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S R, Sivaramakrishnan, Mikovic, Jelena, Kannan, Pravein G., Mun Choon, Chan, Sklower, Keith.  2017.  Enabling SDN Experimentation in Network Testbeds. Proceedings of the ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization. :7–12.
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.
S, Arun, Prasad, Sanjana, Umamaheswari, G.  2022.  Clustering with Cross Layer Design against Spectrum Access Attack in Cognitive Radio Networks. 2022 2nd Asian Conference on Innovation in Technology (ASIANCON). :1–4.
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.
S, Bakkialakshmi V., Sudalaimuthu, T..  2022.  Dynamic Cat-Boost Enabled Keystroke Analysis for User Stress Level Detection. 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES). :556–560.
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.
S, Deepthi, R, Ramesh S., M, Nirmala Devi.  2021.  Hardware Trojan Detection using Ring Oscillator. 2021 6th International Conference on Communication and Electronics Systems (ICCES). :362–368.
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.
S, Harichandana B S, Agarwal, Vibhav, Ghosh, Sourav, Ramena, Gopi, Kumar, Sumit, Raja, Barath Raj Kandur.  2022.  PrivPAS: A real time Privacy-Preserving AI System and applied ethics. 2022 IEEE 16th International Conference on Semantic Computing (ICSC). :9—16.
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%.
S, Kanthimathi, Prathuri, Jhansi Rani.  2020.  Classification of Misbehaving nodes in MANETS using Machine Learning Techniques. 2020 2nd PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS). :1–2.
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.
S, Muthulakshmi, R, Chitra.  2021.  Enhanced Data Privacy Algorithm to Protect the Data in Smart Grid. 2021 Smart Technologies, Communication and Robotics (STCR). :1—4.
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.
S, Naveen, Puzis, Rami, Angappan, Kumaresan.  2020.  Deep Learning for Threat Actor Attribution from Threat Reports. 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP). :1–6.
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.
S, Sahana, Shankaraiah.  2020.  Securing Govt Research Content using QR Code Image. 2020 IEEE International Conference for Innovation in Technology (INOCON). :1—5.
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.
S, Srinitha., S, Niveda., S, Rangeetha., V, Kiruthika..  2021.  A High Speed Montgomery Multiplier Used in Security Applications. 2021 3rd International Conference on Signal Processing and Communication (ICPSC). :299–303.

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.

S, Sudersan, B, Sowmiya, V.S, Abhijith, M, Thangavel, P, Varalakshmi.  2021.  Enhanced DNA Cryptosystem for Secure Cloud Data Storage. 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC). :337—342.
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.
S. Chandran, Hrudya P, P. Poornachandran.  2015.  "An efficient classification model for detecting advanced persistent threat". 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). :2001-2009.

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.

S. Chen, F. Xi, Z. Liu, B. Bao.  2015.  "Quadrature compressive sampling of multiband radar signals at sub-Landau rate". 2015 IEEE International Conference on Digital Signal Processing (DSP). :234-238.

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.

S. Goyal, M. Ramaiya, D. Dubey.  2015.  "Improved Detection of 1-2-4 LSB Steganography and RSA Cryptography in Color and Grayscale Images". 2015 International Conference on Computational Intelligence and Communication Networks (CICN). :1120-1124.

Steganography is the art of the hidden data in such a way that it detection of hidden knowledge prevents. As the necessity of security and privacy increases, the need of the hiding secret data is ongoing. In this paper proposed an enhanced detection of the 1-2-4 LSB steganography and RSA cryptography in Gray Scale and Color images. For color images, we apply 1-2-4 LSB on component of the RGB, then encrypt information applying RSA technique. For Gray Images, we use LSB to then encrypt information and also detect edges of gray image. In the experimental outcomes, calculate PSNR and MSE. We calculate peak signal noise ratio for quality and brightness. This method makes sure that the information has been encrypted before hiding it into an input image. If in any case the cipher text got revealed from the input image, the middle person other than receiver can't access the information as it is in encrypted form.

S. Lohit, K. Kulkarni, P. Turaga, J. Wang, A. C. Sankaranarayanan.  2015.  "Reconstruction-free inference on compressive measurements". 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). :16-24.

Spatial-multiplexing cameras have emerged as a promising alternative to classical imaging devices, often enabling acquisition of `more for less'. One popular architecture for spatial multiplexing is the single-pixel camera (SPC), which acquires coded measurements of the scene with pseudo-random spatial masks. Significant theoretical developments over the past few years provide a means for reconstruction of the original imagery from coded measurements at sub-Nyquist sampling rates. Yet, accurate reconstruction generally requires high measurement rates and high signal-to-noise ratios. In this paper, we enquire if one can perform high-level visual inference problems (e.g. face recognition or action recognition) from compressive cameras without the need for image reconstruction. This is an interesting question since in many practical scenarios, our goals extend beyond image reconstruction. However, most inference tasks often require non-linear features and it is not clear how to extract such features directly from compressed measurements. In this paper, we show that one can extract nontrivial correlational features directly without reconstruction of the imagery. As a specific example, we consider the problem of face recognition beyond the visible spectrum e.g in the short-wave infra-red region (SWIR) - where pixels are expensive. We base our framework on smashed filters which suggests that inner-products between high-dimensional signals can be computed in the compressive domain to a high degree of accuracy. We collect a new face image dataset of 30 subjects, obtained using an SPC. Using face recognition as an example, we show that one can indeed perform reconstruction-free inference with a very small loss of accuracy at very high compression ratios of 100 and more.

