Context: Recently, research community of certain domain showing their eagerness towards the use of social media networks to gain constructive knowledge in decision making and automation, such as aid to perform software development activities, crypto-currencies usage, network community detection and recommendation and so on. Recently, besides other domains of eHealth, the use of social media and big data analytics has become hot topic to predict the patient of mental illness involved in either depression, schizophrenia, eating disorders, anxiety or addictive behaviors. Problem: Traditional methods either need enough historic data or to keep the regular monitoring on patient activities for identification of a patient associated with a mental illness disease. Method: In order to address this issue, we propose a methodology to classify the patients associated with chronic mental illness diseases (i.e. Anxiety, Depression, Bipolar, and ADHD (Attention Deficit Hyperactivity Disorder) based on the data extracted from the Reddit, a wellknown network community platform. The proposed method is employed through Co-training (type of semisupervised learning approach) technique by incorporating the discriminative power of widely used classifiers namely Random Forrest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). We used Reddit API to download posts and top five associated comments for construction of a feature space. Results: The experimental results indicate the effectiveness of Co-training based classification rather than the state of the art classifiers by a margin of 3% on average in par with every state of art technique. In future, the proposed method could be employed to investigate any classification problem of any domain by extracting date from the social media.

A number of techniques for securing plaintext, images and video frames have been developed in cryptography using jointly DNA computing and Chaos Theory. With the advancement of DNA/quantum computing, the threats of security breaches to information have an increasing possibility. In this paper, we propose a symmetric encryption algorithm for color images by extending the current encryption/decryption techniques. Our encryption algorithm is based on three chaotic systems (PWLCM, Lorenz and 4D Lorenz-type), a Secure Hash Algorithm, a scrambler, a chaotic generator and DNA sequence based Linear Feedback Shift Register. We introduce multilevel security to increase the degree of diffusion and confusion. Through experiments, we present security analysis for key irreproducibility and sensitivity, Gray Level Co-occurrence Matrix based analysis, maximum deviation, irregular deviation, entropy, histogram, variance and correlation, number of pixel change rate, unified average cipher intensity, known/chosenplaintext attacks, mean absolute error, robustness against noises of various types using PSNR and occlusion attacks. It is demonstrated that mostly our proposed encryption algorithm has enhanced performance as compared to contemporary works in information security, while comparable in other cases. INDEX TERMS Bit scrambling, chaotic generator, DNA sequence based linear feedback shift register, DNA encoding, hypechaos, secure hash algorithm.

Distributed Denial of Service (DDoS) attacks have caused significant disruptions in the operations of Internet-based services. These DDoS attacks use large scale botnets, which often exploit millions of compromised Internet of Things (IoT) devices worldwide. IoT devices are traditionally less secure and are easy to be exploited. The extent of these exploitations has increased after the publication of the Mirai botnet source code on GitHub that provided a foundation for the attackers to develop and launch Mirai botnet variants. The Internet Engineering Task Force (IETF) proposed RFC 8520 Manufacturer Usage Description (MUD) so that an IoT device can convey to the network the level of network access it requires to accomplish its standard functionality. Though MUD is a promising effort, there is a need to evaluate its effectiveness, identify its limitations, and enhance its architecture to overcome its weakness and improve its efficiency. The latest Mirai variant malware is exploiting vulnerabilities of Internet of Things devices [1]. As MUD does not consider identifying and patching vulnerabilities present in the device before the issuance of the MUD profile, a device can be compromised even in the presence of the Manufacturer Usage Description profile by exploiting either the configuration vulnerabilities or firmware vulnerabilities present in the device [2]. This paper presents an evaluation study of the Manufacturer Usage Description (MUD), identifies its weaknesses, and proposed enhancements in its architecture. This research proposed a mechanism for identifying and eliminating the configuration vulnerabilities before creating the MUD profile for a device to minimize the attack surface. This research adopts the OWASP firmware testing methodology [3] for discovering vulnerabilities in the firmware of WiFi home routers. The device is allowed to request the MUD profile only if the identified firmware vulnerabilities are low. The identified firmware vulnerabilities are patched in case the score of the identified firmware vulnerabilities is moderate or high. The device is allowed to request the MUD profile after the vulnerabilities are patched. The firmware vulnerabilities are shared with other peers using blockchain smart contracts. There is a possibility that the MUD URL might be pointing to a corrupted or malicious MUD profile hosted at the attacker file server due to the absence of an authentication mechanism in the MUD process. This research also proposed an authentication mechanism for device MUD profile, MUD file generator, and MUD file server. Implementation results show that proposed enhancements improve the security services provided by the Manufacturer Usage Description (MUD).

Complicated stages of diabetes are the major cause of Diabetic Retinopathy (DR) and no symptoms appear at the initial stage of DR. At the early stage diagnosis of DR, screening and treatment may reduce vision harm. In this work, an automated technique is applied for detection and classification of DR. A local contrast enhancement method is used on grayscale images to enhance the region of interest. An adaptive threshold method with mathematical morphology is used for the accurate lesions region segmentation. After that, the geometrical and statistical features are fused for better classification. The proposed method is validated on DIARETDB1, E‐ophtha, Messidor, and local data sets with different metrics such as area under the curve (AUC) and accuracy (ACC).

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