Feature Subset Selection for Malware Detection in Smart IoT Platforms

Abawajy, Jemal; Darem, A. Basit; Alhashmi, Asma A.

doi:10.3390/s21041374

Cited by 39 publications

(18 citation statements)

References 37 publications

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“…The proposed framework extracted the malicious information and fed it to the blockchain by utilizing a classification algorithm, and consequently, improving the efficiency of the malware classification process since all the malicious information is stored in the blockchain. An additional proposed framework that uses the machine learning approach was proposed by [ 26 ]. The authors developed a subset of features based on examining the performance of various feature selection algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices

Almomani

Alkhayer

El‐Shafai

2022

Sensors

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Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. This hiding aims to achieve the confidentiality of the IoT secret data; whether it is benign or malicious (e.g., ransomware) and for defensive or offensive purposes. This paper introduces a hybrid crypto-steganography approach for ransomware hiding within high-resolution video frames. This proposed approach is based on hybridizing an AES (advanced encryption standard) algorithm and LSB (least signification bit) steganography process. Initially, AES encrypts the secret Android ransomware data, and then LSB embeds it based on random selection criteria for the cover video pixels. This research examined broad objective and subjective quality assessment metrics to evaluate the performance of the proposed hybrid approach. We used different sizes of ransomware samples and different resolutions of HEVC (high-efficiency video coding) frames to conduct simulation experiments and comparison studies. The assessment results prove the superior efficiency of the introduced hybrid crypto-steganography approach compared to other existing steganography approaches in terms of (a) achieving the integrity of the secret ransomware data, (b) ensuring higher imperceptibility of stego video frames, (3) introducing a multi-level security approach using the AES encryption in addition to the LSB steganography, (4) performing randomness embedding based on RPS (random pixel selection) for concealing secret ransomware bits, (5) succeeding in fully extracting the ransomware data at the receiver side, (6) obtaining strong subjective and objective qualities for all tested evaluation metrics, (7) embedding different sizes of secret data at the same time within the video frame, and finally (8) passing the security scanning tests of 70 antivirus engines without detecting the existence of the embedded ransomware.

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Section: Literature Reviewmentioning

confidence: 99%

A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices

Almomani

Alkhayer

El‐Shafai

2022

Sensors

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“…Quality features are crucial for building effective machine learning-based classification models. The opcode sequence is used effectively in malware detection, but if there are too many features leads to the model complexity and the "curse of dimensionality" problem will occur [8]. In addition to that, data normally contains significant noises and irrelevant features that add little or no value to the performance of the learning algorithms.…”

Section: Static Feature Selection For Iot Malware Detectionmentioning

confidence: 99%

“…Today, feature selection approaches are organized into filters, wrapper, embedded and hybrid methods. The filter methods select a subset of features without altering their original representation [20]. This method is used in many machine learning models because it is not constrained by any machine learning method.…”

Section: B Opcode Feature Selectionmentioning

confidence: 99%

Static Feature Selection for IoT Malware Detection

Toan¹,

Dung²,

Thang³

2022

ISJ

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Abstract—Our world has recently witnessed the explosive growth of IoT networks as one of the pillars of the 4th industrial revolution. Malware on IoT devices also grows accordingly in number and sophisticated techniques. Therefore, it is necessary to come up with more efficient approaches to IoT malware detection with machine learning models that can be used in solutions using limited resources. In this paper, we study and evaluate the efficiency of using a weight of term frequency– inverse document frequency model in feature selection method combined with an effective machine learning model in IoT malware detection based on opcode sequence features. We performed experiments on a MIPS ELF dataset that included 4,511 malicious samples with main four classes and 4,393 benign programs. Experiment results show that our proposed method has very good performance on the above dataset with detection and classification accuracy which are 99.8% and 95.8% respectively while the models only use 20 opcodes that have the highest weight values. Tóm tắt— Cuộc cách mạng công nghiệp lần thứ 4 với sự phát triển của các thiết bị IoT đã và đang ảnh hưởng sâu rộng đến các lĩnh vực trong đời sống xã hội. Các mã độc trên thiết bị IoT ngày càng gia tăng về số lượng và sử dụng các kỹ thuật lẩn tránh tinh vi. Điều này đòi hỏi cần có các phương pháp tiếp cận hiệu quả hơn trong phát hiện mã độc trên thiết bị IoT với các mô hình học máy hiệu quả, có khả năng ứng dụng trong các giải pháp đảm bảo an toàn thông tin có tài nguyên hạn chế. Trong bài báo này, chúng tôi nghiên cứu và đánh giá hiệu quả của việc\ xác định trọng số trong tìm kiếm truy xuất thông tin trong phương pháp trích chọn đặc trưng kết hợp mô hình học máy hiệu quả cho việc phát hiện mã độc IoT dựa trên đặc trưng chuỗi opcode. Chúng tôi đã tiến hành thử nghiệm với một tập dữ liệu MIPS ELF gồm 4.511 mẫu độc hại với 4 loại chính và 4.393 chương trình lành tính. Các kết quả thực nghiệm đã chứng minh rằng phương pháp của bài báo đề xuất cho kết quả tốt đối với tập dữ liệu nêu trên, tỉ lệ phát hiện và phân 4 loại mã độc cao nhất tương ứng là 99.8% và 95.8% khi chỉ cần sử dụng 20 opcode có giá trị trọng số cao nhất.

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“…The feature Subset Selection for Malware Detection in Smart IoT Platforms was developed by Abawajy et al 35 This approach utilized Chi‐squared technique, Naive Bayes approach for extraction of features. Accuracy, f‐measure, precision, recall, percentage of permissions were the performance measures employed and the analysis revealed that the accuracy rate was high with enhanced detection rate.…”

Section: Review Of Related Workmentioning

confidence: 99%

Electro search optimization based long short‐term memory network for mobile malware detection

Shanmugam

Venkateswaralu²,

Rajalakshmi

et al. 2022

Concurrency and Computation

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Mobile malware is malicious software designed specifically for targeting various mobile gadgets like tablets, smartphones, and so forth, in which any type of malicious code affecting the mobile devices without the knowledge of the user. The increasing number of users encourages the hacker for generating various malware applications. Therefore, in this paper, we utilized three vital phases namely the pre‐processing process, Feature extraction process as well as classification process in which the malicious data are detected. In the pre‐processing phase, an Androguard tool is used for decompiling and disassembling the android applications. The API call features are extracted in the feature extraction phase and in the classification phase, long short term memory based electro search optimization (LSTM‐ESO) is employed to detect the unknown mobile applications as benign or malicious. The malicious mobile detecting accuracy deals in requesting permission and exhibiting malicious code applications. In order to enhance the identification of various malware applications, this paper utilized frequency analysis and permissions of API calls. Finally, the experimental analysis is performed by evaluating the performance measures like accuracy, precision, recall, and F‐measure. From the evaluation outcome, it is observed that the classification accuracy obtained is 97.69%.

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Feature Subset Selection for Malware Detection in Smart IoT Platforms

Cited by 39 publications

References 37 publications

A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices

A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices

Static Feature Selection for IoT Malware Detection

Electro search optimization based long short‐term memory network for mobile malware detection

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