Biblio

Securing communication and information is known as cryptography. To convert messages from plain text to cipher text and the other way around. It is the process of protecting the data and sending it to the right audience so they can understand and process it. Hence, unauthorized access is avoided. This work suggests leveraging DNA technology for encrypt and decrypt the data. The main aim of utilizing the AES in this stage will transform ASCII code to hexadecimal to binary coded form and generate DNA. The message is encrypted with a random key. Shared key used for encrypt and decrypt the data. The encrypted data will be disguised as an image using steganography. To protect our data from hijackers, assailants, and muggers, it is frequently employed in institutions, banking, etc.

In last few years, Proxy Re-Encryption has been used for forwarding the encrypted message to the user, these users are the one who has not been a part of encryption. In the past several scheme were developed in order to provide the efficient and secure proxy re-encryption. However, these methodology mainly focused on features like maximum key privacy, minimal trust proxy and others. In such cases the efficiency and security was mainly ignored. Hence, in order to provide the efficient and secure proxy re-encryption, we proposed an algorithm named as MLBC (Modified Lattice Based Cryptography) is proposed. Our method is based on the PKE (Public Key Encryption) and it provides more efficiency when compared to the other cryptography technique. Later in order to evaluate the algorithm simulation is done based on several parameter such as encryption time, proxy key generation time, Re-encryption time and Total computation time. Later, it is compared with the existing algorithm and the plotted graph clearly shows that our algorithm outperforms the existing algorithm.

Secured data transmission between one to many authorized users is achieved through Broadcast Encryption (BE). In BE, the source transmits encrypted data to multiple registered users who already have their decrypting keys. The Untrustworthy users, known as Traitors, can give out their secret keys to a hacker to form a pirate decoding system to decrypt the original message on the sly. The process of detecting the traitors is known as Traitor Tracing in cryptography. This paper presents a new Black Box Tracing method that is fully collusion resistant and it is designated as Traitor Tracing in Broadcast Encryption using Vector Keys (TTBE-VK). The proposed method uses integer vectors in the finite field Zp as encryption/decryption/tracing keys, reducing the computational cost compared to the existing methods.

In this article, we review previous work on biometric security under a recent framework proposed in the field of adversarial machine learning. This allows us to highlight novel insights on the security of biometric systems when operating in the presence of intelligent and adaptive attackers that manipulate data to compromise normal system operation. We show how this framework enables the categorization of known and novel vulnerabilities of biometric recognition systems, along with the corresponding attacks, countermeasures, and defense mechanisms. We report two application examples, respectively showing how to fabricate a more effective face spoofing attack, and how to counter an attack that exploits an unknown vulnerability of an adaptive face-recognition system to compromise its face templates.

Selective encryption designates a technique that aims at scrambling a message content while preserving its syntax. Such an approach allows encryption to be transparent towards middle-box and/or end user devices, and to easily fit within existing pipelines. In this paper, we propose to apply this property to a real-time diffusion scenario - or broadcast - over a RTP session. The main challenge of such problematic is the preservation of the synchronization between encryption and decryption. Our solution is based on the Advanced Encryption Standard in counter mode which has been modified to fit our auto-synchronization requirement. Setting up the proposed synchronization scheme does not induce any latency, and requires no additional bandwidth in the RTP session (no additional information is sent). Moreover, its parallel structure allows to start decryption on any given frame of the video while leaving a lot of room for further optimization purposes.

Most network traffic analysis applications are designed to discover malicious activity by only relying on high-level flow-based message properties. However, to detect security breaches that are specifically designed to target one network (e.g., Advanced Persistent Threats), deep packet inspection and anomaly detection are indispensible. In this paper, we focus on how we can support experts in discovering whether anomalies at message level imply a security risk at network level. In SNAPS (Semantic Network traffic Analysis through Projection and Selection), we provide a bottom-up pixel-oriented approach for network traffic analysis where the expert starts with low-level anomalies and iteratively gains insight in higher level events through the creation of multiple selections of interest in parallel. The tight integration between visualization and machine learning enables the expert to iteratively refine anomaly scores, making the approach suitable for both post-traffic analysis and online monitoring tasks. To illustrate the effectiveness of this approach, we present example explorations on two real-world data sets for the detection and understanding of potential Advanced Persistent Threats in progress.

