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/chosen-plaintext 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.

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 well-known network community platform. The proposed method is employed through Co-training (type of semi-supervised 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.

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).

Intelligent and efficient image retrieval from versatile image datasets is an inevitable requirement of the current era. Primitive image signatures are vital to reflect the visual attributes for content based image retrieval (CBIR). Algorithmically descriptive and well identified visual contents form the image signatures to correctly index and retrieve similar results. Hence feature vectors should contain ample image information with color, shape, objects, spatial information perspectives to distinguish image category as a qualifying candidate. This contribution presents a novel features detector by locating the interest points by applying non-maximum suppression to productive sum of derivative of pixels computed from differential of corner scores. The interest points are described by applying scale space interpolation to scale space division produced from Hessian blob detector resulted after Gaussian smoothing. The computed shape and object information is fused with color features extracted from the spatially arranged L2 normalized coefficients. High variance coefficients are selected for object based feature vectors to reduce the massive data which in fuse form transformed to bag-of-words (BoW) for efficient retrieval and ranking. To check the competitiveness of the presented approach it is experimented on nine well-known image datasets Caltech-101, ImageNet, Corel-10000, 17-Flowers, Columbia object image library (COIL), Corel-1000, Caltech-256, tropical fruits and Amsterdam library of textures (ALOT) belong to shape, color, texture, and spatial & complex objects categories. Extensive experimentation is conducted for seven benchmark descriptors including maximally stable extremal region (MSER), speeded up robust features (SURF), difference of Gaussian (DoG), red green blue local binary pattern (RGBLBP), histogram of oriented gradients (HOG), scale invariant feature transform (SIFT), and local binary pattern (LBP). Remarkable outcomes reported that the presented technique has significant precision rates, recall rates, average retrieval precision & recall, mean average precision & recall rates for many image semantic groups of the challenging datasets. Results comparison is presented with research techniques and reported improved results.

DDoS (distributed denial of service) attacks have drastically effected the functioning of Internet‐based services in recent years. Following the release of the Mirai botnet source code on GitHub, the scope of these exploitations has grown. The attackers have been able to construct and launch variations of the Mirai botnet thanks to the open‐sourcing of the Mirai code. These variants make the signature‐based detection of these attacks challenging. Moreover, DDoS attacks are typically detected and mitigated reactively, making DDoS mitigation solutions very expensive. This paper presents a proactive IoT botnet detection system that detects the anomalies in the behavior of the IoT device and mitigates the DDoS botnet exploitation at the source end, which makes our proposal a low‐cost solution. Further, this paper uses a collaborative trust relationship‐based threat intelligence‐sharing mechanism to prevent other IoT devices from being compromised by the detected botnet. The researchers have evaluated the collaborative threat intelligence sharing mechanism using Ethereum Virtual Machine and Hyperledger. The performance of our proposed system can detect 97% of the Mirai botnet attack activities. Furthermore, our collaborative threat intelligence sharing mechanism based on the Ethereum Virtual Machine showed more scalability.

Key schedule algorithms play an important role in modern encryption algorithms, and their security is as crucial as the security of the encryption algorithms themselves. Many studies have been performed on the cryptographic strength evaluation of the encryption algorithms; however, strength evaluation of the key schedule algorithms often obtains less attention that can lead towards the possible loophole in the overall encryption process. In this paper, a criterion is proposed to evaluate the cryptographic strength of the key schedule algorithms. This criterion includes different methods of data generation from subkeys and a suitable set of statistical tests. The statistical tests are used to explore the cryptographic properties such as diffusion, confusion, independence, and randomness in the subkeys generated by the key schedule algorithm. The proposed criterion has been applied to some of the key schedule algorithms of different block ciphers. The results confirm that the proposed criterion can effectively differentiate between strong- and weak-key schedule algorithms.