The Internet of Things (IoT) has radically reformed various sectors and industries, enabling unprecedented levels of connectivity and automation. However, the surge in the number of IoT devices has ...also widened the attack surface, rendering IoT networks potentially susceptible to a plethora of security risks. Addressing the critical challenge of enhancing security in IoT networks is of utmost importance. Moreover, there is a considerable lack of datasets designed exclusively for IoT applications. To bridge this gap, a customized dataset that accurately mimics real-world IoT scenarios impacted by four different types of attacks—blackhole, sinkhole, flooding, and version number attacks was generated using the Contiki-OS Cooja Simulator in this study. The resulting dataset is then consequently employed to evaluate the efficacy of several metaheuristic algorithms, in conjunction with Convolutional Neural Network (CNN) for IoT networks. •The proposed study's goal is to identify optimal hyperparameters for CNNs, ensuring their peak performance in intrusion detection tasks.•This study not only intensifies our comprehension of IoT network security but also provides practical guidance for implementation of the robust security measures in real-world IoT applications.
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Sharing medical images securely is very important towards keeping patients' data confidential. In this paper we propose MAN-C: a Masked Autoencoder Neural Cryptography based encryption scheme for ...sharing medical images. The proposed technique builds upon recently proposed masked autoencoders. In the original paper, the masked autoencoders are used as scalable self-supervised learners for computer vision which reconstruct portions of originally patched images. Here, the facility to obfuscate portions of input image and the ability to reconstruct original images is used an encryption-decryption scheme. In the final form, masked autoencoders are combined with neural cryptography consisting of a tree parity machine and Shamir Scheme for secret image sharing. The proposed technique MAN-C helps to recover the loss in image due to noise during secret sharing of image.•Uses recently proposed masked autoencoders, originally designed as scalable self-supervised learners for computer vision, in an encryption-decryption setup.•Combines autoencoders with neural cryptography - the advantage our proposed approach offers over existing technique is that (i) Neural cryptography is a new type of public key cryptography that is not based on number theory, requires less computing time and memory and is non-deterministic in nature, (ii) masked auto-encoders provide additional level of obfuscation through their deep learning architecture.•The proposed scheme was evaluated on dataset consisting of CT scans made public by The Cancer Imaging Archive (TCIA). The proposed method produces better RMSE values between the input the encrypted image and comparable correlation values between the input and the output image with respect to the existing techniques.
Neural cryptography is a technique that uses neural networks for secure data encryption. Cryptoanalysis, on the other hand, deals with analyzing and decrypting ciphers, codes, and encrypted text ...without using a real key. Chosen-plaintext cryptanalysis is a subfield of cryptanalysis where both plain text and ciphertext are available and the goal is either to find the encryption technique, the encryption key, or both. This study addresses chosen plaintext cryptanalysis within public key cryptography, to categorize topics of encrypted text. Using a fixed encryption technique and key, the focus was placed on creating a framework that identifies the topic associated with ciphertext, using diverse plaintexts and their corresponding cipher texts. To our knowledge, this is the first time that chosen-plaintext cryptanalysis has been discussed in the context of topic modeling. The paper used deep learning techniques such as CNNs, GRUs, and LSTMs to process sequential data. The proposed framework achieved up to 67% precision, 99% recall, 80% F1-score, and 71% AUPR on a dataset, showcasing promising results and opening avenues for further research in this cryptanalysis subarea.
The Internet of Things is a disruptive technology that has changed the face of many industries. On the contrary, the unpresidential growth of IoT has also introduced many cybersecurity challenges. An ...adversary can exploit a zero-day vulnerability in an IoT to create a botnet of things. An IoT botnet is a group of compromised Internet of Things weaponized to launch cyber attacks. Machine learning and other artificial intelligence techniques are being used to combat the wide range of cyberattacks on the Internet of Things. However, in order to overcome challenges such as early diagnosis, real-time monitoring, and adaptability to different threats, these Machine Learning approaches still require significant feature engineering. In order to identify IoT botnet assaults early on, this paper suggests using a customized convolutional neural network (CCNN) model. The four phases of the model are feature extraction, attack detection, mitigation, and pre-processing. The class imbalance has been improved and the input data pre-processed using the Enhanced Synthetic minority oversampling approach. Furthermore, flow-based features, raw attributes, mean, median, standard deviation, improved entropy, mutual information, and other statistical features are retrieved and regarded as part of the feature set. The CCNN model provides the detection or classification output during the attack detection phase, which operates depending on the features derived from the input data. Additionally, a mitigation process based on entropy has been suggested to locate the attacker node, aiding in the removal of the susceptible attacker IoT node from the network. The compromised IoT node is removed through the entropy-based mitigation method, which establishes the entropy formulation based on the node's activity. The suggested model's specificity is 97.09%, compared to the minimal specificity reached by conventional techniques, including CNN (83.58%), RNN (86.17%), RF (60.46%), SVM (78.50%), and DNN (84.12%) and SMIE (88.42%), respectively.