S. Majumdar, A. Maiti, A. Nath.  2015.  "New Secured Steganography Algorithm Using Encrypted Secret Message inside QRTM Code: System Implemented in Android Phone". 2015 International Conference on Computational Intelligence and Communication Networks (CICN). :1130-1134.

Steganography is a method of hiding information, whereas the goal of cryptography is to make data unreadable. Both of these methodologies have their own advantages and disadvantages. Encrypted messages are easily detectable. If someone is spying on communication channel for encrypted message, he/she can easily identify the encrypted messages. Encryption may draw unnecessary attention to the transferred messages. This may lead to cryptanalysis of the encrypted message if the spy tries to know the message. If the encryption technique is not strong enough, the message may be deciphered. In contrast, Steganography tries to hide the data from third party by smartly embedding the data to some other file which is not at all related to the message. Here care is to be taken to minimize the modification of the container file in the process of embedding data. But the disadvantage of steganography is that it is not as secure as cryptography. In the present method the authors have introduced three-step security. Firstly the secret message is encrypted using bit level columnar transposition method introduced by Nath et al and after that the encrypted message is embedded in some image file along with its size. Finally the modified image is encoded into a QR Code TM. The entire method has also been implemented for the Android mobile environment. This method may be used to transfer confidential message through Android mobile phone.

S. P. Rajamohana, K. Umamaheswari.  2017.  Hybrid Optimization Algorithm of Improved Binary Particle Swarm Optimization (iBPSO) And Cuckoo Search for Review Spam Detection.

With the development of the Internet, people are interested to share their views and opinions about the product on the web, forums, blogs etc. These online reviews are important for individual users and organization. Recently, it is a common tendency to the user to read the reviews or comments before purchasing some products or services. The online reviews are helpful for the business organizations in order to promote their product. However, in practice, these online reviews may be fake in order to promote or devalue the product. These fake reviews are called as opinion spam. Objective of the research paper is that, to select the best feature subset for detecting the fake review. To select a small subset of features out of the thousands of feature is important for accurate classification of review spam detection. Therefore, a good feature selection method is needed in order to speed up the processing rate, predictive accuracy. In this paper hybrid improved Binary Particle Swarm optimization (iBPSO) and cuckoo search (CS) is used for feature selection and Naive Bayes and k Nearest Neighbor classifier is used for classifying the review as spam and ham. Experimental results have shown that the proposed algorithm has yielded the best performance compared with the swarm intelligence techniques Binary Particle Swarm Optimization (BPSO) and Shuffled Frog Leaping (SFL).

S. Parimi, A. SaiKrishna, N. R. Kumar, N. R. Raajan.  2015.  "An imperceptible watermarking technique for copyright content using discrete cosine transformation". 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015]. :1-5.

This paper is nominated for an image protection scheme in the area of government sectors based on discrete cosine transformation with digital watermarking scheme. A cover image has broken down into 8 × 8 non overlapped blocks and transformed from spatial domain into frequency domain. Apply DCT version II of the DCT family to each sub block of the original image. Then embed the watermarking image into the sub blocks. Apply IDCT of version II to send the image through communication channel with watermarked image. To recover the watermarked image, apply DCT and watermarking formula to the sub blocks. The experimental results show that the proposed watermarking procedure gives high security and watermarked image retrieved successfully.

S. Patil, S. Ramayane, M. Jadhav, P. Pachorkar.  2015.  "Hiding User Privacy in Location Base Services through Mobile Collaboration". 2015 International Conference on Computational Intelligence and Communication Networks (CICN). :1105-1107.

User uses smartphones for web surfing and browsing data. Many smartphones are embedded with inbuilt location aware system called GPS [Global Positioning System]. Using GPS user have to register and share his all private information to the LBS server. LBS is nothing but Location Based Service. Simply user sends the query to the LBS server. Then what is happening the LBS server gives a private information regarding particular user location. There will be a possibility to misuse this information so using mobile crowd method hides user location from LBS server and avoid sharing of privacy information with server. Our solution does not required to change the LBS server architecture.

S. Petcy Carolin, M. Somasundaram.  2016.  Data loss protection and data security using agents for cloud environment - IEEE Conference Publication.

Cyber infrastructures are highly vulnerable to intrusions and other threats. The main challenges in cloud computing are failure of data centres and recovery of lost data and providing a data security system. This paper has proposed a Virtualization and Data Recovery to create a virtual environment and recover the lost data from data servers and agents for providing data security in a cloud environment. A Cloud Manager is used to manage the virtualization and to handle the fault. Erasure code algorithm is used to recover the data which initially separates the data into n parts and then encrypts and stores in data servers. The semi trusted third party and the malware changes made in data stored in data centres can be identified by Artificial Intelligent methods using agents. Java Agent Development Framework (JADE) is a tool to develop agents and facilitates the communication between agents and allows the computing services in the system. The framework designed and implemented in the programming language JAVA as gateway or firewall to recover the data loss.

