Sajad Homayoun

• Postdoc Researcher in Computer Security
• PostDoc Position at Technical University of Denmark

Noise (un-important) alerts are generally considered a major challenge in intrusion detection systems/sensors because they require more analysts to review and may cause disruption to systems that are shut down to avoid the consequences of a compromise. However, in real-world situations, many alerts could be raised for automatic tasks being complete...

A printed circuit board (PCB) surface can fail by corrosion due to various environmental factors. This paper focuses on machine learning (ML) techniques to build predictive models to forecast PCB surface failure due to electrochemical migration (ECM) and leakage current (LC) levels under corrosive conditions containing the combination of six critic...

Security intelligence is widely used to solve cyber security issues in computer and network systems, such as incident prevention, detection, and response, by applying machine learning (ML) and other data-driven methods. To this end, there is a large body of prior research works aiming to solve security issues in specific scenarios, using specific t...

Malware remains a threat to our cyberspace and increasingly digitalized society. Current malware hunting techniques employ a variety of features, such as OpCodes, ByteCodes, and API calls, to distinguish malware from goodware. However, existing malware hunting approaches generally focus on a single particular view, such as using dynamic information...

Blockchains are turning into decentralized computing platforms and are getting worldwide recognition for their unique advantages. There is an emerging trend beyond payments that blockchains could enable a new breed of decentralized applications, and serve as the foundation for Internet's security infrastructure. The immutable nature of the blockcha...

The dramatic growth in smartphone malware shows that malicious program developers are shifting from traditional PC systems to smartphone devices. Therefore, security researchers are also moving towards proposing novel antimalware methods to provide adequate protection. This paper proposes a Blockchain-Based Malware Detection Framework (B2MDF) for d...

The dramatic growth in smartphone malware shows that malicious program developers are shifting from traditional PC systems to smartphone devices. Therefore, security researchers are also moving towards proposing novel antimalware methods to provide adequate protection. This paper proposes a Blockchain-Based Malware Detection Framework (B2MDF) for d...

The increasing popularity of Internet of Things (IoT) devices makes them an attractive target for malware authors. In this paper, we use sequential pattern mining technique to detect most frequent opcode sequences of malicious IoT applications. Detected maximal frequent patterns (MFP) of opcode sequences can be used to differentiate malicious from...

Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims' computers and requests a ransom payment to reinstantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately sy...

Ransomware, a malware designed to encrypt data for ransom payments, is a potential threat to fog layer nodes as such nodes typically contain considerably amount of sensitive data. The capability to efficiently hunt abnormalities relating to ransomware activities is crucial in the timely detection of ransomware. In this paper, we present our Deep Ra...

Software Defined Networking (SDN) is an increasingly common implementation for virtualization of networking functionalities. Although security of SDNs has been investigated thoroughly in the literature, forensic acquisition and analysis of data remnants for the purposes of constructing digital evidences for threat intelligence did not have much res...

While botnets have been extensively studied, bot malware is constantly advancing and seeking to exploit new attack vectors and circumvent existing measures. Existing intrusion detection systems are unlikely to be effective countering advanced techniques deployed in recent botnets. This chapter proposes a deep learning-based botnet traffic analyser...

Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims' computers and requests a ransom payment to reinstantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately sy...

Stream data is usually in vast volume, changing dynamically, possibly infinite, and containing multi-dimensional features. The attention towards data stream mining is increasing as regards to its presence in wide range of real-world applications, such as e-commerce, banking, sensor data and telecommunication records. Similar to data mining, data st...

Intrusion detection system (IDS) is becoming a vital component to secure the network. A successful intrusion detection system requires high accuracy and detection rate. In this paper a hybrid approach for intrusion detection system based on data mining techniques is proposed. The principal ingredients of the approach are weighted k-means clustering...