Biblio

Software-defined networking (SDN) has become a popular technology, being adopted in operational networks and being a hot research topic. Many network testbeds today are used to test new research solutions and would benefit from offering SDN experimentation capabilities to their users. Yet, exposing SDN to experimenters is challenging because experiments must be isolated from each other and limited switch resources must be shared fairly. We outline three different approaches for exposing SDN to experimenters while achieving isolation and fair sharing goals. These solutions use software implementation, shared hardware switches and smart network interface cards to implement SDN in testbeds. These approaches are under development on two operational SDN testbeds: the DeterLab at USC/ISI/Berkeley and the NCL testbed at the National University of Singapore.

Cognitive Radio (CR) is an attractive solution in mobile communication for solving the spectrum scarcity problem. Moreover, security concerns are not yet fully satisfied. This article focuses on attacks such as the Primary user emulation attack (PUE) and the jammer attack. These attacks create anomalous spectrum access thereby disturbing the dynamic spectrum usage in the CR networks. A framework based on cross-layer has been designed effectively to determine these attacks in the CR networks. First, each secondary user will sense the spectrum in the physical layer and construct a feature space. Using the extracted features, the clusters are formed effectively for each user. In the network layer, multipath routing is employed to discover the routes for the secondary user. If the node in the path identifies any spectrum shortage, it will verify that location with the help of constructed cluster. If the node does not belong to any of the clusters, then it will be identified as the attacker node. Simulation results and security analysis are performed using the NS2 simulations, which show improvement in detection of the attacks, decrease in the detection delay, and less route dis-connectivity. The proposed cross-layer framework identifies the anomalous spectrum access attack effectively.

The impact of digital gadgets is enormous in the current Internet world because of the easy accessibility, flexibility and time-saving benefits for the consumers. The number of computer users is increasing every year. Meanwhile, the time spent and the computers also increased. Computer users browse the internet for various information gathering and stay on the internet for a long time without control. Nowadays working people from home also spend time with the smart devices, computers, and laptops, for a longer duration to complete professional work, personal work etc. the proposed study focused on deriving the impact factors of Smartphones by analyzing the keystroke dynamics Based on the usage pattern of keystrokes the system evaluates the stress level detection using machine learning techniques. In the proposed study keyboard users are intended for testing purposes. Volunteers of 200 members are collectively involved in generating the test dataset. They are allowed to sit for a certain frame of time to use the laptop in the meanwhile the keystroke of the Mouse and keyboard are recorded. The system reads the dataset and trains the model using the Dynamic Cat-Boost algorithm (DCB), which acts as the classification model. The evaluation metrics are framed by calculating Euclidean distance (ED), Manhattan Distance (MahD), Mahalanobis distance (MD) etc. Quantitative measures of DCB are framed through Accuracy, precision and F1Score.

Hardware Trojans are malicious modules causing vulnerabilities in designs. Secured hardware designs are desirable in almost all applications. So, it is important to make a trustworthy design that actually exposes malfunctions when a Trojan is present in it. Recently, ring oscillator based detection methods are gaining prominence as they help in detecting Trojans accurately. In this work, a non-destructive method of Trojan detection by modifying the circuit paths into oscillators is proposed. The change in frequencies of ring oscillators upon taking the process corners into account, indicate the presence of Trojans. Since Transient Effect Ring Oscillators (TERO) are also emerging as a good alternative to classical ring oscillators in Trojan detection, an effort is made to analyze the detection capability. Evaluation is done using ISCAS'85 benchmark circuits. Comparison is done in terms of frequency and findings indicate that TERO based Trojan detection is precise. Evaluation is carried out using Xilinx Vivado and ModelSim platforms.

With 3.78 billion social media users worldwide in 2021 (48% of the human population), almost 3 billion images are shared daily. At the same time, a consistent evolution of smartphone cameras has led to a photography explosion with 85% of all new pictures being captured using smartphones. However, lately, there has been an increased discussion of privacy concerns when a person being photographed is unaware of the picture being taken or has reservations about the same being shared. These privacy violations are amplified for people with disabilities, who may find it challenging to raise dissent even if they are aware. Such unauthorized image captures may also be misused to gain sympathy by third-party organizations, leading to a privacy breach. Privacy for people with disabilities has so far received comparatively less attention from the AI community. This motivates us to work towards a solution to generate privacy-conscious cues for raising awareness in smartphone users of any sensitivity in their viewfinder content. To this end, we introduce PrivPAS (A real time Privacy-Preserving AI System) a novel framework to identify sensitive content. Additionally, we curate and annotate a dataset to identify and localize accessibility markers and classify whether an image is sensitive to a featured subject with a disability. We demonstrate that the proposed lightweight architecture, with a memory footprint of a mere 8.49MB, achieves a high mAP of 89.52% on resource-constrained devices. Furthermore, our pipeline, trained on face anonymized data. achieves an F1-score of 73.1%.