S. Pund-Dange, C. G. Desai.  2015.  "Secured data communication system using RSA with mersenne primes and Steganography". 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom). :1306-1310.

To add multiple layers of security our present work proposes a method for integrating together cryptography and Steganography for secure communication using an image file. We have used here combination of cryptography and steganography that can hide a text in an image in such a way so as to prevent any possible suspicion of having a hidden text, after RSA cipher. It offers privacy and high security through the communication channel.

S. R. Islam, S. P. Maity, A. K. Ray.  2015.  "On compressed sensing image reconstruction using linear prediction in adaptive filtering". 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). :2317-2323.

Compressed sensing (CS) or compressive sampling deals with reconstruction of signals from limited observations/ measurements far below the Nyquist rate requirement. This is essential in many practical imaging system as sampling at Nyquist rate may not always be possible due to limited storage facility, slow sampling rate or the measurements are extremely expensive e.g. magnetic resonance imaging (MRI). Mathematically, CS addresses the problem for finding out the root of an unknown distribution comprises of unknown as well as known observations. Robbins-Monro (RM) stochastic approximation, a non-parametric approach, is explored here as a solution to CS reconstruction problem. A distance based linear prediction using the observed measurements is done to obtain the unobserved samples followed by random noise addition to act as residual (prediction error). A spatial domain adaptive Wiener filter is then used to diminish the noise and to reveal the new features from the degraded observations. Extensive simulation results highlight the relative performance gain over the existing work.

S. Saquib, R. Ali.  2015.  Malicious behavior in online social network. 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI). :1-6.

Nowadays, Online Social Networks (OSNs) are very popular and have become an integral part of our life. People are dependent on Online Social Networks for various purposes. The activities of most of the users are normal, but a few of the users exhibit unusual and suspicious behavior. We term this suspicious and unusual behavior as malicious behavior. Malicious behavior in Online Social Networks includes a wide range of unethical activities and actions performed by individuals or communities to manipulate thought process of OSN users to fulfill their vested interest. Such malicious behavior needs to be checked and its effects should be minimized. To minimize effects of such malicious activities, we require proper detection and containment strategy. Such strategy will protect millions of users across the OSNs from misinformation and security threats. In this paper, we discuss the different studies performed in the area of malicious behavior analysis and propose a framework for detection of malicious behavior in OSNs.

S. V. Trivedi, M. A. Hasamnis.  2015.  "Development of platform using NIOS II soft core processor for image encryption and decryption using AES algorithm". 2015 International Conference on Communications and Signal Processing (ICCSP). :1147-1151.

In our digital world internet is a widespread channel for transmission of information. Information that is transmitted can be in form of messages, images, audios and videos. Due to this escalating use of digital data exchange cryptography and network security has now become very important in modern digital communication network. Cryptography is a method of storing and transmitting data in a particular form so that only those for whom it is intended can read and process it. The term cryptography is most often associated with scrambling plaintext into ciphertext. This process is called as encryption. Today in industrial processes images are very frequently used, so it has become essential for us to protect the confidential image data from unauthorized access. In this paper Advanced Encryption Standard (AES) which is a symmetric algorithm is used for encryption and decryption of image. Performance of Advanced Encryption Standard algorithm is further enhanced by adding a key stream generator W7. NIOS II soft core processor is used for implementation of encryption and decryption algorithm. A system is designed with the help of SOPC (System on programmable chip) builder tool which is available in QUARTUS II (Version 10.1) environment using NIOS II soft core processor. Developed single core system is implemented using Altera DE2 FPGA board (Cyclone II EP2C35F672). Using MATLAB the image is read and then by using DWT (Discrete Wavelet Transform) the image is compressed. The image obtained after compression is now given as input to proposed AES encryption algorithm. The output of encryption algorithm is given as input to decryption algorithm in order to get back the original image. The implementation of which is done on the developed single core platform using NIOS II processor. Finally the output is analyzed in MATLAB by plotting histogram of original and encrypted image.

S. Zafar, M. B. Tiwana.  2015.  "Discarded hard disks ??? A treasure trove for cybercriminals: A case study of recovered sensitive data from a discarded hard disk" 2015 First International Conference on Anti-Cybercrime (ICACC). :1-6.

The modern malware poses serious security threats because of its evolved capability of using staged and persistent attack while remaining undetected over a long period of time to perform a number of malicious activities. The challenge for malicious actors is to gain initial control of the victim's machine by bypassing all the security controls. The most favored bait often used by attackers is to deceive users through a trusting or interesting email containing a malicious attachment or a malicious link. To make the email credible and interesting the cybercriminals often perform reconnaissance activities to find background information on the potential target. To this end, the value of information found on the discarded or stolen storage devices is often underestimated or ignored. In this paper, we present the partial results of analysis of one such hard disk that was purchased from the open market. The data found on the disk contained highly sensitive personal and organizational data. The results from the case study will be useful in not only understanding the involved risk but also creating awareness of related threats.