The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming technologies and infrastructure, the agricultural sector has become an attractive target for cyberattacks. This paper presents a systematic literature review that assesses the applicability of existing cyber threat intelligence (CTI) techniques within smart farming infrastructures (SFIs). We develop a comprehensive taxonomy of CTI techniques and sources, specifically tailored to the SFI context, addressing the unique cyber threat challenges in this domain. A crucial finding of our review is the identified need for a virtual Chief Information Security Officer (vCISO) in smart agriculture. While the concept of a vCISO is not yet established in the agricultural sector, our study highlights its potential significance. The implementation of a vCISO could play a pivotal role in enhancing cybersecurity measures by offering strategic guidance, developing robust security protocols, and facilitating real-time threat analysis and response strategies. This approach is critical for safeguarding the food supply chain against the evolving landscape of cyber threats. Our research underscores the importance of integrating a vCISO framework into smart farming practices as a vital step towards strengthening cybersecurity. This is essential for protecting the agriculture sector in the era of digital transformation, ensuring the resilience and sustainability of the food supply chain against emerging cyber risks. 

Agriculture 4.0 and Beyond: Evaluating Cyber Threat Intelligence Sources and Techniques in Smart Farming Ecosystems

Traditional malware detection systems based on signature-based detection methods cannot detect new and unseen malware. Moreover, conventional machine learning methods for malware detection have utilized features extracted through static program analysis or dynamic analysis, which requires code debugging and execution primarily through offline processing; hence not a scalable approach. This paper proposes a novel intelligent malware classification using a deep convolution neural network (IMCNN) in organizational networks enabled with Honeypots. Systematic customization of pre-trained convolutional neural networks(CNN) as a transfer learning and ensemble learning as a classification is presented to detect intelligent modern-day malware. Real-world malware samples are systematically labeled and visualized into grayscale images. Four cutting-edge deep CNN models - VGG16, VGG19, InceptionV3, and ResNet50, are trained on the ImageNet database (≥1 million) and fine-tuned as feature extractors along with a basic CNN model. Three strategies are designed for feature extraction and selection: Rectified linear unit (ReLU) fully connected layer embedded in a deep CNN model, principal component analysis (PCA), and singular value decomposition(SVD). Reduced sets of features are stacked and used to train k-nearest neighbor (k-NN), support vector machine (SVM), and random forest (RF) classifiers for predictions. Subsequently, the predictive probabilities of different machine-learned models are ensembled using a soft voting method for final classification. The proposed method is evaluated on MalImg datasets (9339 malware samples of 25 families) and real-world modern malware datasets (690 malware of 22 families). The experimental results reveal that despite using a reduced feature set, the IMCNN effectively detects malware with 99.36% test accuracy on unseen data for MalImg datasets and 92.11% for real-world malware. In addition, the proposed method is compared with several existing state-of-art malware detection models in terms of performance accuracy and found performing as the best. Experiments demonstrated that the proposed method is resilient to polymorphic code obfuscation used by the malware authors. 

IMCNN:Intelligent Malware Classification using Deep Convolution Neural Networks as Transfer learning and ensemble learning in honeypot enabled organizational network

The endangerment of online data breaches calls for exploring new and enhancing existing sneaky ways of clandestine communication to tailor those to match the present and futuristic technological and environmental needs, to which malicious intruders wouldn't have an answer. Cryptography and Steganography are the two distinct techniques that, for long, have remained priority choices for hiding vital information from the unauthorized. But the visibility of the encrypted contents makes these vulnerable to attack. Also, the recent legislative protection agreed to law enforcement authorities in Australia to sneak into pre-shared cryptographic secret keys (PSKs) shall have a devastating impact on the privacy of the people. Hence, the need of the hour is to veil in the encrypted data underneath the cover of Steganography, whose sole intent is to hide the very existence of information. This research endeavor enhances one of the most famous images Steganography technique called the Least Significant Bit (LSB) Steganography, from the security and information-theoretic standpoint by taking a known-cover and known-message attack scenario. The explicit proclamation of this research endeavor is that the security of LSB Steganography lies in inducing uncertainty at the time of bit embedding process. The test results rendered by the proposed methodology confers on the non-detectability and imperceptibility of the confidential information along with its strong resistance against LSB Steganalysis techniques.