Recently security plays a crucial part throughout the communication framework. In the most recent decade, organizations have endeavored to move from a paper-concentrated environment to a sans paper ...environment where data security is one of the essential issues, and any weakness in this respect can have crushing impacts. Security implies the assurance of the data from any unauthorized access or manipulation through spying or scientific or probabilistic calculations and different strategies. For the security of information, researchers are doing more work on ID-based Cryptography (IDC). However, it has two limitations: one suffers from an inherent key escrow problem, and the second is how to revoke the misbehavior/compromised user. Tseng and Tsai have proposed a novel Revocable ID-Based Encryption (RIBE). Yet, they have not considered the key escrow problem. This paper proposed an approach in which secure key issuance is done only for non-revocable ID. This will reduce the key escrow problem as multiple entities rather than one Private Key Generator (PKG) issue the key. It will reduce the possibility of compromised PKG as the private key is cooperatively issued by PKG and multiple Key Privacy Authorities (KPAs).
The botnet have developed into a severe risk to Internet of Things (IoT) systems as a result of manufacturers ‘insufficient security policies and end users’ lack of security awareness. By default, ...several ports are open and user credentials are left unmodified. ML and DL strategies have been suggested in numerous latest research for identifying and categorising botnet assaults in the IoT context, but still, it has a few issues like high error susceptibility, working only with a large amount of data, poor quality, and data acquisition. This research provided use of a brand-new IoT botnet detector built on an improved hybrid classifier. The proposed work's main components are "pre-processing, feature extraction, feature selection, and attack detection." Following that, the improved Information Gain (IIG) model is used to choose the most reliable characteristics from the received information. To detect an attack, a hybrid classifier is utilized which can be constructed by integrating the optimized Bi-GRU with the Recurrent Neural Network (RNN). To increase the detection accuracy of IoT-BOTNETS, a novel hybrid optimization approach called SMIE (Slime Mould with Immunity Evolution) is created by conceptually integrating two conventional optimization modes: Coronavirus herd immunity optimizer (CHIO) and the Slime mould algorithm. The final output of the hybrid classifier displays the presence or absence of IoT-BOTNET attacks. The projected model's accuracy is 97%, which is 22.6%, 18.5%, 27.8%, 22.6%, and 24.8% higher than the previous models like GWO+ HC, SSO+ HC, WOA+ HC, SMA+ HC, and CHIO+ HC, respectively.
Internet of Things (IoT) involves wide-ranging devices connected through the Internet with an aim to enable coherent communication amongst them without human intervention to realize profuse smart ...applications which inherently makes our life a lot easier and furthermore productive. These connected devices continuously sense and gather information from surroundings, thereby producing an immense amount of data that cater for big data analytics. In the current era, number of smart devices are increasing rapidly due to the magnificent features they offer. Moreover, public access to the Internet makes the system even more vulnerable to intrusions. Catastrophically, this has fascinated numerous cybercriminals who have turned the IoT ecosystem into a hotbed of illicit activities. Thereupon, implication of Intrusion Detection System (IDS) in IoT is apparent. The literature suggests a number of IDS to address intrusions/attacks in the discipline of IoT. In the current paper, besides Systematic Literature Review of the IDS for IoT environment, a deep learning model with aquila optimization is proposed to predict anomaly using IoTID20, UNSW-NB15–1 and UNSW_2018_IoT_Botnet_Full5pc_4 datasets. The hybrid model that we have developed, uses a combined network structure of convolutional neural network and aquila optimization algorithm. In all of the studies that were carried out, the swarm intelligence-driven deep learning strategy outperformed other, comparable approaches. Based on current findings, it is reasonable to draw the conclusion that the suggested technique offers an efficient method for early anomaly detection and contributes to viable control of anomaly in the IoT environment.
