which discretizes sequences of malware samples in a sequence and captures long-range dependencies, essentially identifying the \"cause\" and \"effect\" hidden within malware execution flow. This study presents two novel contributions: the first empirical application of the S4 model for malware analysis, and a comprehensive comparison of its performance against other deep learning architectures, laying the stepping stone for future research in this new paradigm."
    }
]