Classification of Misbehaving Nodes in wireless mobile adhoc networks (MANET) by applying machine learning techniques is an attempt to enhance security by detecting the presence of malicious nodes. MANETs are prone to many security vulnerabilities due to its significant features. The paper compares two machine learning techniques namely Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) and finds out the best technique to detect the misbehaving nodes. This paper is simulated with an on-demand routing protocol in NS2.35 and the results can be compared using parameters like packet Delivery Ratio (PDR), End-To-End delay, Average Throughput.

Smart Grid is used to improve the accuracy of the grid network query. Though it gives the accuracy, it has the data privacy issues. It is a big challenge to solve the privacy issue in the smart grid. We need secured algorithms to protect the data in the smart grid, since the data is very important. This paper explains about the k-anonymous algorithm and analyzes the enhanced L-diversity algorithm for data privacy and security. The algorithm can protect the data in the smart grid is proven by the experiments.

Threat Actor Attribution is the task of identifying an attacker responsible for an attack. This often requires expert analysis and involves a lot of time. There had been attempts to detect a threat actor using machine learning techniques that use information obtained from the analysis of malware samples. These techniques will only be able to identify the attack, and it is trivial to guess the attacker because various attackers may adopt an attack method. A state-of-the-art method performs attribution of threat actors from text reports using Machine Learning and NLP techniques using Threat Intelligence reports. We use the same set of Threat Reports of Advanced Persistent Threats (APT). In this paper, we propose a Deep Learning architecture to attribute Threat actors based on threat reports obtained from various Threat Intelligence sources. Our work uses Neural Networks to perform the task of attribution and show that our method makes the attribution more accurate than other techniques and state-of-the-art methods.

Government division may be a crucial portion of the nation's economy. Security of government inquire about substance from all sorts of dangers is basic not as it were for trade coherence but too for supporting the economy of the country as a entirety. With the digitization of conventional records, government substances experience troublesome issues, such as government capacity and access. Research office spend significant time questioning the specified information when getting to Government investigate substance subtle elements, but the gotten information are not fundamentally rectify, and get to is some of the time limited. On this premise, this think about proposes a investigate substance which utilize ciphertext-based encryption to guarantee information privacy and get to control of record subtle elements. The investigate head may scramble the put away data for accomplishing get to control and keeping information secure. In this manner AES Rijndael calculation is utilized for encryption. This guarantees security for the data and empowers Protection.

Security plays a major role in data transmission and reception. Providing high security is indispensable in communication systems. The RSA (Rivest-Shamir-Adleman) cryptosystem is used widely in cryptographic applications as it offers highly secured transmission. RSA cryptosystem uses Montgomery multipliers and it involves modular exponentiation process which is attained by performing repeated modular-multiplications. This leads to high latency and owing to improve the speed of multiplier, highly efficient modular multiplication methodology needs to be applied. In the conventional methodology, Carry Save Adder (CSA) is used in the multiplication and it consumes more area and it has larger delay, but in the suggested methodology, the Reverse Carry Propagate (RCP) adder is used in the place of CSA adder and the obtained output shows promising results in terms of area and latency. The simulation is done with Xilinx ISE design suite. The proposed multiplier can be used effectively in signal processing, image processing and security based applications.

Cloud computing has revolutionized the way how users store, process, and use data. It has evolved over the years to put forward various sophisticated models that offer enhanced performance. The growth of electronic data stored in the Cloud has made it crucial to access data without data loss and leakage. Security threats still prevent significant corporations that use sensitive data to employ cloud computing to handle their data. Traditional cryptographic techniques like DES, AES, etc... provide data confidentiality but are computationally complex. To overcome such complexities, a unique field of cryptography known as DNA Cryptography came into existence. DNA cryptography is a new field of cryptography that utilizes the chemical properties of DNA for secure data encoding. DNA cryptographic algorithms are much faster than traditional cryptographic methods and can bring about greater security with lesser computational costs. In this paper, we have proposed an enhanced DNA cryptosystem involving operations such as encryption, encoding table generation, and decryption based on the chemical properties of DNA. The performance analysis has proven that the proposed DNA cryptosystem is secure and efficient in Cloud data storage.

Among most of the cyber attacks that occured, the most drastic are advanced persistent threats. APTs are differ from other attacks as they have multiple phases, often silent for long period of time and launched by adamant, well-funded opponents. These targeted attacks mainly concentrated on government agencies and organizations in industries, as are those involved in international trade and having sensitive data. APTs escape from detection by antivirus solutions, intrusion detection and intrusion prevention systems and firewalls. In this paper we proposes a classification model having 99.8% accuracy, for the detection of APT.

Sampling multiband radar signals is an essential issue of multiband/multifunction radar. This paper proposes a multiband quadrature compressive sampling (MQCS) system to perform the sampling at sub-Landau rate. The MQCS system randomly projects the multiband signal into a compressive multiband one by modulating each subband signal with a low-pass signal and then samples the compressive multiband signal at Landau-rate with output of compressive measurements. The compressive inphase and quadrature (I/Q) components of each subband are extracted from the compressive measurements respectively and are exploited to recover the baseband I/Q components. As effective bandwidth of the compressive multiband signal is much less than that of the received multiband one, the sampling rate is much less than Landau rate of the received signal. Simulation results validate that the proposed MQCS system can effectively acquire and reconstruct the baseband I/Q components of the multiband signals.