More and more advanced persistent threat attacks has happened since 2009. This kind of attacks usually use more than one zero-day exploit to achieve its goal. Most of the times, the target computer will execute malicious program after the user open an infected compound document. The original detection method becomes inefficient as the attackers using a zero-day exploit to structure these compound documents. Inspired by the detection method based on structural entropy, we apply wavelet analysis to malicious document detection system. In our research, we use wavelet analysis to extract features from the raw data. These features will be used todetect whether the compound document was embed malicious code.

Models of visual attention postulate the existence of a saliency map whose function is to guide attention and gaze to the most conspicuous regions in a visual scene. Although cortical representations of saliency have been reported, there is mounting evidence for a subcortical saliency mechanism, which pre-dates the evolution of neocortex. Here, we conduct a strong test of the saliency hypothesis by comparing the output of a well-established computational saliency model with the activation of neurons in the primate superior colliculus (SC), a midbrain structure associated with attention and gaze, while monkeys watched video of natural scenes. We find that the activity of SC superficial visual-layer neurons (SCs), specifically, is well-predicted by the model. This saliency representation is unlikely to be inherited from fronto-parietal cortices, which do not project to SCs, but may be computed in SCs and relayed to other areas via tectothalamic pathways.

Cyber-Physical Systems (CPS) consist of embedded computers with sensing and actuation capability, and are integrated into and tightly coupled with a physical system. Because the physical and cyber components of the system are tightly coupled, cyber-security is important for ensuring the system functions properly and safely. However, the effects of a cyberattack on the whole system may be difficult to determine, analyze, and therefore detect and mitigate. This work presents a model based software development framework integrated with a hardware-in-the-loop (HIL) testbed for rapidly deploying CPS attack experiments. The framework provides the ability to emulate low level attacks and obtain platform specific performance measurements that are difficult to obtain in a traditional simulation environment. The framework improves the cybersecurity design process which can become more informed and customized to the production environment of a CPS. The developed framework is illustrated with a case study of a railway transportation system.

The growing use of ICT in complex and critical infrastructures in the energy- and maritime domain leads to the development of system-of-system engineering efforts especially for system architectures. Such efforts need to integrate a standardized elicitation and harmonization of requirements between different interoperability perspectives and with domain-specific aspects. According to this, the paper adapts the existing architecture management approaches SGAM and MAF for a methodology to structure the identification and harmonization of requirements considering domain specific characteristics and interoperability.

Hash based biometric template protection schemes (BTPS), such as fuzzy commitment, fuzzy vault, and secure sketch, address the privacy leakage concern on the plain biometric template storage in a database through using cryptographic hash calculation for template verification. However, cryptographic hashes have only computational security whose being cracked shall leak the biometric feature in these BTPS; and furthermore, existing BTPS are rarely able to detect during a verification process whether a probe template has been leaked from the database or not (i.e., being used by an imposter or a genuine user). In this paper we tailor the "honeywords" idea, which was proposed to detect the hashed password cracking, to enable the detectability of biometric template database leakage. However, unlike passwords, biometric features encoded in a template cannot be renewed after being cracked and thus not straightforwardly able to be protected by the honeyword idea. To enable the honeyword idea on biometrics, diversifiability (and thus renewability) is required on the biometric features. We propose to use BTPS for his purpose in this paper and present a machine learning based protected template generation protocol to ensure the best anonymity of the generated sugar template (from a user's genuine biometric feature) among other honey ones (from synthesized biometric features).