/pdf/prudently-secure-information-theoretic-lsb-steganography-for-16413mbr7t.pdf

Prudently Secure Information Theoretic LSB Steganography for Digital Grayscale Images

Malware detection is a complex problem. The commonly used signature-based technique cannot detect unknown or zero-day malware. Traditional machine learning-based methods can identify unknown malicious programs but require high domain expertise for feature engineering. This research study presents a new method of malware detection using the stacking of static opcode and bytecode features. At first, bytecode and opcode sequences of executables files are extracted using a disassembler. After that, an NLP technique, TF-IDF, is employed to vectorize the extracted feature map. The extracted features as opcodes and bytecodes are concatenated to combine a single feature map, which serves as input to train machine learning classifiers: SVM, k-NN, and Random Forest (RF). The RF classifier obtained the best accuracy, precision, recall, and F-score results as 98.47%, 98.60%,98.46%, and 98.47% even using small data counts of bench-marked Microsoft BIG data set. The accuracy of the best model is comparatively better than the accuracy of using bytecodes and opcodes as individual feature vectors. The empirical results signify that the proposed method is helpful to the security industries with the benefit of being less dependent on manual feature engineering and can reduce the burden on virus databases by handling large-scale malware. 

Machine learning-based malware detection using stacking of opcodes and bytecode sequences

In the Internet ecosystem, URLs (Uniform Resource Locators) are widely used to propagate malicious infections through spam, spear-phishing, drive-by-download exploitation, malware embedding, etc. The blacklisting, pattern analysis, and signature-matching approaches are widely used to detect such threats. However, these techniques are very effective in detecting known types of malicious URLs but unable to detect new types of attacks launched by the malicious URLs. Moreover, traditional machine learning-based malicious URL detection is highly dependent upon manual feature engineering, which is a costly approach. , This research study proposes deep learning-based malicious URL detection with less dependent on manual feature engineering to overcome the above-mentioned challenges. The Natural Language Processing (NLP) technique, i.e., Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer, along with the N-gram parameter, is used for extracting the features. Afterward, two feature selection techniques as Embedded methods are used - Logistic Regression with L1 (Lasso) penalty and Random Forest Selection. Neural network models such as Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Convolution Neural Networks (CNN) are constructed to detect malicious URLs. The experiment results demonstrate that the DNN model gives the best performance results as 96.95% accuracy, 99% precision, 100% recall, and 99% F1-score with logistic regression along with the L1 (Lasso) penalty as a feature selection method. Finally, this study compares state-of-art implemented deep learning methods for malicious URL detection. 

Deep learning methods for malicious URL detection using embedding techniques as Logistic Regression with Lasso penalty and Random Forest

The evolving of advance cyber threats requires the cyber security specialist and system analyst to detect, analyse and timely react against such kind of cyber attacks. In real practical scenario, the timely dissemination of attack information is a challenge and that is not possible without cyber threat intelligence with inclusion of deep analysis of attack features and attack contextual information. In this paper, automated proactive approach for cyber threat intelligence generation is presented integrated with standard data sharing formats that can act as attack indicator for the security defence mechanism put in place in an organization such as SIEM. The strength of Honeypot-based approaches for cyber threat intelligence is proven with well-defined use cases. The capabilities of Honeypots to detect zero-day attacks can be benefited if and only if the attack events are timely digested by the security solutions and that is only possible by sharing the attack events in standard data sharing languages. The developed system is fully automated that include captured attack data is processed by various automated analysis engines, augmenting the contextual information and applying deep learning models for later threat prediction. Finally, we propose a system design incorporating deep learning neural network-based cyber threat intelligence generation for cyber threat prediction. To achieve all these, cluster of VM Honeypots are deployed in a public IP4 network.

Automated Cyber Threat Intelligence Generation from Honeypot Data

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Automated Cyber Threat Intelligence Generation from Honeypot Data