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Over the last four years as the Syrian uprising has grown into a full-blown civil war, a sinister parallel conflict has been fought out in cyberspace, with combatants wielding bytes and software rather than guns as they have battled for supremacy on Syria’s internet frontline.

But the consequences of this secret cyber war have been real and deadly – particularly for opponents of the Assad regime who have been targeted for arrest and torture as a consequence of personal information gleaned from their email traffic.

In some cases even the military plans of crucial rebel offensives had been hacked. But the opposition has been busy too, leaking President Bashar al-Assad’s embarrassing personal correspondence and eavesdropping on government troop deployments amid much else.

As a consequence Syria’s civil war has become fertile ground for ‘hacktivists’ from both sides – egged on and in some cases assisted by governments and agencies from outside the region.

In this special investigation for People & Power , Juliana Ruhfus has been finding out why some experts believe Syria’s electronic armies have been drawing up the blueprints for all wars of the future, conflicts that transcend traditional physical boundaries but which can be just as significant as those fought with tanks and missiles.

Jonathan Russell, Head of Policy at Quilliam, talks to Sky News #digitalview about how everyone, not just governments, can help challenge extremist propaganda.

#digitalview, Sky News (23/01/16)

Quilliam is the world’s first counter-extremism think tank set up to address the unique challenges of citizenship, identity, and belonging in a globalised world. Quilliam stands for religious freedom, equality, human rights and democracy.

A strategy to counter violent radicalisation on the Internet must be user driven, empowering Internet users with “cultural intelligence”. Cultural intelligence is essentially shorthand for an understanding of the key pillars of, and vulnerabilities inherent in, militant Islamist rhetoric. What this report calls “Enabling Stakeholders”, such as schools, and religious and community organisations, can disseminate this knowledge, thereby empowering Internet users to choose whether and how to challenge the call to violence.

VOX-Pol Blog post.

This study investigated the phenomena of group polarisation with particular attention to the differences between offline and online settings. Polarisation is a process that leads people to develop extreme ideologies. Three hundred and seven participants were recruited and randomly assigned to different experimental conditions, i.e. antisocial and prosocial polarisation, within groups of 6 people composed of four confederates, participating in discussions about a social dilemma under two different circumstances: face to face and online. The degree of polarisation was assessed considering the final decisions adopted by the participants, as well as the internal dynamics characterising their final attitudes, i.e. compliance versus conversion. Results showed that online groups appeared more susceptible to polarisation and their members reported a greater degree of conformism. In particular, within online environments, the risk of being polarised, both antisocially and prosocially, increased by around 12%. Furthermore, in an online setting, a greater degree of conversion emerged only when the members decided to adopt a pro-social behaviour, while a greater degree of compliance emerged whenever they decided to adopt antisocial behaviour.

Security informatics and intelligence computation plays a vital role in detecting and classifying terrorism contents in the web. Accurate web content classification using the computational intelligence and security informatics will increase the opportunities of the early detection of the potential terrorist activities. In this paper, we propose a modified frequency-based term weighting scheme for accurate Dark Web content classification. The proposed term weighting scheme is compared to the common techniques used in text classification such as Term Frequency (TF), Term Frequency-Inverse Document Frequency (TF-IFD), and Term Frequency- Relative Frequency (tf.rf), on a dataset selected from Dark Web Portal Forum. The experimental results show that the classification accuracy and other evaluation measures based on the proposed scheme outperforms other term weighting techniques based classification.