A malware detection system using a hybrid approach of multi-heads attention-based control flow traces and image visualization

Ullah, Farhan; Srivastava, Gautam; Ullah, Shamsher

doi:10.1186/s13677-022-00349-8

Cited by 22 publications

(9 citation statements)

References 30 publications

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“…Ullah et al [8] and Mani et al [9] provide perspectives on securing advanced computational models against emerging threats. These studies underscore the importance of proactive and innovative defenses in protecting sophisticated computational systems.…”

Section: Dynamic Analysismentioning

confidence: 99%

FFRA: A Fine-Grained Function-Level Framework to Reduce the Attack Surface

Zhang,

Liu,

Fan

et al. 2024

CSSE

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System calls are essential interfaces that enable applications to access and utilize the operating system's services and resources. Attackers frequently exploit application's vulnerabilities and misuse system calls to execute malicious code, aiming to elevate privileges and so on. Consequently, restricting the misuse of system calls becomes a crucial measure in ensuring system security. It is an effective method known as reducing the attack surface. Existing attack surface reduction techniques construct a global whitelist of system calls for the entire lifetime of the application, which is coarse-grained. In this paper, we propose a Fine-grained Function-level framework to Reduce the Attack surface (FFRA). FFRA employs software static analysis to obtain the function call graph of the application. Combining the graph with a mapping of library functions generates each function's legitimate system calls. As far as we know, it is the first approach to construct the whitelist of system calls for each function of the application. We have implemented a prototype of FFRA and evaluated its effectiveness with six popular server applications. The experimental results show that it disables 33% more system calls compared to existing approaches while detecting 15% more shellcode vulnerabilities. Our framework outperforms existing models by defending against a broader range of attacks. Integrated into antivirus software and intrusion prevention systems, FFRA could effectively counter malware by precisely restricting system calls.

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Section: Dynamic Analysismentioning

confidence: 99%

FFRA: A Fine-Grained Function-Level Framework to Reduce the Attack Surface

Zhang,

Liu,

Fan

et al. 2024

CSSE

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“…Consequently, creating new skills that can reliably detect malicious behavior is one of the main tasks of the Android security department. Ullah et al [17] presented a novel feature representation approach for malware identification that combines API-Call Graphs (ACGs) with byte-level picture representation.…”

Section: Mishra Et Al [10] 2020mentioning

confidence: 99%

“…Opting for cloud-based detection instead of more traditional methods has many advantages. With the advent of cloud computing, which makes use of processing power and enormous databases, malware detection has the potential to become more rapid and accurate [17]. Computers, mobile devices, and cyber-physical systems can all benefit from the cloud-based method's enhanced object identification capabilities.…”

Section: Introductionmentioning

confidence: 99%

Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review

Rao,

Jain

2024

IJSSE

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Cloud computing, integral for data storage and online services, presents significant advantages over traditional data storage and distribution methods, including enhanced convenience, on-demand storage, scalability, and cost efficiency. Its growing adoption in securing Internet of Things (IoT) and cyber-physical systems (CPS) against various cyber threats offers numerous opportunities. Despite the continuous evolution of malware and the lack of a universally effective detection method, cloud environments provide a promising approach for malware detection. Cloud computing, recognized for its efficiency, scalability, flexibility, and reliability on elastic resources, is widely utilized in the IT industry to support IT infrastructure and services. However, one of the foremost security challenges faced is malware attacks. Certain antivirus scanners struggle to detect metamorphic or encrypted malware in cloud environments due to complexity and scale, allowing such threats to evade detection. High detection rates with precision in reducing false positives are essential. Machine learning (ML) classifiers, a vital component in Artificial Intelligence (AI) systems, require training on extensive data volumes to develop credible models with high detection rates. Traditional detection methods face challenges in identifying complex malware, as modern malware employs contemporary packaging and obfuscation techniques to circumvent security measures. This paper provides a detailed discussion on detecting malware in cloud environments and the advantages of cloud computing in safeguarding IoT and CPS from cyber attacks. It presents a survey on malware analysis and detection models, aiding researchers in identifying limitations of traditional malware detection models in cloud environments and inspiring the design of innovative models with enhanced quality of service levels.

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“…To regulate data flow, cache data, offload data, secure connections, and not lose connections, dynamic network access was essential. [22][23][24] Their study describes a novel, distributed resource allocation method for V2V communications 25 informed by DRL. This method is adaptable to both unicast and broadcast settings.…”

Section: Literature Reviewmentioning

confidence: 99%

“…DRL blends deep learning and reinforcement learning. To regulate data flow, cache data, offload data, secure connections, and not lose connections, dynamic network access was essential 22‐24 …”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent multimedia content delivery in 5G/6G networks: A reinforcement learning approach

Iqbal

Farhan

Ullah

et al. 2023

Trans Emerging Tel Tech

Self Cite

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Multimedia content in 5G/6G networks makes safe, confidential, and efficient content delivery difficult. Intelligent systems that adapt to the ever‐changing network environment are needed to distribute multimedia content in these networks. Reinforcement learning (RL) can optimize multimedia content distribution based on network congestion, capacity, and user preferences. This study proposes RL‐based intelligent multimedia content distribution. RL algorithms learn from the network environment and generate optimum judgments incorporating several aspects of the suggested framework. The framework delivers multimedia material securely and privately with great quality. This study provides an intelligent multimedia content delivery architecture that uses RL approaches to solve 5G/6G content delivery problems. This research presents an RL system optimized with the double DQN algorithm having a reward of 51604.93 in 7000 episodes for efficient video file sharing on intracity buses. The RL agent balances network congestion and bandwidth by leveraging multiple sources such as bus and intersection caches and base stations, improving secure multimedia content delivery in 5G/6G networks and enhancing the passenger experience. The study confirms the system's effectiveness using reward and loss metrics and identifies potential future research directions. Future work could explore additional RL algorithms, scalability for larger networks, complex delivery scenarios, and integration with blockchain and edge computing for improved security and efficiency in multimedia content delivery.

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A malware detection system using a hybrid approach of multi-heads attention-based control flow traces and image visualization

Cited by 22 publications

References 30 publications

FFRA: A Fine-Grained Function-Level Framework to Reduce the Attack Surface

FFRA: A Fine-Grained Function-Level Framework to Reduce the Attack Surface

Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review

Intelligent multimedia content delivery in 5G/6G networks: A reinforcement learning approach

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